What is customer analytics, and why does it matter today?

At its core, customer analytics is the process of turning raw signals from your customers into business decisions. Put simply, analytics is the process of using data to generate a meaningful view of the customer so that you can make better choices about products, service, and communication. Modern customer analytics spans digital channels, support, sales, and even offline interactions – giving a much richer picture than a simple monthly report.

In practice, analytics involves collecting, cleaning, and analysing customer interactions, then feeding the results back into operations. When done well, customer analytics helps you turn scattered events into structured knowledge: which customers are at risk, what drives customer satisfaction, when to act to protect customer retention, and which offers perform best. This is what makes effective customer analytics a strategic capability, not a “nice to have” dashboard.

Imagine a retail bank reviewing its quarterly dashboard.Churn is up by 2%. Conversion is flat. NPS dropped slightly. On paper, nothing looks dramatic – but something feels off. Only when the team digs into customer analytics beyond surface-level metrics do they notice a pattern: customers who recently contacted the call center about mobile app issues are quietly moving their savings elsewhere. No formal complaints. No angry emails. Just silence – and then churn. This is where customer analytics stops being a reporting exercise and starts becoming an early warning system.

The benefits of customer analytics have grown as customers moved online. Switching banks, telcos or retailers is now a few clicks away; your customer base is more fluid than ever. Companies that rely purely on gut feeling struggle to keep up with evolving customer behaviours and preferences. In contrast, data-driven organizations use customer analytics to understand why people stay, why they leave, and how to improve customer outcomes across the entire customer journey.

What types of customer data should you collect?

To do serious customer analytics, you first need the right raw material. Customer data is an essential asset – but only if it actually reflects how people use your services. The most valuable types of customer data typically include:

• Profile & demographic data – age, location, segment, account type.
• Transactional data – purchases, payments, product usage.
• Behavioural data – clicks, sessions, app flows, customer behaviour data in digital channels.
• Unstructured signals – emails, chat logs, call transcripts, customer feedback surveys, social comments.

These categories form the foundation for customer data analytics. Classic analytics tools focused mostly on web traffic, but modern setups enrich that with deeper examples of customer data – such as tone of voice in a support call or topics recurring in complaints. This kind of customer data from various sources allows you to better understand customer expectations instead of guessing.

The process of collecting this data has to be deliberate. Randomly trying to collect data “just in case” quickly becomes a burden. Instead, you should collect data that maps to clear questions: how people use your app, when they drop off, which features correlate with loyalty. Over time, companies can use customer data to better match offers to customer needs and identify new opportunities in their products and services.

Collecting customer data is one thing – making sense of it is another

If you want to understand how to move from raw feedback to actionable customer insights, don’t miss the latest episode of Prompt & Response webcast. It’s a practical deep dive into customer analytics that actually delivers.

 
Watch now

Types of customer analytics: from descriptive to prescriptive

Not all analytics is the same. Understanding the main types of customer analytics helps you choose the right methods for each problem. In a typical maturity model you’ll find four layers:

#1 Descriptive analytics answers “what happened?”. It aggregates and summarizes historical customer data: revenue by segment, tickets by topic, NPS by region. This is where tools like Google Analytics started, and where most organizations still spend the majority of their time.

#2 Diagnostic analytics asks “why did this happen?”. It explores relationships in the data – for example, which changes in onboarding flow led to lower customer satisfaction, or which touchpoints in the customer journey correlate with complaints. This helps you understand customer reactions instead of just observing metrics.

#3 Predictive analytics estimates “what is likely to happen next?”. Predictive customer analytics can forecast customer churn, customer lifetime value or conversion probabilities in specific customer segments.

#4 Prescriptive analytics suggests “what should we do?”. It connects patterns to actions: who to contact, with what offers, through which channels.

Together, these types of customer analytics provide visibility and guidance across the entire customer journey.

How do you actually analyze customer data?

The mechanics of customer data analysis vary by industry, but the principles are similar. First, you collect and analyze customer data that is directly tied to a business question. Then you enrich it with additional context (segments, channels, lifecycle stage) and start analyzing customer patterns.

Modern teams analyze customer data across structured and unstructured sources. On the structured side, they track funnels, flows and key events. On the unstructured side, they use language models to analyze customer reviews, complaints and chats. This analyzing customer interactions produces qualitative insights that numbers alone can’t show – for example, specific phrases that signal frustration or delight.

In our Prompt & Response webcast, we discussed how GenAI accelerates the process of collecting and analyzing customer feedback at scale. Instead of manually reading thousands of comments, AI can summarize themes, detect customer sentiment shifts, and highlight risks or opportunities. That’s where analytics provides not just charts, but concrete next steps.

The key benefits of customer analytics for your business

When done well, customer analytics delivers value far beyond reporting. It directly supports growth, profitability and resilience. Here are some of the most important outcomes that customer analytics helps businesses achieve:

Stronger retention and loyalty.

With the right models, you can detect early signs of customer churn, anticipate issues and act before people leave. This protects customer lifetime value and builds customer loyalty by showing that you react to problems quickly. Over time, this raises overall customer experience instead of treating churn as an unavoidable cost.

More efficient acquisition and marketing efforts.

By understanding which behaviours lead to purchase, you can design smarter marketing campaigns and align product analytics with the channels that drive results. Customer analytics helps organizations focus their marketing efforts on segments with real intent, instead of broadcasting to everyone. This also supports targeted customer acquisition strategies.

Better decisions at every level.

From the customer service team prioritizing tickets to executives planning roadmap investments, customer analytics to understand patterns becomes a shared language. It helps decision makers better understand customer behaviour, improve customer outcomes and align around facts.

How customer analytics improves customer experience and the customer journey

One of the most tangible payoffs of customer analytics is its impact on customer experience. By connecting signals across channels, you can create a customer journey that reflects how people actually behave, not how you imagine they behave in slide decks. You can literally create a customer journey map based on real data instead of assumptions.

For instance, by tracking customer interaction paths, support tickets and customer feedback, companies discover friction points they didn’t know existed. They might find that a specific payment step generates confusion, or that a new login flow harms customer engagement. When you design improvements, you can then measure whether they genuinely improve customer experience across the entire customer journey.

Analytics can also help operational teams. A customer service team can use customer analytics to understand which topics are rising, while customer relationship management professionals can use customer data to better personalize outreach. When you then monitor customer sentiment in reviews, chat and social channels, you get a continuous feedback loop that lets you enhance customer trust and experience over time.

Customer analytics tools, data platforms and tech stack

To deliver all this at scale, you need more than spreadsheets. A modern stack combines customer analytics tools and a robust analytics platform or customer data platform (CDP). Classic analytics tool choices such as tools like Google Analytics remain useful for web metrics, but they don’t cover the full breadth of customer data you need today.

A more complete architecture usually includes:

• Data pipelines that collect data from apps, websites, CRM and support.
• An analytics platform or CDP to unify profiles and events.
• ML and AI components for predictive analytics and prescriptive analytics.
• Dashboards and exploratory tools for teams to use customer analytics in daily decisions.

When evaluating customer analytics tools, focus less on buzzwords and more on whether teams can realistically use customer data every day. In the webcast, we stressed that even the best engine fails if people still export everything to Excel. The right stack lets you plug insights directly into marketing campaigns, product experiments and service workflows.

Collecting and storing customer data securely

All of this only works if you store customer data and use it responsibly. Security, privacy and governance are not side topics; they are central to implementing customer analytics at scale. Customers share information on the assumption that you will both protect it and use it to deliver better services.

That’s why collecting and storing customer data must follow clear policies and legal frameworks. You need to store customer information with encryption, access control and transparent consent. You also need to think carefully about which customer data collection practices are truly necessary. It’s often better to collect customer signals that you know how to use than to hoard data you never analyze.

Done correctly, you both store customer data securely and make it available for analytics. The goal is to collect customer information that helps you enhance customer experiences without compromising trust. Many organizations partner with specialists to ensure their customer data securely supports innovation instead of limiting it.

Customer analytics in action: where insights turn into decisions

Customer analytics proves its value not in dashboards, but at the moment a team changes a decision it would otherwise make on instinct.
Several of the situations described below were discussed during the Prompt & Response webcast on voice of the customer, while others build on the same patterns observed across real projects.

Together, they show how customer analytics turns insight into action.

Pharma: improving patient experience in a regulated process

One pharmaceutical company analyzed unstructured customer data from patient forums, social media, and healthcare professional feedback while redesigning a reimbursement application. The initial goal was straightforward: digitize an existing, paper-based process.

Traditional analytics suggested acceptable completion rates. Customer sentiment told a different story.

By listening to real customer feedback, the team identified two critical gaps: a lack of educational content explaining the disease and unclear communication between patients and healthcare professionals.

These insights didn’t come from standard dashboards. They emerged only after unstructured data was included in customer analytics. By addressing these gaps, the company improved the perceived usability of the process and made the solution easier to adopt — without changing regulatory requirements.

This example, discussed during the Prompt & Response webcast, shows how customer analytics can help product teams refine flows and content even in highly regulated environments.

Banking: predicting churn before customers leave

In banking, customer analytics is less about reacting to problems and more about anticipating them.

Banks combine transactional customer data with behavioral signals from mobile and web channels to build predictive models that identify early signs of customer churn. These signals are often subtle: reduced logins, abandoned flows, or changes in transaction patterns — long before a customer formally closes an account.

By applying predictive analytics, banks can score customer accounts based on risk and propensity, allowing teams to prioritize outreach. Instead of running generic campaigns, they focus on the customer segments that need attention most, improving customer retention and protecting customer lifetime value.

As highlighted in the Prompt & Response webcast, this approach shifts customer experience from reactive problem-solving to prevention.

E-commerce: when growth hides friction in the customer journey

The team was running a fast-growing online store. Sales were increasing and marketing performance looked healthy. On the surface, there was no reason to worry.
Then customer churn started creeping up – quietly, without a clear trigger.

Instead of reacting with discounts or assumptions, the team examined customer interaction paths across checkout, login, and post-purchase flows. Behavioural data was reviewed alongside support tickets and open-text feedback.

Two issues emerged.

A single payment step caused hesitation and repeated retries. At the same time, a redesigned login flow — introduced to improve security — interrupted engagement.

Neither problem looked critical in isolation. Together, they explained the churn pattern.

With a clearer view of the entire customer journey, both flows were simplified and measured again. Checkout completion improved. Engagement stabilized. Churn slowed.

Customer analytics didn’t just explain what was happening — it showed precisely where experience design needed to change.

Customer support: when feedback signals a problem — but not the one you expect

Monday morning, the customer service inbox was full. Chat requests kept coming in. Social mentions were rising.
At first glance, it looked like a typical post-release spike.

A closer look at customer interactions revealed a different picture.

By analyzing tickets, chat logs, and reviews together, the team noticed a surge in questions related to a new feature. There were no bug reports — only uncertainty.

Sentiment analysis confirmed the pattern. Customers weren’t frustrated with the functionality itself, but with how it was explained. Without combining structured metrics and unstructured feedback, the issue would likely have been dismissed as noise.

The response was operational rather than technical. In-app guidance was updated. Onboarding messages were refined. Short, contextual tips were proactively shared.

