Data Warehouse vs Data Lake vs Data Mesh 

Initially (in the late 1980’s), the destination of data was a data warehouse. It was powered by ETL processes, which means that data was extracted from the source systems, transformed into the target form, and loaded into the target destination. What has been constantly changing over the past years is the target form of data to be analyzed.   

What is Data Lake 

Definition and purpose 

The Data Lake approach is a centralized repository that allows companies to store all structured and unstructured data at any scale. Unlike traditional databases that store data in tables and columns, Data Lake stores data in its raw form until it is needed for analysis. This means you can store everything from raw logs, photos, videos, to processed datasets in Data Lake. 

Key characteristics 

1. Scalability: Data Lake is highly scalable and allows you to store huge amounts of data from different sources. Using cloud solutions, it offers unlimited storage space for your data.  
2. Flexibility: As data is stored in raw format, Data Lake offers flexibility by allowing data scientists and data analysts to process data in different ways depending on their needs. 
3. Cost-effectiveness: Formats such as Parquet organizes data by columns rather than rows, which is very efficient for analytical queries that only access a subset of columns. This can lead to a significant reduction in storage requirements and costs.  
4. Data Variety: Data Lake can handle structured, semi-structured, and unstructured data, making it ideal for organizations that use a wide variety of data sources.  

Challenges in data governance in distributed data structure 

Data Lake architecture is not without the challenges. Lack of structure can lead to the so-called data swamp, in which data management and their effective use becomes difficult. Without proper management, data quality can worsen, making it difficult to find the data you need for analysis and treat it as trustworthy. 

What is Data Mesh?  

Definition and purpose 

Data Mesh is a relatively new concept that decentralizes data management by treating data as a product and assigning ownership to individual business domains. In the Data Mesh architecture, each domain – e.g. sales, marketing or finance – manages its own flows and data sets. This approach allows for more flexible and autonomous data management. 

Key characteristics 

1. Domain-oriented design: Data Mesh transfers data ownership to the domain teams that are closest to that data, making it easier to ensure its high quality.  
2. Scalability: Data Mesh provides scalability through decentralization. Each domain can be scaled independently, reducing bottlenecks and improving work efficiency on data. 
3. Data as a product: This principle emphasizes that data should be treated as a product, and dedicated teams care about its quality, availability and usability. 
4. Interoperability: Data Mesh promotes interoperability between different domains, ensuring that data can be easily shared and used across the organization. 

Challenges in Data Mesh strategy 

The implementation of the Data Mesh architecture requires significant cultural and organizational changes. This requires domain teams to have the necessary skills and resources to manage their own data, which can be challenging in organizations, in 

 which data management has traditionally been centralized. Moreover, a decentralized nature can sometimes lead to inconsistencies without proper management. 

Data Lake vs Data Mesh – key differences

Centralization vs decentralization 

The most significant difference between Data Lake and Data Mesh lies in the approach of these architectures to data management. Data Lake is a centralized repository. This means that all data is stored in one place and managed by a central team. Such centralization can facilitate the execution of standards and ensure consistency across the organization. 

Data Mesh, on the other hand, is about decentralization. Each domain in the organization is responsible for its own data, i.e. data management and ownership are distributed among different teams. This can lead to greater flexibility and specialization in the field, but requires robust management to avoid fragmentation. 

Data owners 

In the Data Lake architecture, the central IT or data team usually owns and manages the data. This can sometimes result in so-called bottlenecks, because the central team may not have the domain-specific knowledge needed to effectively manage data. 

On the other hand, Data Mesh assigns data ownership to domain teams. This not only empowers those who are closest to the data, but also ensures that the data is managed with the specific needs of the domain in mind. However, this requires domain teams to have the necessary skills and resources for data management. 

Data Management in Data Mesh and Data Lake 

Data management is a key factor in any data architecture. In Data Lake, management is often easier to enforce because all data is centralized. However, this can lead to rigid structures that may not meet the needs of all users. 

Data Mesh requires a management approach where each domain is responsible for managing its own data. This can lead to more flexible and domain-specific management policies, but it also calls for a strong, overarching management framework to ensure consistency and interoperability across the organization. 

Scalability 

Both Data Lake and Data Mesh offer scalability, but they do so in different ways. Data Lake is scaling by adding more space and computing power to a centralized repository. This can be cost-effective, but it can also impact performance negatively as the system develops.  