Within days, ticket volume dropped and sentiment stabilized. Feature adoption improved — without increasing pressure on the support team.

From industry use cases to broader insight

Although these examples come from different industries, the underlying pattern is the same. Customer analytics works best when it combines multiple data sources — including unstructured feedback — and when insights are connected directly to operational decisions.

Rather than relying on one-size-fits-all messaging, organizations use customer data to personalize flows, target the right people at the right moment, and adapt faster as customer expectations change.

Key challenges of customer analytics

Despite the clear benefits, the challenges of customer analytics are real. Common blockers include fragmented data, legacy systems, unclear ownership and the habit of treating analytics as an afterthought. Many teams feel that customer analytics is “something BI does” instead of a shared capability across the organization.

Another frequent issue is misalignment between ambition and practice. Leaders talk about AI and predictive analytics, but day-to-day workflows still run on manual exports and gut feeling. In the webcast, we described cases where customer analytics models were technically excellent but operationally useless because decisions happened weeks after the data was produced.

To overcome these challenges, organizations need to connect data, tools and people. That means defining ownership, aligning on a roadmap, and making sure that every insight has a clear “so what?”. When customer analytics helps organizations act within hours instead of months, you start seeing compounding gains in performance and overall customer outcomes.

Getting started with customer analytics

Many teams feel overwhelmed thinking about how to get started with customer analytics. You don’t need a perfect, enterprise-wide setup from day one. A more pragmatic path is to pick one or two concrete use cases where analytics can also help – for example, reducing complaints in one channel, or improving a specific onboarding flow.

From there, you use customer data already available, fill in a few gaps, and run experiments. This is the essence of implementing customer analytics in an agile way: start small, prove value, then scale. Over time, you can expand from one area of the entire customer journey to others, connecting dots and reusing building blocks such as a unified customer data platform.

As your capabilities mature, you’ll move from simple reports to more advanced techniques such as customer segmentation, predictive analytics and prescriptive analytics. The point is not to chase buzzwords, but to build an engine where customer analytics continuously informs product, service and communication decisions.

The future of customer analytics: AI, GenAI and real-time decisions

Looking ahead, the future of customer analytics is clearly shaped by AI and GenAI. Language models make it much easier to process unstructured customer data, generate summaries, and surface insights into customer intent and frustration. They also democratize analytics, letting non-technical teams ask questions, explore customer behaviour, and design experiments faster.

This opens the door for more real-time, predictive analytics scenarios. Instead of monthly reporting, organizations can run predictive customer analytics and active monitoring on a daily basis, adjust flows within hours, and build playbooks around typical patterns. AI-assisted customer analytics turns your data into a living, continuously updated map of insights into customer behaviour across channels.

Ultimately, the winners will be organizations that combine strong governance, secure customer data practices, and a clear focus on using analytics to improve customer outcomes. When you pair that with modern tooling and a culture that values evidence over opinions, customer analytics helps businesses make decisions that are better for both the company and the people it serves.

Summary: what to remember about customer analytics

To wrap up, here are the key points that matter when you think about customer analytics for your organization:

• Customer analytics is the process of turning raw events into decisions across your customer base.
• Customer data is an essential asset – but only if you use it to better understand customer needs and identify the changes that matter.
• The right combination of descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics gives you a full picture across the entire customer journey.
• Secure customer data collection, governance and the ability to store customer data safely are non-negotiable foundations.
• With AI and GenAI, you can collect and analyze customer data from unstructured feedback, monitor customer sentiment, and act faster on what you learn.

In short, when you bring together the right customer data, tools and mindset, customer analytics helps organizations move from guessing to knowing – and from reacting to shaping the future of their overall customer experience.

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Why is Application Modernization Essential for Legacy Systems? 

When a system is referred to as a legacy system, it can mean many different things: 

• it no longer meets architectural standards, 
• it uses outdated technologies (old programming languages, tools or platforms),  
• it has no support for certain plug-ins, 
• maintenance and fixing errors require a lot of effort, 
• its development is blocked because of the legacy code, 
• a lack of up-to-date documentation, 
• compatibility issues between older systems and newer technologies. 

Modernizing such systems is challenging, as they are often key enterprise applications. Reviving them is perceived as problematic because of their complexity. As the saying goes: “if it ain’t broke, don’t fix it!”  

This would be true, but not in an era where “just working” would be enough. Modern systems are scalable, more load-bearing, and simply faster. You can’t expect to outdo the competition with a system that does only what it’s supposed to do. 

The Importance of modernization in legacy systems 

The purpose of the modernization journey undertaken by so many companies is not to “get rid” of a legacy system – often it’s quite the opposite. The “why” behind the scenes is to maintain application functionality and improve its performance with newer technologies – either by migrating to the cloud platform, code refactoring, or changing its architecture.  

Which technologies are considered legacy ones?  

In the case of legacy code, the issue is quite clear: it’s about obsolete or unnecessary code that doesn’t fit in with new technological developments. All right, but what do we mean when we say “legacy technologies”? When does technology become a legacy? Does “legacy” refer to the technology stack itself, or do we need to understand it more broadly?  

In the world of technology, legacy technology may refer to different areas: 

• Older programming languages such as Cobol (Common Business Oriented Language), used in business and finance applications, or Visual Basic, especially popular in the 1990s and 2000s, succeeded by the .NET framework, or Perl.  
• Architecture styles, including SOA (Service-Oriented Architecture), Client–Server Architecture, and monolithic architectures. 
• Software development approaches can also be considered legacy ones. For many, the traditional waterfall approach is an outdated approach, contrary to Agile iterative frameworks like Scrum, Kanban, or DevOps, which bring greater flexibility and a sense of fresh air to the projects.  
  

How to identify legacy software in your company 

To effectively recognize legacy technologies in your company, a number of key questions must first be analyzed. At the outset, in order to start the discussion on categorizing legacy systems, they must first be identified. 
 
Key questions should be asked, such as: 

• Is the software based on outdated technology? 
• Is it incompatible with current systems? 
• Is the source code or documentation no longer available for this system? 

If we have correctly identified that at least one of your applications is considered ‘legacy’, we recommend establishing additional facts: 

• Do they pose a risk to your organization? 
• How much does this application cost you annually in maintenance fees? 
• How many changes are being implemented and how much does it cost per year? 

The steps indicated will allow you to better understand which systems require attention and management action. The effective identification of legacy technology will enable you to make more informed decisions relating to IT infrastructure maintenance and long-term business strategies. 

Benefits of Application Modernization for Legacy Applications 

It’s highly likely that software developed for over 5 years won’t meet your growing needs and recurring issues will impact its performance. Here are the main advantages of modernizing your app:  

• Cutting costs – by eliminating technical debt. Legacy systems tend to be very expensive to maintain. Much of this cost comes from managing legacy code and the growing number of support tickets. Diagnosing problems takes hours or even days, and few team members know how to work with an older system. 
• Improved performance and speed – poor performance is an important reason to implement more modern solutions. Through the modernization process (including application development) you can significantly increase the speed and resilience of your systems. 
• Enhanced security – security vulnerabilities are not just a roadblock to modernization. Only after modernization, with new protocols and authentication methods, will you meet security standards for systems and applications.  
• Business Flexibility – legacy systems limit a company’s ability to create new features and services. Moving legacy software to the cloud by rolling out microservices and containerization also gives your organization the ability to add new features and functionalities. 

Key Technologies for Application Modernization 

Two aspects in particular are worth noting. Firstly, containers have become a key technology in application modernization due to their capability to package and isolate applications with their dependencies. 

The second such aspect is hybrid cloud solutions. Legacy systems can often be complex and difficult to upgrade, especially if tied to on-premises infrastructure. A hybrid cloud offers a way to modernize legacy systems by integrating them with cloud services while still maintaining some components on-premises.  

Understanding the Role of Containers in Modernization 

Application modernization often tends toward having secure and highly available applications. Containerization is an excellent response to the need to build scalable, efficient, and fail-safe systems that will run on multiple platforms, and, of course, in the cloud. Containers that bring together individual microservices positively impact application reliability. 

The advantages include speed, better integration, and improved application lifecycle management. In an era of resource optimization, companies are also looking to modernize infrastructure by reducing resources. Here, too, containers will do well, as they are more efficient than virtual machines, which in turn require an evolved operating system and need to run their own resources.  

Exploring Hybrid Cloud Solutions for Legacy System Upgrades 

Cloud migration is not the only way to modernize legacy apps. Many companies opt for a hybrid cloud, which means benefiting from infrastructure that is a blend of private and public cloud services. 

Such a solution is flexible and extremely popular. As many as 70% of companies are utilizing it today, according to Flexera’s State of the Cloud Report. Employing hybrid cloud solutions for legacy system upgrades involves the deployment of existing applications from on-premises environments to public clouds such as Microsoft Azure, Google Cloud, or AWS Cloud. This type of application modernization process is vitally important for all businesses looking to enhance productivity and meet their changing business needs.  

Automation in Legacy Application Modernization 

One key aspect of the application modernization process is automation, which significantly expedites the transition from a legacy application to a cutting-edge business application. In a fast-paced IT environment, companies aim for more releases and better software quality, and the ideal way to do this is to implement DevOps automation tools.  

Such solutions also include predictive analytics capabilities, so the system is monitored, optimized, and maintained in real time. By automating testing, code refactoring, and integration, organizations not only deploy software faster but also gain business value quicker when compared to more traditional methods.  

Best Practices for Successful Application Modernization 

An application modernization strategy is essential for organizations looking to stay competitive but also not wanting to waste their budget on maintaining legacy applications. With legacy modernization becoming the way to go, companies must develop a comprehensive approach to updating their legacy systems.  

Implementing Cloud-Native Strategies for App Modernization 

As cloud solutions have gained popularity, the role of cloud-specific functionalities allowing you to use their full potential has increased. Such cloud-native solutions include microservices, containerization, API integrators, or serverless solutions – it is difficult to imagine any modern application without them.  

• Also read: Cloud-native applications: all you need to know 

Accelerating Application Delivery through Modernization Efforts 

According to research, the average application modernization process takes 16 months. We cannot deny it: this is a lengthy and laborious undertaking. Fortunately, application modernization services can be accelerated without compromising the quality of solutions. And here again, let’s turn back to the DevOps model, which in the world of state-of-the-art application development is no longer an innovation but the standard. 

The DevOps model works well, especially in projects in which a company decides to modernize a system and then hand it over for maintenance to external experts (e.g., in a nearshoring model).  

Application Modernization Patterns and Strategies 

If you are planning to modernize your monolithic application, migrate to a cloud environment, or simply modernize the software, familiarize yourself with the most effective strategies, also known as the 7 Rs:  

1. Rehosting  
   This approach is also known as “lift and shift” and involves moving the existing infrastructure to a cloud platform as it is. 
    
   Advantages: 
   Such a digital transformation strategy is popular – it comes at a low cost and effort. You don’t need to invest in cloud-native solutions or change the application code, and can still enjoy the same functionalities in a relatively short time. 
    
   Disadvantages:  
   You face the limitations of not using native solutions (such as containers and serverless computing). Also, you are moving the entire application along with its issues and performance limitations.  

2. Refactoring 
   If you want to use a Platform-as-a-Service solution, application refactoring will be an option for you. After application refactoring, you will gain access to cloud-first solutions. 
    
   Advantages:  
   After refactoring, you will benefit from better scalability. You will gain permanent settlements – billing based on resource usage. You can take advantage of the many opportunities provided by cloud-native solutions.  
   
   Disadvantages:  
   Due to code changes, refactoring will require the allocation of more resources and time than in the case of “ordinary rehosting”.

3. Rearchitecting  
   If a legacy application was designed in the old architecture style, you will need to make changes to the application architecture as part of the modernization. Bear in mind that this is a large-scale modernization project.  
    
   Advantages: 
   With modern architecture, you can gain entirely new application performance capabilities and scalability, plus enhanced security. For example, using small components, that is, microservices, instead of one monolithic application allows you to make your application more fail–safe and resilient. It’s also an opportunity to leverage DevOps practices and application modernization tools. 
    
   Disadvantages:  
   Working on the original application architecture requires an appropriate strategy, so any undertaking must be preceded by a solid business analysis. If you are pressured by time, you will have to wait patiently for the results.  

4. Rebuilding 
   When your business needs change significantly, or when the technologies used differ significantly from the latest trends, you may need to rebuild the application, either entirely or partially.  
    
   Advantages:  
   New technologies will take the constraints of dealing with legacy code off developers’ shoulders, allowing for faster application development. You can add new functionalities and rethink existing ones. Furthermore, new technologies attract programmers who want to work with the latest possible stack.  
    
   Disadvantages:  
   Application rebuilding will involve additional work (e.g., database migration) and a greater potential risk that not all functionalities will work as expected. It is the most time-consuming and costly option.   
    
   You may encounter difficulties in finding experts with the right seniority for the application rebuilding project. In such a situation, it is worth considering external support.  

5. Replatforming 
   This strategy is also known as “lift and reshape”. Moving the application to the cloud, while taking optimization work into account – with this approach you can take advantage of cloud-native capabilities relatively quickly.  
   
   Advantages:  
   No changes need to be made to the application code, and during the migration process, the application is available all the time. This saves time and money while keeping the application running.  
   
   Disadvantages:  
   The process should be well -thought -out to avoid time-consuming additional work (and thus expanding the scope of the project). 

6. Replacing/retiring 
   None of the above app modernization strategies may turn out to be suitable for your project.  
    
   The decision to replace a legacy application, even a business-critical one, is not easy to make, especially if you have invested a great deal of time and money in developing your custom business solution. 
    
   Advantages:   
   There are many Software-as-a-Service solutions available today that can have similar functionality to your application and will not generate maintenance costs. It’s a strategy that is easy and quick to implement and comes with the predictable fees, upgrades, and security of the solution provided by the vendor.   
    
   Disadvantages:  
   When opting for a SaaS solution or any other commercially available application, you have limited influence over its design or development. You also have to consider the legal aspects of data processing and available options for integrating the application with other systems. 

7. Retaining 
   If, for security reasons or existing dependencies, upgrade work cannot be planned, companies opt to “leave it as is.” This does not mean, however, that no works are planned. They are simply postponed until a more favorable time – when the company has met security or compliance standards.  
    
   Advantages:   
   Such a strategy provides an opportunity to evaluate the process and properly prepare for modernization, and then upgrade an application when modernization is in a position to bring business value. 
    
   Disadvantages:  
   You still have to deal with the problems of the legacy application for some time and bear the costs of its maintenance. The longer an upgrade is postponed, the longer a company fails to benefit from the potential of cloud-native solutions. 

Developing a Comprehensive Application Modernization Roadmap 

Each approach to application modernization will affect areas such as the budget, application availability, resource commitment, scope of work, or risk. Choosing the right strategy will help you avoid unnecessary effort and cost. There are tools available to estimate your needs, such as Gartner guides, which will walk you through the world of modernization. 

You can also take advantage of Application Managed Services that usually offer flexible development and maintenance models.  

The process of evaluating the modernization of applications 

To evaluate the modernization process of applications at Inetum Poland, we follow a structured approach considering various factors such as complexity, risk, impact, workload, architecture, cost, operations, and security. Below is a description of how we go through the evaluation process: 

• Confirm the feasibility of modernization and define the problem. Before proceeding with any modernization effort, we ensure that the possibility of modernizing a legacy application is confirmed, and the problem is clearly defined by the customer. 
• Select the modernization approach. We apply seven approaches known as “the 7 Rs”, ranked from lowest to highest in terms of complexity and risk.  
• Consider all factors. We carefully consider factors such as workload, architecture, cost, risk, operations, and security when choosing the modernization approach. The priority given to each factor depends on its importance to our business objectives. 
• Choose the right resources and skills at Inetum Group. In acknowledging that small in-house teams may lack the skills needed to upgrade legacy systems, we understand the importance of adequate resources and the highly skilled experts we have throughout the Inetum group. That’s why having a large team of skilled experts in various technologies helps us to achieve optimal results in the short, medium and long term. 
• Optimize and analyse. We understand that the modernization process is iterative and requires constant optimization. Therefore, we continuously test and optimize application modifications to ensure smooth hosting, software licensing, and connectivity during the transition phase. 

By following this structured evaluation process, we ensure that our modernization efforts are aligned with our business objectives, minimize risks, and maximize the positive impact on our organization. 

Modernizing legacy applications – summary 

Application modernization includes incorporating new features and taking advantage of cloud computing technologies. This modernization can involve transforming a legacy application into a cloud-native application that leverages cloud infrastructure and a managed services model. 

Cloud application modernization is an essential aspect of any application modernization project, sometimes called legacy modernization. This approach to application modernization entails taking an existing application from the existing application portfolio and migrating it to the cloud to reap the numerous benefits of cloud technology. A modernized application is a better experience both for you and the programmers maintaining the project. 

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What is Data-Driven Decision Making?

In essence, data-driven decision-making empowers organizations to leverage the wealth of information at their disposal to identify trends, anticipate market shifts, understand customer behavior, and optimize internal operations. By harnessing the right data effectively, businesses can unlock valuable insights that drive innovation, enhance efficiency, and ultimately, propel growth.

In this article, we will delve into the principles, benefits, and best practices of data-driven decision-making, exploring how the process of using data revolutionizes the way businesses operate and navigate the complexities of the modern business and marketplace.

What are the benefits of Data-Driven Decision Making?

Implementing data-driven decision-making offers a multitude of benefits for businesses across various industries:

• Enhanced insights and accuracy. By analyzing data, organizations gain deeper insights into market trends, customer preferences, and operational performance. This enables more accurate forecasting, better understanding of customer needs, and make data-driven decisions.
• Improved efficiency and productivity. Data-driven processes streamline operations by identifying inefficiencies, bottlenecks, and areas for improvement. By optimizing workflows and resource allocation based on data insights, businesses can enhance efficiency and productivity.
• Better risk management. Data-driven decision-making enables organizations to assess risks more effectively by identifying potential threats and vulnerabilities. This proactive approach allows businesses to mitigate risks before they escalate, thereby safeguarding their reputation and financial stability.
• Personalized customer experiences. By leveraging data and analytics, businesses can create personalized purchase experiences tailored to individual customer data including personal preferences and behaviors. This not only enhances customer satisfaction but also fosters loyalty and increases retention rates, illustrating how data-driven decision-making is important.
• Competitive advantage. Data-driven decision-making provides businesses with a competitive edge by enabling them to respond quickly to market changes and emerging trends. By staying ahead of competitors and anticipating customer needs, organizations can position themselves more effectively in the marketplace.
• Innovation and business growth. Data-driven insights often uncover opportunities for innovation and business growth. By identifying new market segments, product enhancements, or service offerings, organizations can capitalize on untapped potential and drive revenue growth.
• Cost savings. Analyzing data allows decision makers to identify areas where costs can be reduced or resources reallocated more efficiently. By optimizing processes and minimizing waste, organizations can achieve significant cost savings over time.

Overall, implementing data-driven decision-making empowers organizations to make more informed, strategic choices that drive performance, foster innovation, and fuel long-term success in today’s competitive business landscape.

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How data-driven decision-making aligns with strategic business decisions?

Year by year, decision-makers say that making decisions is getting more complex and the achieved results become uncertain and unpredictable. Data-driven decision-making approach is serving as a catalyst for informed, forward-thinking strategies. By incorporating data analytics into the decision process, business users gain an understanding of market dynamics, consumer behavior, and internal performance metrics.

This data-driven approach enables executives to identify emerging opportunities, anticipate potential risks, and align operational initiatives with overarching business objectives. Forbes reports that 73,5% of respondents from data-leading companies assert that their decision-making processes consistently rely on data.

What is a Data-Driven Culture and how does it support DDDM approach?

A Data-Driven Culture refers to an organizational environment where data is valued, utilized, and integrated into decision-making processes at all levels. Between 2023 and 2024, the number of organizations that established a data and analytics culture increased from 21% to 43%, reports Waveston in the report The State of Data and AI in Leading Companies 2024. Data-Driven Culture encompasses attitudes, behaviors, and practices that prioritize the collection, analysis, and application of data to drive business outcomes. Employees are encouraged to use data to inform their decisions, experiment with new ideas, and continuously improve processes based on evidence and insights.

This culture supports the Data-Driven Decision-Making (DDDM) approach in several ways:

Promoting data literacy

A data-driven culture fosters data literacy among employees, ensuring that they understand how to collect, interpret, and apply the right data set effectively. This empowers individuals across the organization to make better decisions based on evidence rather than intuition alone.

Encouraging collaboration among data teams

In a data-driven culture, collaboration between different departments and teams is encouraged to share data, insights, and best practices. This cross-functional collaboration enables a more holistic view of the business and facilitates better-informed decision-making.

Fostering responsibility

Adopting a data culture promotes responsibility among employees, as decisions are grounded on quantifiable results and performance indicators. This motivates individuals to be accountable for their decisions and pursue constant progress.

Promoting innovation

It respects data and experimentation, which inspires innovation and creativity in the organization. Employees can try out new ideas, check assumptions, and find ways to enhance performance based on data-driven insights.

Responding to change

Organizations can change more easily to fit different market situations and trends when they have a data-driven culture, underscoring the link between data that isn’t just collected, but effectively used to inform strategic changes. They can watch data and key performance indicators regularly and see how consumers or markets are changing and amend their plans accordingly.

Improving business results

A data-driven culture supports the DDDM approach by improving business results and competitive edge. By using data-driven insights to make decisions, businesses can improve processes, enrich customer experiences, and attain better business outcomes.

In summary, a data-driven culture is essential for effectively implementing the DDDM approach, as it fosters a mindset of data-driven decision-making, promotes collaboration and innovation, and drives accountability and performance across the organization.

Read also: Company data management: data-driven decision making

Identifying Relevant Data Sources

Despite recognizing the value of data and employing advanced technology to gather it, most companies are not effectively utilizing their data. As Gartner highlights:

• A staggering 97 percent of data remains unutilized by organizations.
• Over 87 percent of organizations fall into the low maturity bracket regarding their Business Intelligence and analytics capabilities.

A key step in the process is finding appropriate data sources that offer the information required to make smart strategic decisions. These data sources can differ depending on your business, industry, and specific goals, but some common sources are essential to think about.