Scaling in Data Mesh is done by distributing data management across different domains. Each domain can be scaled independently, reducing the risk of bottlenecks and enabling more flexible and responsive data management. However, in this approach management can be more complicated because it requires coordination between various domains. 

Flexibility and agility 

Flexibility is another area in which these architectures differ significantly. Data Lake provides flexibility in terms of the types of data it can store and way of processing. However, its centralized nature can sometimes limit flexibility, as changes or new requirements may involve the need for a central team to process them. 

Data Mesh is inherently more flexible because each domain can manage its own data as needed. This can have a positive impact on innovation and enable the adaptation of data management to business needs. However, this flexibility comes at the expense of increased complexity and the need for robust coordination. 

Read also:

• Data-Driven Decision Making
• What is Data Quality?

Data Mesh integration vs. Data Lake Best Practices

Data Integration in Data Lake architecture 

In Data Lake, data integration typically involves centralizing data from different sources into a single repository. This process can be complex, especially for data from different systems that use different formats, schemas, and protocols. Best practices for data integration in Data Lake include: 

• ETL/ELT Processes:  Extracting, Transforming, Loading (ETL) or Extracting, Loading, Transforming (ELT) are critical in cleaning, transforming, and organizing the data being transferred to Data Lake. Properly designed ETL flows allow for usability and consistency of data. 
• Schema-on-Read: Unlike traditional databases, Data Lake often uses the “Schema-on-Read” approach, which means that data is stored in raw form and transformed into the required schema during a read or query. This provides flexibility, efficiency and usability, but involves the need for thorough design. 
• Data Catalog: Implementing a data catalog is essential to managing the massive volumes of data in Data Lake. Data Catalog  helps users to discover, understand, and trust the data being shared for analysis. 
• Data management: To maintain data quality, security, and compliance in a centralized environment, a robust governance framework is essential. This includes metadata management, access control, and audit mechanisms. 

Data Integration in Data Mesh architecture 

Data integration in the Data Mesh architecture is more decentralized and domain-based. Each business domain is responsible for integrating its own data to create standalone, interoperable data products.  
Best practices for data integration in Data Mesh include: 

• Domain-Driven Design: Data integration processes should be tailored to the specific needs of each domain. This includes designing data flows that are consistent with the workflows and data models within a given domain. 
• APIs and Interfaces: To ensure interoperability between domains, data products should be made available through well-defined APIs or data interfaces. This makes it easier to share data and integrate it across organization. 
• Federated Governance: While domains are responsible for their own data, this governance model ensures that integration standards, data quality, and compliance requirements are met across all domains. This often involves the need for a central management team to work closely with domain teams to define and enforce standards. 
• Event-Driven Architecture: Data Mesh often uses the Event-Driven architecture, where changes in one domain’s data can cause updates in another. This keeps your data in sync and up-to-date across the organization. 

What is the difference between Data Mesh vs Data Lake data products?

What is a data product 

A data product is a resource created from data that brings value to users, usually by providing practical insights, enabling decision making or automating processes. Unlike raw data, which is often unstructured and not immediately useful, data products are designed to be used directly by end users or applications. 

Data Products in Data Lake 

In a Data Lake architecture, data products are typically created and maintained by a centralized data team. These products often come from raw data stored in Data Lake, which is transformed and processed to meet the needs of different business units. The responsible team manages the entire lifecycle of these data products centrally, from their taking them for processing, storage, and eventual retrieval by users. 

While this centralized approach can ensure consistency and uniformity of data across the organization, it can also lead to delays in data delivery. A centralized team may lack the domain expertise required to create highly specialized data products, leading to generic, non-specialized solutions that may not fully meet the needs of specific business units. 

Data Products in Data Mesh 

Data Mesh treats data as a product from the very beginning. Each domain is responsible for creating and maintaining its own data products. These products are designed with specific users and use cases in mind, making them more useful for users and the business unit that created them. 

This decentralized approach allows for more flexibility and innovation as domain teams can quickly change their data products to meet changing business needs. However, it also requires robust governance and cross-domain collaboration to ensure the interoperability of data products and enable the overall needs of the organization to be met. 

The key difference lies in the approach to design and ownership. In Data Lake, data products are often reactive and standardized, created after data collection. In Data Mesh, data products are proactive and specialized, designed as part of the data lifecycle in the domain. 

When to choose Data Lake

Data Lake is a good choice for organizations that: 

• Need a centralized repository of all their data, especially in the case of big amounts of various data, the so-called “single source of truth”. 
• Use centralized teams of data scientists or analysts who need access to raw data for exploratory data analysis and the use of Machine Learning. 
• Prefer a more traditional, centralized approach to data management. 
• Have an experienced data management team that is able to handle the complexity of Data Lake without falling into the “data swamp” trap. 