Firstly, internal sources such as customer relationship management (CRM) systems, enterprise resource planning (ERP) platforms, and sales and marketing databases contain valuable data about customer interactions, sales performance, and operational efficiency. Analyzing this data can uncover patterns, trends, and opportunities for improvement within your organization.

Additionally, external sources like market research reports, industry publications, and competitor analysis can provide valuable context and insights into market trends, consumer behavior, and the competitive landscape. By keeping up with external data sources, business leaders can acquire a complete knowledge of the wider market factors that affect their industry.

Furthermore, emerging sources of data such as social media metrics, web analytics, and sensor data offer valuable real-time insights into customer sentiment, online behavior, and product usage. By using these sources, businesses can remain flexible and adaptable to market changes and customer needs.

Basically, finding relevant data sources means looking for both internal and external data that can help you understand your business operations, market trends, and customer preferences. By accessing these different sources of data, business leaders can prepare for efficient data-driven decision-making and create opportunities for lasting growth and success.

Utilizing Data Visualization for Informed Decisions

Businesses today have access to a lot of information, but it can be hard to manage and analyze. To use this data effectively and find valuable insights, business leaders need to use data visualization methods. Data visualization means showing complicated datasets in a way that is visually appealing and simple to comprehend, such as charts, graphs, and interactive dashboards. 

Business leaders can use data visualization tools and techniques to turn raw data into clear visual displays that show trends, patterns, and outliers. This visual simplicity makes complex concepts easier to understand and allows faster and better decision-making. 

• Line charts show how trends change over time, revealing seasonal variations or long-term patterns. 
• Pie charts and bar graphs are good for comparing percentages or analyzing categorical data, such as market share or customer demographics. 
• Heatmaps and geographic visualizations give spatial insights, showing regional differences or distribution patterns, an example of data to make more informed decisions. 
• Interactive dashboards let users examine data dynamically, zooming in on specific metrics or filtering information based on predefined criteria. This level of interactivity allows business leaders to investigate data from multiple angles, finding hidden insights and making more refined decisions, embodying the essence of decision making defined as using facts. 

Organizations can achieve more understanding of their activities, market trends, and customer preferences by using visuals to display data in attractive ways. This can lead to improved results and a stronger position in the market. 

Enhancing Decision-Making through Customer Data Analysis 

Businesses can learn about customer actions and choices by examining data from different sources like purchases, web interactions, and reviews. 

Segmentation helps group customers for specific marketing, while clustering finds similar groups for customized products. 

Predictive modeling forecasts future actions, helping in planning ahead, and is a prime example of using data to guide decisions. Using customer data analysis in different functions allows data-based decisions, resulting in more individualized experiences and better customer bonds, showcasing how data to make more informed decisions improves customer relationships. 

Implementing Data-Driven Decision-Making in the Decision-Making Process 

Data-driven decision-making means using data analysis throughout the whole decision-making process, from start to finish. Let’s look at a few important stages. It’s not a step-by-step guide but it’s important to know what the essentials before you will delve into the implementation process. 

1. Setting specific goals and measures of success ensures that data collection efforts are focused on relevant metrics that align with business goals. 

2. Collecting and arranging data from various sources, both internal and external, provides a comprehensive understanding of the factors influencing the decision. 

3. Using suitable statistical methods and visualization techniques to examine the data reveals insights and trends that inform the decision-making process. 

4. Working with stakeholders and subject matter experts ensures that diverse perspectives are considered, leading to more robust decisions. 

5. Assessing the outcomes of decisions based on data-driven insights allows for continuous learning and improvement, closing the loop and refining future decision-making processes.  

Cultivating a successful Data-Driven Culture within the Organization 

We already discussed what data culture is. Now let’s focus on how to cultivate it in an organization.  

Leadership influence and engagement 

Leadership shapes the organization’s culture. Executives and managers should strongly support and focus on data-driven decision-making, showing their engagement through behaviors, rules, and resource distribution. 

Developing data literacy and training 

Employees need the right skills and knowledge to comprehend, examine, and explain data. By giving them training programs, workshops, and resources on data literacy, they can use data more efficiently in their choices. 

Making data available and clear 

Make sure that data can be easily reached by employees throughout the organization. Create open data-sharing methods and platforms that allow employees to access suitable data sets and insights to guide their decisions. 

Promotion of cooperation and learning from data 

Create a culture where employees can work together and exchange data-driven findings, effective methods, and positive outcomes. Set up teams or groups that span different functions and where employees can cooperate on data-related tasks and goals. 

Appreciation and incentives for data-oriented actions 

Show appreciation and give incentives to employees who show a dedication to making decisions based on data. Acknowledge the outcomes and accomplishments that come from data-oriented projects to emphasize the value of data in the organization. 

Constant learning and improvement cycle 

Highlight the significance of ongoing learning and improvement in data-driven culture. Motivate employees to give feedback on data processes and systems, and use this feedback to improve and enhance data-driven practices over time. 

Summary – why data-driven decision making is important? 

Decision making is the process in which individuals or organizations make choices based on available information. In today’s digital age, there are a plethora of decision making examples that showcase the power of using data effectively as well as data sources that can be used to inform decisions. From customer feedback to sales data, there is a wealth of information at our fingertips. By utilizing data visualization and Business Intelligence tools, businesses can effectively interpret data and uncover insights that can drive decision making. Through data analysis and ensuring high data quality, organizations can make informed decisions in line with their business goals. 

Big Data and data science have revolutionized the way organizations approach decision making. By leveraging these technologies, businesses can gain a deeper understanding of their operations and customers. In the world of data culture, managers have possibilities to use facts rather than assumptions and build their decision making process relying on data. The importance of data in decision making cannot be understated. 

But, the good news is that data accuracy can easily be improved by implementing various best practice processes, systems, and governance steps. In this article, we look at all things data quality, including why it’s so important, the benefits of good quality data, and some tips and tricks to help you get started on the path to high-quality data immediately.

Let’s get started!

What is Data Quality and Data Quality Management?

Data quality refers to how well a dataset meets criteria for areas such as accuracy, completeness, and validity. As part of a broader data governance strategy, if your data quality is high, it gives your organization the confidence to rely on the data to measure performance, analyze productivity, and make business decisions.

To keep different data sets in good shape, many organizations deploy various data quality management tactics to maintain data standards. While we’ll look at these in more detail later on, data quality management uses a mix of people, processes, and governance steps to monitor data values and continually optimize data entry techniques for greater and greater quality.

Why is Data Quality and Data Integrity Important?

As AI-led big data systems, underpinned by data lakes and data warehouses, become increasingly popular for modern businesses, the saying ‘rubbish in, rubbish out’ is on the minds of many CTOs and CIOs. Poor quality data drives poor quality outcomes, so effective data quality management is essential to maintain data integrity, stay ahead of the game, and enable your business strategy. 

Fail to maintain good data quality standards, and you could find yourself:

• Repeatedly making poor business decisions, driven by inaccurate enterprise data analysis
• Missing opportunities to drive additional revenue 
• Unable to respond to changes in your industry ahead of your competitors
• Open to fines and data breaches, especially if you regularly handle customer data or medical patient data 

To bring this to life even further, in 2021, consulting firm Gartner found that bad data quality costs organizations an average of $12.9 million per year. That’s a lot of money to lose all because of inaccurate data and poor data quality control across your organization!

Also read:  Data mining methods 

The 4 Most Common Data Quality Issues

It’s easy for us to sit here and recommend you improve your data quality, but no one purposefully sets out to have poor-quality data – unfortunately, high-quality, consistent data is simply hard to achieve.

But, there are some common reasons businesses struggle with data quality problems. Let’s take a look at the most common data consistency issues.

• Businesses use a range of different systems that each hold data in different formats and structures, creating inconsistent data standards.
• Businesses take data from multiple data sources, each using its own data types, data pipelines, data formats, and business rules.
• Businesses run many projects to move between different systems. Regular data migration can erode quality effort, making maintaining a trusted data set hard.
• The legislation of particular countries or regions regularly changes, meaning data records constantly need updating, which opens the door to errors and inconsistency.
• Staff simply aren’t educated on maintaining high data quality, leading to the quality standard eroding over time.

Even though many businesses use data analytics to drive business decisions, to maintain data quality, you need to overcome the common challenges that affect every aspect of data. But if you make a fresh start and manage to get new data in good shape, here are some of the benefits you can uncover.

The Benefits of Good Data Quality

Now that we know the consequences of bad data quality, it’s time to look on the bright side. Improving the quality of data stores across your business will allow you to level up your business operations by:

• Improving productivity through automated business rules
• Enhancing the service you can provide to your customers, driving increased revenue opportunities
• Reducing costs thanks to faster decision-making and less duplication
• Creating consistency across processes and systems
• Easily demonstrating compliance thanks to embedded quality assurance frameworks 
• And many more!

Data quality improvement will benefit every corner of your business, so it pays to invest time and effort into a data quality framework that you can implement across the business. To help you begin to address data problems in your business, let’s look at some data management best practices. 

Enhancing Strategic Decision-Making with Quality Data

Understanding the depth of data-driven decision making is crucial for businesses aiming to fully harness the power of high-quality data. It’s not just about having data but making sure that data is of the quality required to produce reliable, actionable insights that influence critical business decisions. Integrating data quality with data-driven decision strategies ensures that the information used is accurate, timely, and relevant, thereby enhancing the effectiveness of strategic choices. For more on how to effectively apply these practices within your organization, read our in-depth exploration on data-driven decision making.

The 6 Data Quality Dimensions – Best Practices to Improve Data Quality

Before embarking on your own data quality improvement process, it’s worth stepping back to look at the fundamentals of a good data governance program.

This begins with the six data quality dimensions. Each views data quality through a different lens, helping you consider all angles to ensure your data is fit for purpose. Let’s take a look at each in turn. 

• Completeness: Completeness measures the amount of data records that are complete and ready to be used. Partially complete data may lead to unreliable results that are not fully representative of the analysis you’re trying to complete.
  
• Uniqueness: Data sets have a habit of duplicating themselves, so uniqueness measures the percentage of duplicate data in your sets. To avoid misrepresentation, tactics such as data cleansing and data quality rules help ensure that records are unique and individual within your sets.
  
• Validity:When it comes to using the data in real time, the validity of data measures how much of the data meets the required format for a particular business rule. Often, this refers to data being in the right format, pattern, or range to make it applicable to a given situation.
  
• Timeliness: In an ever-digital world, data must be available at the click of a button. Real-time data processing is a demand of many businesses, so when managing data, you have to ensure it can be generated, received, and manipulated at exactly the time it’s needed.
  
• Accuracy: In a world where many systems use the same sets of data, accuracy refers to how reliable the data is versus the agreed ‘source of truth’.
  
• Consistency: Building on accuracy, while one system often leads the way to ensure reliable data, other systems will conduct data quality checks to measure data quality against the master source. This is what consistency is all about, ensuring that, as well as using other data quality metrics to ensure data quality at an enterprise level using rules, audits, and data quality assessments.
  
• Fitness for purpose: Finally, effective data is only effective if it helps enable the business to do its job. While the accuracy of the data could be 100% if no one needs it, it’s a waste of time, effort, and money.

The dimensions of data quality are the underpinning foundations of great data governance. They should be applied no matter the size and shape of your data sets and no matter where the data is located. 

Here’s How to Get Started with Data Governance – 6 Data Quality Tools, Techniques, and Processes

Now that we know data quality is important for business performance and the dimensions to build trust in data, it’s time to build out your own data quality assessment framework. This will help you continuously root out inaccurate or inconsistent data and make positive strides to fix it for the benefit of your entire organization.

Let’s take a look at the 6-step process.

#1 – Hire Dedicated Data Stewards

If you want to get serious about data quality, it all starts with getting the right people on board who are dedicated to improving the state of data in your organization. Many businesses start by hiring a Data Steward who’s ultimately responsible for leading the governance approach to data quality and improving the accuracy of data in your organization.

While the best results come from hiring dedicated stewards and data managers, it could also be a side-of-desk activity for data scientists, architects, or information security professionals. This will help you get off to a good start if you don’t have the budget to take on a new headcount. 

Either way, once the roles are assigned, invest in adequate training and awareness of data quality to ensure they have all the knowledge they need to start promoting data quality best practices.

#2 – Agree on a Data Profiling Strategy

With the right people in place, it’s time to start building your data profiling strategy. Data profiling is the process of examining, analyzing, and creating a summary of the data you hold. Put simply, this is the process you’ll go through to assess the quality of your data. 

Within your strategy, you’ll want to define things such as: 

• How often you’ll analyze data based in different locations
• The techniques/lenses you’ll use to identify data
• How you’ll measure the data accuracy
• The level of data quality that’s acceptable for your business
• How you’ll report data quality issues to other departments, such as IT, Risk, and Compliance

Once you have a clear view of your strategy, it’s time to put your data quality measures into action.

#3 – Go Hunting for Data Quality Problems

Now, it’s time for action as you hunt for data quality problems within your organization. You’ll need to plan exactly how you’ll do this, arranging things like data access requests, access to systems and tools, and time with relevant stakeholders to understand how data is used in a particular area. 

It’s best to have a clear data governance process to accompany this. This will help you keep track of what’s happening when and ensure you have oversight and support if you encounter any problems.

Once you’ve completed your assessment, you’ll likely generate an outcome report to share with stakeholders. This will clearly show the level of data quality in that particular area, including an objective pass/fail assessment and recommendations for improvement. 

#4 – Build an Expert Data-Fixing Taskforce

With a list of recommendations, it’s time to get the right people together to fix the problems. Depending on the size and scope of your data quality team, this may be done yourself or passed onto the business area that owns the data to take action themselves. 

The latter aligns with a master data management approach, whereby business and IT professionals work together to ensure data quality remains high across all applications and data sets. Given that poor data quality can result in various negative consequences, it makes sense for everyone to pull together to ensure the data is fit to serve the business operations. 

#5 – Audit the Process with Quality Assurance Activities

Like all good compliance activities, you need to add a third-party review to ensure your data governance framework is adhered to. Whether you team up with a formal auditing team or set up your own local quality assurance, you have to keep your data stewards, business owners, and application teams accountable for data quality best practices. 

Many organizations track a schedule of data quality reviews, ensuring each is completed on time, to a good standard, and that any follow-on actions are completed to a high standard. This helps keep bad data at bay, ensuring that every component of the overall data quality governance model is effective. 

#6 – Track It All With a Data Quality Management Tool

Where many businesses start and fail with data quality is by trying to do it all manually. Especially when dealing with large data sets, a data quality solution or tool can help automate a lot of manual work, increasing the speed and accuracy of your analysis activities. 

Popular tools such as OpenRefine, Talend, and Cloudingo are perfect for executing your data profiling strategy, helping you identify, analyze, clean, and re-format data to boost quality and provide confidence to business leaders when making decisions. 

While these tools require investment and setup, they’ll help take your data quality capabilities to the next level as you standardize and assure your organization’s entire enterprise data set. 

Businesses succeed and fail based on their data quality

In a world where data is at the heart of everything we do, if your data quality is low, it can leave you at a real disadvantage. If you want to avoid risk, poor decision-making, and slow performance, you need to invest in your ability to maintain high levels of data quality. 

The tips in this guide are a great place to start, but for the best results across all of your data needs, we suggest partnering with a technology expert. At Inetum, our Smart Data offering helps organizations worldwide define, implement, and achieve their data strategies, including initial visioning, change management, and long-term data governance. 

If you’d like to join the hundreds of customers who benefit from our expertise, knowledge, and partnership, reach out today to understand how we can help level up your enterprise data quality. 

DATA ANALYTICS SERVICES Use data to your advantage Discover our Data Management End-to-End offer! Find out more!

Understanding Azure Cost Management

Cost optimization on the Azure platform involves using tools and strategies that allow you to minimize the costs associated with using the cloud. 

The importance of cost optimization in Azure is a key element in the effective and efficient use of cloud computing. Cost optimization in Azure is not just about minimizing expenses; it has a significant impact on overall operational efficiency. 

One of the most important aspects of cost optimization in Azure is monitoring resource utilization. This allows you to identify unnecessary or unused resources and turn them off, saving costs. In addition, cost optimization on the Azure platform also includes the use of process automation tools, which allows you to reduce the costs associated with infrastructure management. 

Read also:  Azure Cost Management 101. How to optimize cloud costs?

Zero-Cost Azure Cost Optimization Best Practice 

Cost optimization is becoming an integral part of the cloud strategy, enabling organizations to achieve the highest performance with minimal financial outlays. To achieve this, it is crucial to remember about practice that do not generate additional costs. 

Use of native Azure tools 

Azure provides a comprehensive suite of tools specifically crafted to streamline the monitoring and optimization of costs within the cloud environment. One notable tool is Azure Cost Management and Billing, a centralized platform that empowers users to meticulously track their expenditures. Through this tool, organizations can gain insights into resource consumption patterns, identify cost drivers, and make informed decisions on where cost optimizations can be implemented. 

Furthermore, Azure Advisor plays a crucial role by offering personalized recommendations for resource management. This intelligent advisory service leverages Machine Learning algorithms to analyse usage patterns and suggests optimizations tailored to the specific needs of the organization. These recommendations span diverse aspects, ranging from right-sizing virtual machines to identifying underutilized resources, providing actionable insights for efficient cost management. 

Virtual machine management 

The dynamic scaling of resources based on real-time demand is a cornerstone of efficient cost optimization. Leveraging the auto-scaling feature within Azure allows organizations to seamlessly adjust the number of virtual machines in response to fluctuating workloads. During periods of increased demand, the system can automatically provision additional resources, ensuring optimal performance without incurring unnecessary costs during periods of reduced activity. 

The auto-scaling mechanism can be finely tuned to respond to predefined metrics such as CPU utilization or network traffic, providing a flexible and responsive infrastructure. This not only enhances the overall efficiency of resource utilization but also aligns with the principles of pay-as-you-go, where costs are incurred only when resources are actively needed. 

Data warehouse optimization 

Strategic management of data warehousing significantly influences overall costs in the cloud. Adopting practices such as data partitioning and compression becomes instrumental in curbing expenses related to storage. Data partitioning involves dividing large datasets into smaller, more manageable segments, allowing for more efficient storage and retrieval. Compression, on the other hand, reduces the physical storage space required for data, leading to direct cost savings in terms of cloud storage. 

Azure provides tools and features that enable organizations to implement these optimization strategies seamlessly. By incorporating these practices, businesses not only economize on storage costs but also enhance the performance of data-intensive workloads, contributing to an overall more cost-effective cloud deployment. 

Network traffic management 

Effective control of data transfer costs is paramount in the pursuit of cloud cost optimization. Conscious management of network traffic emerges as a crucial strategy in achieving this objective. Organizations can strategically minimize data transfer between Azure regions by strategically placing resources or utilizing region-specific services. Additionally, the adoption of Content Delivery Network (CDN) services aids in optimizing the delivery of content by strategically caching data at edge locations, reducing latency, and mitigating unnecessary data transfer costs. 

By implementing robust network traffic management practices, organizations can mitigate the impact of data transfer costs on their overall cloud expenditure. This involves a strategic balance between optimizing the performance of applications and minimizing unnecessary data movement, ultimately contributing to a more efficient and cost-effective cloud infrastructure.

Take Advantage of Azure Cloud Optimization trends 

Trends in Azure cost optimization are increasingly focused on both strategic resource management and the utilization of specific Azure services. Organizations can significantly reduce their Azure spend without compromising performance by: 

• Strategic Resource Management: This trend involves the careful allocation and management of resources to ensure they are used efficiently. By strategically managing resources, organizations can avoid unnecessary Azure costs. 
• Utilization of Specific Azure Services: Services like Azure Hybrid Benefit and Azure Reservations are being increasingly used for cost optimization. Azure Hybrid Benefit allows organizations to use their on-premises Windows Server and SQL Server licenses in Azure, leading to significant cost savings. Azure Reservations, on the other hand, provide discounted prices for one- or three-year commitments on various Azure services. 
• Regular Cost Analysis: Regularly analysing Azure costs can help identify areas where spending can be reduced without compromising performance. Tools like Azure Cost Management are being used to monitor, allocate, and optimize cloud costs. These tools provide insights into where organizations are accruing costs and offer suggestions for reducing unnecessary spending. 
• Use of Azure Spot VMs and Reserved Instances: Azure Spot VMs offer unused Azure capacity at a significant discount, while reserved instances allow organizations to commit to Azure resources over a one- or three-year period in exchange for a discount. Both of these strategies help optimize the Azure budget. 
• Leveraging Azure Cost Optimization Strategies: Using server licenses with Azure and Azure Kubernetes Service can help organizations find the most cost-effective approach for their specific workloads. 
• Trend of Containerization: The use of container technologies like Docker and Kubernetes allows for efficient management and deployment of applications in the cloud. This not only improves performance but also contributes to cost reduction. 
• Security as a Key Element of Cost Optimization: Preventing security breaches is more cost-effective than addressing them after they occur. Azure offers a range of tools and services to protect data and applications in the cloud, which can significantly aid in cost optimization efforts. 

Future Perspective: The Development of Azure policy 

The future of cost optimization in the cloud is promising and is expected to develop significantly in the coming. This is because as many as 94% of organizations are already in the cloud, but 69% of them don’t have a defined cloud strategy. It includes the development of advanced tools and strategies to help companies manage and reduce cloud expenses. One such tool is Azure Policy, which helps organizations enforce and maintain compliance with corporate standards and service level agreements.  

These policies can play a key role in controlling and minimizing cloud costs by setting guidelines and automatically implementing cost-saving measures. Additionally, the development of more sophisticated automation tools will further improve the process of resource allocation and cost control. Technologies such as Machine Learning and Artificial Intelligence solutions can provide companies with insights and recommendations for optimizing cloud spend. 

CLOUD ENGINEERING Make the best use of the cloud! Enter the digital world taking advantage of our cloud competences! Get started now!

Tips and Best Practices to Optimize your Azure – Summary  

Utilizing cost optimization techniques, such as resource management, use of monitoring and analysis tools, as well as understanding and optimizing pricing models, can significantly enhance cloud performance while simultaneously reducing costs. 