Also read:

• Data Mesh vs Data Fabric
• 5 most popular data analytics tools 

When to choose Data Mesh

The Data Mesh architecture may be more appropriate for organizations that: 

• Have a complex organizational structure with multiple domains or business units that require autonomy in data management. 
• Strive to enable domain teams to take responsibility for their data, adapting their management to business needs. 
• Are looking for more flexibility and responsiveness in their data management practices. 
• Have the right resources and organizational culture to handle the transition to decentralized data management. 
• Need for high quality and specialized data products. 

Elevate Your Data Strategy Our customized Data solutions align with your business objectives. Consult with Marek Czachorowski, Head of Data and AI Solutions, for expert guidance. Schedule a meeting

Summary 

Data Lake is a traditional approach that allows for storing data in a centralized location. It is characterized by scalability but comes with certain challenges in data governance. Data Mesh is a decentralized approach in which data owners from various data domains are responsible for specific data, and its quality, making it available for data consumers from other domains.  

The choice between Data Lake and Data Mesh depends mostly on the structure of the organization, its needs, and its readiness for change. Data Lake offers a centralized, flexible, and cost-effective solution for storing large amounts of various data. However,  good data management is required. Data Mesh, although more complex and requiring significant changes in the culture of the organization, offers a decentralized approach that better adapts data management to business needs and allows for greater flexibility and scalability.  

Self-Service Business Intelligence (SSBI) is a modern approach that allows end-users to analyze data and generate reports without the need to involve IT specialists or data analysts. It is designed for business team members who do not have advanced technical skills but need access to data to make informed business decisions. Read our article to find out what makes it different from the traditional approach, how to implement SSBI and what are the possible software solutions for your business.

Traditional BI and self-service BI – what is the difference?

The traditional approach in reporting means that users order reports that the IT department creates. It’s engaging and quite time-consuming, especially if reports are needed right away.

Modern BI solutions allow you to gain insights from a variety of data sources, and access data for self-service analytics. Using self-service is a convenient way to ease the burden on business analyst departments. However – I advise against being over-enthusiastic. It’s worth bearing in mind that a self-service BI tool doesn’t do everything by itself. To prepare more advanced analyses and reports, you need to involve BI specialists or get training, for example, under the guidance of a solution provider. 

What is Self-Service BI?

Self-Service Business Intelligence is a set of tools and technologies that enable business users to independently collect, process, and analyze data. With SSBI, one can create their own reports, dashboards, and data visualizations, which speeds up the decision-making process. These tools are typically user-friendly and offer intuitive interfaces, minimizing the need for technical support. 

Also read:

• Data Quality Importance in Data Governance
• Data-Driven Decision Making
• Data Mesh vs Data Lake

Applications of Self-Service Business Intelligence

SSBI is used in many fields, such as:

• Marketing: Analyzing marketing campaigns, customer segmentation, monitoring ROI.
• Finance: Cost analysis, revenue forecasting, monitoring financial performance.
• Sales: Monitoring sales results, analyzing customer behaviors, predicting market trends.
• Operations: Optimizing processes, monitoring operational performance, analyzing supply chains.
• HR: Analyzing personal data, monitoring employment metrics, assessing employee performance.

Benefits of Self-Service BI

SSBI offers many benefits, including:

• Faster decision-making: Users have immediate access to the necessary data, allowing for quicker decisions.
• Cost reduction: Less involvement of IT teams in creating reports and analyzing data.
• Greater flexibility: Users can customize reports and analyses to meet their individual needs.
• Improved data quality: Users can continuously verify and correct data, which leads to higher-quality information.
• Increased innovation: Access to SSBI tools stimulates creativity and innovation in data analysis.

Risks of Self-Service Business Intelligence

Despite many benefits, SSBI also involves certain risks:

• Data security: Lack of proper security measures can lead to data leaks or unauthorized access.
• Data quality: Users may not have the necessary skills to correctly interpret data, leading to erroneous conclusions.
• Improper use of tools: Without proper training, users may use SSBI tools incorrectly.
• Information overload: Excessive data can lead to difficulties in analysis and interpretation.