Inetum, as an experienced specialist in the implementation and cost optimization of cloud solutions, can assist in fully exploiting the potential of Microsoft Azure. Thanks to our experience and knowledge, we are able to provide tailored solutions that will help achieve optimal cloud performance at minimal costs. 

Remember, cost optimization is a continuous process that requires regular evaluation and adjustment. Therefore, using the services of experts such as Inetum can bring significant benefits to your organization. 

“The success of implementing GenAI solutions is influenced by several factors, including the competencies of the implementation team, the tools and licensing forms used, appropriate business processes, and cost-effectiveness. The costs of such an investment can be optimized, for instance, by using properly selected algorithms that require less computing power and, consequently, generate fewer costs.

Conducting an AI Maturity Assessment is beneficial to achieve optimal conditions in these areas. As part of this assessment, an experienced team will evaluate the organization’s readiness to implement GenAI solutions and provide recommendations for future work.” – adds Marek Czachorowski, Practice Leader of Cloud Engineering at Inetum.

So how to save money on the cloud and scale it all right? One way is to take advantage of solutions from public cloud providers. In this guide, we examine solutions of the leader in public cloud services, that is Azure Saving Plans offer. Find out what they are, how they differ from Azure Reserved Instances, and how to purchase them and use them most effectively.

How to Buy and Manage Azure Savings Plans?

According to respondents to Flexera’s “State of the Cloud 2023” report, cost management is now even more important than cyber security. Over 80% of respondents surveyed say that managing the cost of the cloud is their number 1 challenge. In times of economic concerns, companies are seeking support from FinOps practices or building the Cloud Centers of Excellence. 

It’s worth knowing that Azure savings plans for computing capacities are also available. Let’s look at what public cloud leader Microsoft offers in their Azure Portal. Below we explain how to gain access to the savings plans.

What Azure Savings plan for computing is?

The idea behind the savings plan for computing is simple: you commit to spending a fixed hourly amount on compute services for one or three years and in doing so, you will be able to save up to 65% on pay-as-you-go prices.

How do you buy a savings plan?

1. First, ensure you meet the conditions for purchasing a savings plan – check your billing plan.
2. Note that savings plan discounts are only applicable to resources connected to subscriptions acquired through an Enterprise Agreement (EA), Microsoft Customer Agreement (MCA), or Microsoft Partner Agreement (MPA).
3. Once done, proceed to Azure Portal, and navigate to All services, and then choose Savings plans. From there, click Add to buy a saving plan.

Azure Savings Plans Benefits Explained

Understanding Savings Plan Benefits for Compute Usage

• Working on your terms – you decide whether to commit for one year or three years, and determine the hourly plan based on recommendations for past compute usage.  
• Optimizing consumption of selected services  – as part of savings plans, lower prices are available for selected services, including Azure Virtual Machines, Azure App Service, container instances, Azure Functions, and more.
• Saving even more – the longer the commitment time, the greater the benefits and savings. You can combine savings offers, such as with Azure Hybrid Benefit explained below. 
•  Gaining flexibility – even though you cannot modify or cancel the commitment, if your usage exceeds estimated figures, you may add another savings plan to cover the extra usage for computing resources.
  Key differences between Azure Reservation vs. Savings Plan A savings plan is not the only way to save expenditures. Azure gives you various options – another s is the Azure Reservation service. How do they differ?
• Azure Reservation – it means a commitment to a specific virtual machine type in a given cloud region. Choose reserved instances for stable workloads or if you do not expect any changes eg. to the cloud region. Azure reserved instances provide cost savings compared to pay-as-you-go pricing and ensure the availability of resources for your specific workload. It’s important to carefully consider your workload requirements and usage patterns before reserving instances.
• Azure Savings Plan – it is a commitment to spending a fixed hourly amount in total on compute services. This option is recommended for dynamic workloads. Azure savings plan offers a pricing model that provides significant cost savings on consistent compute usage in Azure regions. By purchasing an Azure Savings plan, you can achieve savings across multiple Azure regions and select compute services covered by the plan.

Getting the most out of Azure Saving Plans

It’s a good idea to carry out thorough research on the topic before committing to a specific plan. Below are two popular options.

Utilizing Azure Advisor to Leverage Savings Plan Discounts

Azure Advisor is available for free in the Azure Portal. Using Azure Advisor can be compared to having your cloud assistant, which with Machine Learning capabilities at its service, helps you recognize underutilized resources and ensure optimal usage of virtual machines and their scale sets. Then, the Advisor recommends shutting down or resizing given resources.

But Azure Advisor is also helpful in estimating recommendations on appropriate plans for your company.  Based on hourly and total on-demand usage costs, calculations are created to help you choose the right option.

Get inspired to save money with minimal effort and read about this powerful tool in our guide: Azure Advisor – the Microsoft Cost Management Tool.

Utilizing Azure Hybrid Benefit with Savings Plans

Azure Hybrid Benefit is a special licensing offer for Azure cloud migrations.
In simple terms, Azure Hybrid Benefit is a program that allows new Azure customers to use their existing on-premises licenses for certain Microsoft software products. This way, they can reduce the cost of running them in the Azure cloud.

For instance, if someone already has licenses for certain Microsoft software (Windows Server or SQL Server) and wants to move their workloads to the cloud, Azure Hybrid Benefit lets them apply existing licenses and run the same product within Azure.

By utilizing Azure Hybrid Benefit with new Savings Plans, you can potentially save more money on Azure compute services.

Purchasing Azure Savings plan – summary 

A savings plan allows you to lower cloud costs and optimize selected Azure resources at the same time. These plans cannot be canceled or changed during the contract, so the choice must be well-thought-out depending on the workloads, services, and cloud regions used. Also, you need to consider any planned changes in this context in the future. While there are a lot of knowledge sources on the Microsoft Learn portal, it’s good to know the opinions of cloud migrations and cost optimization experts. You don’t have to be alone in making this decision!

Read also: Azure Cost Management 101. How to optimize cloud costs?

Contact our subject matter experts if you want to quickly choose the right plan and start saving money right away. 

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According to Flexera’s “State of the Cloud 2023” report, for the first time in a decade for over 80% of companies, managing cloud spend has become a greater challenge than security. 

The Microsoft Azure platform offers organizations, regardless of industry or business challenge, endless opportunities to scale their IT environment and cost savings. However, to effectively leverage these opportunities and reduce expenses, it is worthwhile to reach for 10 proven and effective practices. 

10 best practices for optimizing Azure cloud costs  

1. Accurate Size and Performance Analysis of Virtual Machines 

Before you throw yourself into the Azure cloud, consider what kind of computing power and instance size you really need. Avoid excessive computing power choices, which can lead to unnecessary costs. 

2. Use of SPOT and B Series Virtual Machines 

SPOT and “b-series” virtual machines may be a good alternative to traditional services. The difference between standard virtual machines and b-series ones is that in case of the latter computing power is adapted to the current needs of the application, which allows for cost savings. SPOT, on the other hand, is a Microsoft Azure cloud service that allows you to run virtual machines in “spot instance” mode, i.e. only when free resources are available in the cloud. This allows you to save on expenditures because the prices for using VMs in “spot instance” mode are lower than for running VMs in a standard way. 

3. Monitoring and Disabling Unused Resources 

An extremely important part of cost optimization is to regularly monitor the use of resources in the Azure cloud and consistently shut down unused elements, such as virtual machines, databases, or data stores. This operation can be performed manually but can also be automated using tools such as Azure Automation or Azure Logic Apps. 

Read also: Azure Cost Management Tools: 10 Tools to Optimize Your Budget 

4. Database Instance Optimization 

Database instances in the Azure cloud can be costly, especially if they are used suboptimally. To minimize costs, consider using elastic database pools that allow you to automatically scale up and down as needed. You can also consider using the Azure SQL Database Managed Instance service, which offers lower costs compared to traditional database instances. 

5. Advanced Use of the Autoscaling Function 

Advanced use of the autoscaling feature in the Azure cloud allows resources to be automatically sized according to the workload of an application or service, minimizing unnecessary costs. Combining this feature with SPOT and B-series VMs will contribute to additional cost reductions. Advanced autoscaling options, such as scheduling or application metrics, enable precise optimization of resource utilization and effective cost management. 

6. Application of Reserved Instances

The use of proprietary installations allows the purchase of a predetermined resource for a long period of time. This provides significant savings compared to paying for resources on a pay-as-you-go basis. Proprietary instances are particularly advantageous for stable and predictable workloads. 

7. Effective Cleaning of Backup Copies and Computing Instances 

Managing backups and computing instances on a regular basis will allow you to avoid accumulating unnecessary costs. Set policies for storing backups, delete those that are no longer needed, and control the number of active computing instances to maintain an optimal balance between availability and cost. 

8. Full Control and Costs Visibility  

Keeping costs under control and planning wisely should come at the beginning of your cloud adventure. Give yourself full control and visibility over costs by using the monitoring and reporting tools available in the Azure cloud. Regularly analyze your cost reports, identify areas where you can make savings, and adjust your strategy based on the information you gain to avoid hidden cloud computing costs that affect your bills. 

9. Effective Monitoring of Data Transfer 

Control and analyze data transfer between different services and regions. Implement strategies to minimize unnecessary data transfer by using local copies or CDN (Content Delivery Network). Optimizing data transfer can significantly contribute to reducing costs associated with cloud services. 

10. Prioritizing Testing and Optimization of Application Code 

The practice involves conducting regular performance tests and optimizing application code before moving it to the Azure cloud. Improving code efficiency can significantly impact resource consumption, which translates into lower operating costs. Reliable testing and optimization before migrating to the cloud avoids unnecessary expenses related to sub-optimal use of resources. 

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• 90% of companies use services from multiple cloud service providers (hybrid cloud / multi-cloud). 
• 30% of cloud courses go unused/underutilized. 
• By 2025, spending on cloud solutions will exceed 50% of IT expenditure. 

The hidden costs of your cloud infrastructure 

Cloud providers’ offerings are rich and tempting, and clients’ expectations are high: it will all be innovative, easier, and faster. Gartner forecasts that business IT spending on public cloud computing will hit 50% by 2025. But what about the pitfalls of cloud migration that we would prefer to consider later (or not at all)?  

Here are the 5 most common mistakes that generate unforeseen costs that will be painful for your budget: 

1. Cloud storage issues 

Not properly managing your cloud storage can lead to unnecessary costs related to storing redundant or outdated data. It’s important to regularly review and clean up your storage. 

2. Overprovisioning 

This is a common problem, especially in new cloud projects. Overestimating your resource needs and purchasing more cloud capacity than necessary will most certainly lead to wasted spending, aka “cloud waste”.  

3. Underutilization of resources / Virtual Machines 

Running virtual machines that are not fully utilized can lead to wasted resources and increased costs. You must regularly monitor and adjust your VM usage to optimize performance and minimize unnecessary spending. 

4. Choosing the wrong location 

You should not only think about choosing the right cloud provider, but also about the geographic location of your cloud services. Selecting the wrong location can result in higher data transfer costs and latency issues.  

5. Data transfer costs 

We have already discussed this aspect in detail. Transferring large amounts of data in and out of the cloud can result in significant costs.

The impact of the hidden costs of cloud computing 

Now that you know that networking, storage, data transfer, and computing CPU issues are some of the most common factors affecting cloud costs, let’s move on to understanding how optimizing them will help you avoid unpleasant surprises.  

Right-sizing your resources, choosing the right regions, identifying idle workloads, and choosing appropriate cloud storage plans are just some of the practices you can apply to use the available resources more wisely without overpaying for them. We discuss them in detail below. 

Understanding the true cost of cloud services 

The true cost of the cloud does not have to be high. Often, when just starting to move to the cloud, capacities, VMs, services, or applications are selected and then later are not used or underutilized. The most important thing is to locate such waste as early as possible and adjust your usage accordingly. Fortunately, there are proven tools and tactics that identify troublesome areas and provide you with specific recommendations.   

Avoiding the hidden costs of cloud computing 

With proper cost management, you can reduce your cloud costs by up to 30%. In this chapter, you’ll learn strategies to minimize the risk of unforeseen costs and how to minimize your cloud expenses. 

Strategies to mitigate unanticipated expenses in cloud computing 

Even if your enterprise has just migrated to the cloud and you are out of your depth when it comes to cost management, you are not at a disadvantage. It’s worth taking an interest in strategies to minimize costs as early as possible so you don’t fall into one of the traps we discussed in the FinOps guide or the Azure Cost Management article.  

First of all: 

1. Learn about the available billing models and choose the right one 

Public cloud vendors offer models based on forward billing (regardless of the resources and capacity used), as well as pay-as-you-go (PAYG) models. 

2. Take advantage of autoscaling 

This option adjusts the scaling of resources and applications to your actual usage, ensuring adequate performance. Such a dynamic environment is ideal for handling variable traffic, such as those struggling with sales peaks for online stores, for example. 

3. Use infrastructure as a code (IaaC) 

Instead of manually configuring IT environments, with IaaC you can configure servers in such a way as to use resources only when needed. If you are a software house developing a system in the cloud, you can, for example, disable the development environment on weekends if it is not used then, or configure the service to be active only during deployment. 

Considerations while choosing a public cloud provider 

Service costs and pricing plans are not everything. It’s worth learning about the provider’s SLAs, its legal policies, and its approach to cyber security to know what measures safeguard your data.  

Your choice will also depend on business goals and the way the cloud is used. For example, Azure is considered a top choice in terms of the hybrid cloud (which, by the way, almost 90% of enterprises use, according to Flexera), while Amazon’s cloud is a great solution for AWS integration, and the Google Cloud platform for collaboration.  

Optimizing cloud infrastructure to reduce hidden expenses 

Many companies can easily optimize their cloud infrastructure by taking advantage of strategies such as scaling and right-sizing resources, using FinOps tools to monitor spending, and setting budget alerts. Financial Operations tools benefit from automation and take cost management to a new level. If you’re curious about the specifics, read our comprehensive cloud guides:  

• Azure Cost Management
• Azure Cost Management Tools
• Azure Advisor
• Azure savings plans
• Cloud Native applications 

Data egress and other issues. Understanding data transfer costs  

When talking about the cost of data transfer in the cloud, you first need to familiarize yourself with the term ‘data egress’. This means data exiting a network and being moved to an external location. The expenses involved are separate from those your company already incurs for storage and computing; hence, they are hard to monitor and keep under control.  

What are the sample transfer costs? They largely depend on the region. 

For the Azure service, for example, data transfer within the Availability Zone is free, but between individual AZs in the same region, it is already €0.010 per GB. Of course, these costs increase depending on which continents the data is transferred between. Intercontinental data transfer between Europe and other continents, for example, will cost you €0.046 per GB. You can find the full price list on Azure Bandwidth Pricing. These costs may seem small at first glance, but remember: we are talking about huge amounts of data, counted in terabytes, and sums for that can go into the millions. 

How to reduce data transfer costs 

• Choose the region wisely since data transfer fees differ depending on the continent. For instance, transfer costs from Asia, Oceania, the Middle East, and Africa to other continents are higher (€0.073 per GB). 
• Optimize your routing policy with the help of cloud experts. 
• Consider building your systems architecture in such a way as to minimize data transfer across cloud regions or availability zones. 

Implementing a cost-effective hybrid cloud strategy 

The hybrid cloud is a leading trend. In 2022, over 90% of organizations surveyed by CISCO in the Global Trend Report admitted they use more than two public clouds (compared to only 8% who utilize the services of a single provider).  

Understanding how to manage a multi-cloud approach is an important factor in reducing unnecessary costs.  

Implementing a hybrid cloud strategy means determining which applications and data are allocated to particular parts of your infrastructure. You can achieve this most easily with the help of cloud architects and consultants who have experience with other cloud migration projects. 

Best practices for managing cloud costs 

Implementing budgeting and monitoring tools for cloud expenditure 

FinOps and cloud cost monitoring tools are one of the fastest-growing areas and a key trend in cloud computing. In the recent Flexera State of the Cloud 2023 survey, over 80% of enterprises indicated that the challenge of cloud cost management was their top priority. 

Leveraging cloud provider discounts and savings programs 

Consider taking advantage of deals and discounts – public cloud providers offer them, e.g., for reserved instances. Check your service provider’s offer to ensure you don’t overpay for on-demand services that may generate unexpected/hidden cloud costs.  

For example, with Microsoft Azure you can:  

• Get more than 60% off specific computing services when you subscribe to a savings plan. Just select the duration of your plan and the hourly expenditure amount.  
• Save about 70% by reserving your resources in advance. 

Combating hidden cloud costs – summary

Cloud solutions give you more elasticity and agility than traditional data centers and on-premises solutions. The fight against hidden cloud costs does not have to be an uneven struggle! Remember, a properly implemented and managed cloud will save you money. Still, it takes a well-thought-out strategy rather than individual ad hoc actions. Fortunately, there are proven ways and specific tools that will enable you to use the cloud to its full potential. 

Want to gain cost and competitive advantage in the cloud?

Consult with Marek Dobkowski, Head of Microsoft Practice, for expert guidance.

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But when organizations need to enhance their cloud capabilities, it’s hard to know which scaling strategy is the best to take. The two most popular scaling strategies, horizontal and vertical, and the common routes – but how do you know which one is right for you? 

What is Cloud Scaling & Cloud Scalability? 

In cloud computing, scalability describes your technology’s ability to increase and decrease in size and capability to meet day-to-day changes in demand. With many people now utilizing external providers such as Azure and AWS for their cloud computing needs, this scalability is something organizations need their suppliers to provide.  

To help understand the concept, let’s bring scalability to life. Imagine you’re running a web portal that manages a demand of 1000 users per day. Your infrastructure has the bandwidth, processing power, and resilience to comfortably manage that demand. Let’s say you launch a new product and drive an additional 1000 users to the portal; that’s a 100% increase in demand – could your systems handle that? 

Systems that are scalable can, with servers, nodes, and databases that are able to react almost instantly to an increased workload without lengthy upgrades or periods of downtime. This helps you stay online and react to your customer’s needs. 

Scalability is something all modern businesses want, but there are many different ways to achieve it. Let’s look at the two most common models, horizontal and vertical scaling.  

What is Horizontal Scaling? 

Horizontal scaling involves the addition of new servers and nodes to increase capacity across multiple machines. Rather than simply relying on the capacity of a single machine, scaling horizontally sees companies adding more machines to deal with an increase in demand.  

For example, imagine you were running a server with 1 CPU, 32GB RAM, and 10TB storage. To horizontally scale, you would add servers with the same specifications to complement the first. So, if you had 3 of these servers, your overall capability would grow to 3 CPU’s, 96GB RAM, and 30TB storage.  

This concept isn’t specific to computing resources. Business teams have been using this scaling system for years, bringing more people into the team and spreading the work as their organizations grow – it’s the same concept! 

What is Vertical Scaling? 

On the other hand, vertical scaling increases the existing machine’s capacity to drive better performance. Rather than implementing multiples of the same capability, when you scale vertically, you invest in that single server or node, increasing its ability to handle an increased workload by way of an upgrade. 

Again, if we use the previous example of a server with 1 CPU, 32GB RAM, and 10TB storage, vertical scaling may increase the capacity by upgrading to 64GB RAM and 20TB storage to handle additional demand.  

Again, this concept of adding additional capacity isn’t unique to managed cloud scaling. For years, businesses have invested in training their people to learn new skills and techniques to enable them to handle more advanced tasks – again, it’s the same concept!  

The Key Differences Between Horizontal and Vertical Scaling 

Now that we know what scaling horizontally and vertically looks like, let’s compare the key differences between the two. If you need to scale, horizontal scaling and vertical scaling are both good options but do work differently in reality.  

Let’s explore some of the differences now.  

Scaling Execution 

Horizontal Scaling – You’re scaling in and out as you add more nodes/servers in a distributed computing model.

Vertical Scaling – You’re scaling up and down as you increase or decrease the computing power of a single node.  

Ongoing Maintenance 

Horizontal Scaling – More maintenance overhead as you have more infrastructure to upgrade, service, and modify.  

Vertical Scaling – Less maintenance as you still have the same number of nodes, just with more power.  

Implementation Downtime 

Horizontal Scaling – None. When you scale horizontally, your existing infrastructure can stay running throughout. 

Vertical Scaling – Some. When you upgrade a single server, it will need downtime to replace components and perform upgrades. 

Work Distribution 

Horizontal Scaling – Work is spread evenly across multiple machines, with different machines responsible for other tasks.  

Vertical Scaling – Work is isolated to one machine, with techniques such as cross-threading used to allocate resources to different types of requests,  

Implementation and Run Costs 

Horizontal Scaling – There are high initial costs to implement more infrastructure, but over time, costs can be optimized.  

Vertical Scaling – There are low initial costs to implement upgrades, but if not fully utilized, vertical scaling isn’t a good cloud cost management solution. 

How to Choose the Right Scaling Solution for Your Business – Horizontal vs Vertical Scaling 

You now have all the key information on vertical scaling and horizontal scaling to begin to choose the right solution for your business.  

To finish this article, let’s look at the pros and cons of each to help you make the best decision for your organization.  

Also read: 

• 7 cloud computing trends for 2025-2027 
• Azure Cost Management 101
• NoSQL vs SQL – the key differences 

Pros of Horizontal Scaling 

The advantages of horizontal scaling are: 

• Easier to Execute – Using horizontal scaling is a more straightforward strategy as you’re essentially copying and pasting your existing config to another machine.  
• Better Resilience – Having multiple machines gives you security if one was to fail. This reduces single point of failure risk and helps you react faster when disaster strikes.  
• Less Downtime – While horizontal scaling may require you to add extra machines, this scaling strategy means you won’t have to turn off any existing machines.  

Cons of Horizontal Scaling 

The disadvantages of horizontal scaling are: 

• Higher Upfront Costs – By adding additional machines, horizontal scaling would cost your business more upfront versus vertical scaling approaches.  
• More To Maintain – Horizontal upgrades require more work and effort from the team as the same patches, fixes, and version upgrades need to be completed multiple times.  
• More Supplier Coordination – Depending on your cloud service provider, this type of scaling may require more management with suppliers as you have more assets to oversee. 