Implementation of Self-Service Business Intelligence and BI best practices

The implementation process of SSBI requires a thoughtful approach, encompassing several key stages:

1. Data preparation: Identifying and preparing data that will be easy to analyze is crucial. Data must be accurate, consistent, and up-to-date. The best solution is a central data repository that users can access. Popular tools like Power BI can use this prepared data, facilitating analysis.
2. Selection of SSBI tools: Many SSBI tools are available on the market, such as Power BI, Tableau, or Qlik. It is important to choose a tool that best meets the organization’s needs and is easy for end-users to use.
3. User training: To effectively use SSBI tools, users need to be trained. Training should cover the basics of using tools, creating reports and dashboards, and data interpretation. This way, they will be able to prepare reports and analyses themselves without waiting for further support.
4. Monitoring and support: After implementing the SSBI tool, it is important to monitor its use and provide support to users. Regular data updates, training sessions, and technical support help maintain the high quality and efficiency of SSBI tools

Elevate Your Data Strategy

Our customized Data solutions align with your business objectives. Consult with Marek Czachorowski, Head of Data and AI Solutions, for expert guidance.

Schedule a meeting

Examples of Self-Service BI tools

Some traditional BI tools include complex BI software that requires specialized knowledge to operate, while self-service BI platforms are designed to be easy to use for non-technical users. These self-service BI tools provide a way for users to access and analyze business data without engaging BI specialists. Below we present the list of popular Self-Service tools for data analytics – get to know those BI tools capabilities in detail to choose the best self-service tool.

Tableau

Tableau is a self-service tool with a drag-and-drop function that enables users to easily create and manage visualizations and dashboards. The system is popular thanks to its simple interphase and intuitiveness.

Microsoft Power BI

Microsoft Power BI is integrated with the Azure cloud platform. This solution can be easily handled by anyone. Power BI comes with modern reports and dashboards made available on all devices. Data for reports is collected from various sources to create multidimensional data models, making it possible to conduct analyses in real time. 

Qlik 

Qlik analytics engine allows users to explore data and easily create sharable reports. Qlik Sense is a powerful platform that, thanks to AI and ML  capabilities, allows business users to use the full potential of the data.

Read more:

• Exploring the power of Qlik NPrinting
• Qlik + Talend – the future of data integration

The future of AI-powered BI and self-service BI

The future of SSBI looks promising, especially in the context of integration with Artificial Intelligence (AI) technologies. AI can significantly improve the functionality of SSBI tools by:

• Automation of analyses: AI can automatically analyze data and generate reports, further speeding up the decision-making process.
• Predictive analyses: AI can predict future trends and behaviors based on historical data, allowing for better planning and strategy.
• Personalization: AI can adapt SSBI tools to individual user needs, offering personalized ad hoc analysis and reports.
• Pattern recognition: AI can identify hidden patterns and correlations in data that are difficult for humans to detect.

Also read:

• What is Data Quality? Data Management Best Practices & More
• Data Mesh vs Data Fabric
• Data Warehouses management

BI strategy for enhanced analytics

Choosing the SSBI tool alone is not enough. To become a data-driven organization, you need a well-thought-out strategy. Which data will be analyzed? This is where the Master Data Management approach will help. Who will be responsible for the data project? Are you going to conduct it alone or in partnership with a Data Management services provider? 

Implementation of the self-service bi strategy should include the following steps: 

– Selecting a Chief Data Officer (CDO) to support the initiative.  
– Choosing a platform – if you are not sure which tool will meet your expectations, decide to work with a partner company. 
– Engaging users – representatives of different departments should support the implementation plan and be involved at every stage of the project. 
– Creating a Business Intelligence team. Typically, a BI team consists of:
small teams: analyst, developer, tester. Larger project teams: Release manager, administrator, team manager.  

 Summary

Self-Service Business Intelligence is a powerful tool that allows end-users to independently collect, process, and analyze data. While SSBI involves certain risks, the benefits of its application are significant. The future of SSBI, combined with AI technologies, seems even more promising, opening new possibilities in data analysis and business decision-making.

Elevate Your Data Strategy

Our customized Data solutions align with your business objectives. Consult with Marek Czachorowski, Head of Data and AI Solutions, for expert guidance.

Schedule a meeting

What is Qlik Sense Enterprise?

Qlik Sense is a modern data analytics and visualization platform that allows users to explore, analyze, and share data intuitively and interactively. The primary goal of Business Intelligence tools is to shift the focus from preparing reports (often requiring time-consuming exports from multiple systems, then combining and aggregating them, and finally preparing visualizations) to using this data for daily operations. This enables employees at all levels to make more accurate decisions based on current data rather than intuition.

Also read:

• Use data to make informed decisions. Understanding the Importance of Data-Driven Decision Making
• Self-service BI: What it is? Applications, Benefits, Risks, and Future with AI
• 5 of the most popular Business Intelligence tools

What is Qlik NPrinting?

In a standard Business Intelligence approach, users must log into the platform and open a specific dashboard to analyze the data contained therein.

Qlik NPrinting, however, enables automated reporting that goes beyond this – users do not need to check the dashboard themselves (though this is still an option for a broader context).

Understanding the basics of Qlik Sense NPrinting

Instead, users can receive a predefined report after each data reload or at a fixed time. This represents a further step in simplifying reporting and report distribution – from creating reports “manually” without BI tools, to a dashboard where data reloads cyclically but must be opened to view, to Qlik NPrinting, where the report is automatically distributed to a defined group of recipients, e.g., via email or the NewsStand portal. Qlik’s NewsStand platform serves to store up-to-date reports. This portal grants users the ability to preview and download reports and request automated updates.

Key features and functionalities of Qlik NPrinting

Qlik NPrinting is an add-on to Qlik Sense that allows the creation of a report template, which is then distributed to a defined group of recipients. This requires the following steps:

1. connecting to the application that will be the data source for the report,
2. preparing filters (if we want to send a report only from a specific region/data subset),
3. conditions (if we want the report to be sent only when a specific condition is met),
4. the report itself.

Elevate Your Data Strategy

Our customized Data solutions align with your business objectives. Consult with Marek Czachorowski, Head of Data and AI Solutions, for expert guidance.

Schedule a meeting

Formats for Qlik NPrinting reports

Qlik NPrinting enables report generation in several formats:

• Excel
• HTML
• PowerPoint
• Qlik Entity
• Word
• Pixel Perfect

After loading the data and setting the scheduler, the created template is then attached to an email or placed on the NewsStand platform.

Benefits of Qlik NPrinting

Multiple formats

Qlik NPrinting offers a wide range of report formats, allowing for easy integration with other business tools tailoring reports to the specific needs of recipients. For example, financial reports can be generated in Excel for further analysis and data manipulation, while presentation reports can be created in PDF format for readability and a professional appearance.

Providing the right reports to the right people

Qlik NPrinting offers extensive report customization capabilities. Users can tailor reports to specific needs using templates and scripts. Personalization allows for the creation of reports that precisely meet the recipients’ requirements, containing only the information relevant to them. For instance, managers can receive reports summarizing key performance indicators (KPIs), while analysts can receive more detailed reports with raw data for further analysis.

Great-looking reports with NPrinting reporting capabilities

Qlik NPrinting enables users to create visually attractive reports based on Qlik data. Such appealing visualizations can be created and distributed effectively in an automated manner.

Not only can a user define an email message using the built-in editor, which allows the use of HTML tags, and add variables directly from Qlik (such as email addresses or a list of recipients that is dynamically updated with each data model refresh), but also, for example, attach an entire chart from the dashboard (as in the Supplier Performance Reporting paragraph). Additionally, they can format the attachment suitably – e.g., an .xlsx file – by appropriately coloring the columns:

Or by attaching a sheet with data to the output and an additional pivot table that will contain a summary of the data:

Of course, there is nothing to prevent formatting the data to meet the company’s internal standards regarding report layout (adding an image with the company logo, appropriate font and size, colors, etc.).

Qlik NPrinting – sample Business Intelligence use cases

Now that we know the basic capabilities in terms of reporting and distribution, it’s time to move on to specific applications made possible by the NPrinting engine.

Example 1: Automating sales reporting

Let’s imagine that every week we need to create a sales breakdown for individual salespeople and send such a report to each salesperson. Nothing could be simpler. Using Qlik NPrinting, we connect to the sales dashboard and configure the report with an attachment in the form of an Excel/PDF/HTML file containing the data snapshot. Additionally, we can fully customize the message, attach any graphics (or a chart directly from the dashboard), and also pull the current list of salespeople directly from Qlik using a variable.

The report can also be customized – salespeople from a given region can receive only the data relevant to them. Thus, Qlik NPrinting will generate as many messages as there are defined regions, the same number of attachments, schedule reports, and send them accordingly. It is as simple as that. This way, we eliminate the need for the manual preparation of weekly summaries and distribution to a specific group of recipients.