Pros of Vertical Scaling 

The advantages of vertical scaling are:  

• Cost Effective – Because vertical scaling focuses on upgrading existing machines, it’s likely to cost you less upfront. 
• Less Management Overhead – Vertical scaling also means less management overhead as you still have just one overall asset to manage. This is great when working with suppliers such as Azure and AWS Cloud as there’s less complexity in your contract.  
• Simpler Maintenance – Vertical scaling allows you to keep your maintenance simple as you’ll only have one machine to maintain – this means patches, fixes, and upgrades only need to be applied once.  

Cons of Vertical Scaling 

The disadvantages of vertical scaling are:  

• More Downtime – If you’re scaling often, a vertical option will inevitably lead to more downtime as you’re upgrading in-place resources.  
• Failure Risk – The key standout when you compare vertical and horizontal scaling is the failure risk. By continually vertically scaling, you put all of your resources into one machine, which, if it fails, could bring your operation to a halt.  
• Capability Limitation – If you continually use this type of scaling strategy, eventually, you will reach the limit of what one machine, server, or node can deliver. This may not be a problem in the short term, but over time, it may lead to some challenges.  

The Key Things to Consider 

Based on the advantages and disadvantages of horizontal and vertical scaling, we’d recommend taking the following routes: 

• If you’re looking for a short-term scaling solution that’s cost-effective and easy to implement, vertical scaling could be the best strategy for your business growth ambitions. 
• If you’re looking for a long-term scaling solution that maximizes performance, reduces risk, and requires less downtime, a horizontal strategy would be what’s right for your business.  
   

It’s Time to Boost your Business Through a Mix of Horizontal Scaling and Vertical Scaling Strategies 

In a time when businesses everywhere need to maximize their IT performance, having flexible and scalable cloud-based platforms is essential for success. Horizontal and vertical scaling strategies are the most common, helping organizations move quickly while managing cost, risks, and downtime.  

Even when you’re armed with the right cloud scaling knowledge, organizations achieve the best results when they partner with the right experts. At Inetum, we have years of experience helping clients achieve their cloud transformation goals by leveraging the power of Microsoft Azure, whether through a full-scale cloud implementation or tactical capability enhancement.  

If you’d like to take the next steps, find out more about what we offer by visiting the Cloud Services page on our website or reach out to us! 

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In this complete guide to effective remote work, we will unveil the secrets of productivity that will help you succeed as a remote worker. We will analyze the tools and strategies that will enable you to effectively manage your time, communicate with your team and ensure work-life balance. We will also consider issues related to self-discipline, mental health and techniques for dealing with isolation, which are indispensable when it comes to remote work.

The current state of remote work – what does remote work mean?

Nowadays, people are increasingly striving to gain the skills to work remotely more effectively, seeking work-life balance, eliminating the need for a time-consuming commute to the office, and reaping the benefits of a flexible work schedule. But what actually is remote work?

Remote work, also called teleworking, involves performing professional duties outside of a traditional office or workplace. This type of work can be carried out in many forms, depending on the type of work and the needs of the employer. Below are a few examples.

1. Fully remote work: The employee works entirely remotely, without having to visit the office or another workplace. They communicate with the team and manager via phone, email, chat, and online collaboration tools.
2. Hybrid work: The employee works both remotely and at the office. Depending on their schedule or individual preferences, they may be at the office for part of the week or only from time to time.

Remote work has become widespread in recent years due to the SARS-CoV-2 virus pandemic, which has forced major changes in the approach to employees’ duties. According to Forbes magazine, as of 2023, 12.7% of full-time employees work from home, and 28.2% in a hybrid model, which goes to prove the standardization of remote work environments.

What is the difference between remote work and working from home?

The terms “remote work” and “working from home” are often used as synonyms, but actually there is a slight difference between them.

• Remote work refers to work that can be performed from anywhere outside the traditional workplace (the office). Apart from home, it can be carried out from many different locations, such as cafes, coworking spaces, hotels, libraries etc.
• Working from home refers only to situations when work is performed at the employee’s home. This type of remote work is limited only to the home environment.

In practice, however, the above terms are often used interchangeably, but it is worthwhile understanding these subtle differences.

Why do people work remotely?

The decision to work remotely may depend on individual preferences, professional needs, or one’s personal situation. Specialists deciding to work remotely usually take into account aspects such as:

• Time flexibility: Working remotely often gives employees more control over their working time. They can adjust the schedule to their preferences, which can be particularly beneficial for parents, students, people with various commitments, or employees who prefer specific working hours.
• Location challenges: Working remotely eliminates the need to commute to the office, allowing employees to save time and money on travel, parking, and lunchtimes in the city. This argument is decisive for people who live far away from the city or region where their office is located, or even in another country altogether.
• The need for focus and productivity: Some people believe that working remotely helps them to focus and be more productive, since they avoid distractions related to office noise and contact with other employees.
• Unusual situations: Remote work can be a useful alternative in situations when employees cannot visit the office (e.g. when feeling unwell, during business trips, or in case of an emergency).

Read also: Remote team management in Scrum

Remote teamwork – the challenges of remote working

Although remote work brings with it several opportunities and can be extremely profitable and satisfying, it is worth bearing in mind it is not for everyone. It calls for self-discipline, time management skills, and the ability to work regardless of the conditions.

Apart from the advantages of this form of work, employees encounter challenges such as:

• Time management: Self-discipline and time management skills are the key to remote work. Remote workers often have to organize their work themselves, which might be difficult for some people.
• Communication: Effective communication is the key to successful remote work. Remote teams often use online tools like email, instant messaging, video conferencing, and collaboration platforms. However, the lack of face to face contact can result in vague messages, misunderstandings, and a lack of context. It is important to be clear and specific in your messages and keep your team notified of any important changes.
• Cultural and time differences: If team members work on different continents or in different time zones, difficulties with work schedules and cultural differences may arise. It is worth being aware of those differences and planning collaboration carefully.
• Social isolation: Working remotely can lead to social isolation because employees do not have the opportunity to interact with colleagues on a daily basis. This can negatively affect motivation and well-being. It’s important to interact regularly through team chats and online meetings, and take advantage of other forms of communication.

Despite the abovementioned challenges, many people eagerly opt for this solution. How do they deal with them and what is worth remembering?

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Remote working best practices

Due to the ever-increasing popularity of remote work, we present a list of 12 best practices that help employees and employers alike achieve satisfactory results:

1. A stable Internet connection: Nothing is as demotivating as spending ages waiting for a file to load or opening a work tool. Ensuring a fast and stable internet connection is crucial. This is the foundation of effective remote work.
2. Organization of the workplace: Set up a separate space at home, which will become your office. This helps to separate work from your private life and increase productivity. This approach has been proven to positively affect well-being.
3. Day planning, self-discipline: Working remotely calls for self-discipline. Establish a work routine, define the hours in which you are available, and stick to them. Creating a daily plan or to-do list helps you organize work and focus on priorities. This is especially important if there is no manager supervising your progress.
4. Regular breaks: It is worth planning regular but short breaks to take a breath and relax. Spending too long in front of the screen can have a detrimental effect on your health. Give both your eyes and your mind a break.
5. Communication: Keep in touch with your team using communication tools. Good contact and clear communication are crucial to the development of the project and the people working on it.
6. Remote work tools: Use online collaboration tools like Google Workspace, Microsoft Teams, Slack, or Trello. There are a number of them to choose from! They facilitate project management and make communication in a team easier.
7. Information security: Cybersecurity has also become a major concern for companies. 73% of managers perceive remote workers as an element of risk to the company’s security. To minimize the risk of attacks, keep company data and information safe by following the security policies of your organization. Remember to run your VPN every time you log in to your computer.
8. Flexibility: Be flexible with scheduling changes and bear in mind the needs of other team members.
9. Physical activities and overall well-being: Take care of your physical and mental health. It is worth exercising, going out regularly and maintaining a healthy work-life balance. Working remotely/from home may translate to reduced activity. Physical effort before or after work will not only help you stay healthy and in good shape, but will also improve your efficiency and ability to focus.
10. Training and skills development: While working remotely, make sure to develop your skills and gain new knowledge.
11. Feedback: Be open to feedback from managers and colleagues. This helps you grow and adapt to your team’s needs.
12. Troubleshooting: Understand the basics of fixing technical issues so you don’t waste time on unnecessary downtime caused by breakdowns.

When it comes to best practices, with the increasing popularity of remote work, the demand for remote working tools or communication has also increased.

Applications supporting remote work

Currently, there is a full range of tools and applications available on the market that facilitate project management, communication with the team, work organization and many other aspects of remote work.

1. Communication and videoconferencing tools:
   • Zoom: A tool for organizing video conferences, video chats, and webinars.
   • Microsoft Teams: Provides real-time communication and collaboration features, including video conferencing.
2. Online collaboration tools:
   • Slack: Enables real-time communication in the form of chats and allows integration with other applications.
   • Trello: Helps you manage projects with a Kanban board.
   • Asana: A tool for managing projects and tasks.
   • Jira: Designed primarily for software development teams, it helps with task tracking and project management.
3. Document management tools:
   • Google Workspace (formerly G Suite): Provides access to documents, spreadsheets, and online collaboration tools.
   • Microsoft 365: Offers similar tools for creating and collaborating on documents.
4. Time and task management tools:
   • Remember The Milk: A to-do list management app.
   • Todoist: Helps organize tasks and projects. ·
   • Toggl: Used to keep track of working time on projects.
5. Work organization tools:
   • Notion: Allows you to create personalized workspaces for project organization and documentation.
   • Evernote: Allows you to create and store notes and manage information.
6. Project management tools:
   • Basecamp: Helps manage projects, communication, and tasks.
   • Monday.com: A project and task management tool that allows you to adjust your work to your team’s needs.
7. Cybersecurity tools:*The above tools will work for both employees and companies.
   • LastPass: Helps manage passwords and make online accounts more secure.
   • 1Password:: An alternative tool for managing passwords and credentials.
8. Work monitoring tools:
   • RescueTime: Helps track and analyze time spent on different apps and websites.
   • Time Doctor: Allows you to manage your working time and track your productivity.

The abovementioned tools are just a few examples of applications that support remote work. Choosing specific tools depends on the individual needs of the team and the nature of work.

Organizational culture fostering remote work

In fact, a key element of effective remote work is not only tools and giving employees an option to work from home, but also creating an open and flexible organizational culture.

According to the Deloitte report “The state of hybrid work in Poland”, more than 70% of organizations have implemented clear rules regarding remote / hybrid work. Wherever no such policies have been put in place, this is considered a “lack of ideas” for organizing work, leading to generally less positive employee experiences.

Employers who want to manage remote workers effectively need to understand that working remotely needs a different organizational culture than working in an office, in the context of communication, management, and building trust, supporting employees and allowing them to rest assured that their work is appreciated, regardless of where they work. Moreover, openness to change and adaptation to work trends give organizations a competitive advantage, and thereby attract qualified employees. All this is beneficial not only to people working remotely but also to the enterprise itself, allowing them to reduce costs and increase flexibility.

Effective remote work – summary

Effective remote work is a challenge, but also a real opportunity if you take the right steps and use best practices. It requires a combination of organizational skills, technology, and self-discipline. By working on these aspects, high performance and greater job satisfaction are within reach.