Example 2: Automatic notification system

Another interesting use case for Qlik NPrinting is an automatic notification system. Let’s now imagine that we have a dashboard collecting data on the current state of production, refreshed every hour. If the employee responsible for production metrics had to check it every hour, it would be inefficient and bear the risk of error. This is another excellent example of the application of Qlik NPrinting, saving time by creating a notification that will be generated only if a predefined metric drops below a certain level. In our example, the user responsible for monitoring the metrics will get an automatic notification as soon as one of the metrics falls below the set threshold.

Such a solution optimizes the employee’s time and, most importantly, shortens the time between the event, reading data from the dashboard, and taking action. It also reduces the risk of missing an event, as we are notified each time it occurs (and the recipient group can be broader than one person).

Example 3: Supplier performance reporting

Qlik NPrinting can also be used to distribute content outside the organization. Let’s say that as a manufacturing company, we evaluate our suppliers quarterly in an automated manner (based on data on delivery timeliness, quality, packaging errors, etc.). Without Qlik NPrinting, it would be necessary to manually export this data from the dashboard or have an external IT system that would pull it from the ERP system, calculate it, and send it to recipients. However, using the power of this Qlik Sense add-on, it is possible to quickly create a report, define filters, and automatically inform suppliers of their results each quarter.

Qlik Sense and QlikView NPrinting alternatives

Qlik NPrinting is not the only tool that extends the functionality of Qlik. One of the major advantages of the Qlik Sense environment is its ability to allow the community to create extensions and add-ons, making it possible to find alternatives for every solution – and this is no exception.

Mail & Deploy

One of the alternative solutions that can compete with Qlik NPrinting is Mail & Deploy, which was created by members of the Qlik community and later commercialized. This add-on has similar options to the product endorsed by Qlik, allowing for the creation of reports in various formats, sending notifications, and managing them in an administrative panel.

Qlik Cloud

When it comes to Qlik Cloud, the equivalent of Qlik NPrinting is Qlik Reporting. This tool is the successor to NPrinting. In the case of migration from an on-premises version to the cloud, it is possible to import reports from NPrinting, adapted to function in a cloud environment.Also read: Qlik Talend

Summary

Qlik NPrinting is a powerful tool that brings significant benefits to companies through advanced report generation capabilities, process automation, and flexible distribution options. Thanks to integration with the QlikView and Qlik Sense platforms, Qlik NPrinting enables the creation of accurate, personalized reports that support decision-making processes and increase operational efficiency.

Andrzej Czechowski: At this point, all of the companies we work with, regardless of the industry, are gradually becoming analytical and IT companies. As Jeff Immelt, the CEO of General Electric, once said, we are waking up to a new reality and can no longer run a business without business analytics. So we spend more and more time analyzing data and trends, because it makes it much easier to make decisions. And because of that, these decisions become more accurate, and we can avoid a lot of mistakes, thanks to an analysis of the past, and the knowledge of various phenomena that can be observed this way. If you want to run a business, you have to know the numbers. Samsung’s strategy here might not be unique, but we most certainly would like to be a mature company and aware of these numbers, the ways in which we want to develop, competition, and internal and external conditions.

Companies are increasingly becoming aware of the potential which data has, but most still associate it with Excel spreadsheets. How do modern business analytics work for such a traditional approach to data?

Andrzej Czechowski: Here are some trends that I myself also try to reinforce and implement. First, we can see that visualization is becoming the language of analytics. Good visualization, meaning that it is consistent and facilitates the so-called drill down, that is, delving deep into an analytical area, is a very useful tool for contemporary analytics. On the other hand, there is also self-service BI that allows you to discern a lot from the data yourself, without the need to send endless inquiries to IT teams, and without the need to have a professional-level knowledge of databases. At the moment, analysts are on hand for almost anyone, which is as it should be. And the next thing which we can observe is the change in technology itself. This means that data can already be read in real-time, servers are ever stronger, and cloud solutions are available, so technologically advanced analytical solutions are much more widely available than ever before. All that shows that business analysis itself has changed greatly.

In terms of visualization, you use Qlik products – I have QlikView and Qlik Sense in mind here. What do you think is the greatest value of these tools?

Andrzej Czechowski: We use many tools, including self-service data integration. We integrate data earlier so that it is easily accessible in the visualization layer. Everything is managed in such a way that the data is always available and well-visualized. With visualization tools, the data can be read well and I have to admit that there is a lot of pleasure to be derived from using all these possibilities. This makes it very fast and easy to independently set a variety of different dimensions. They can of course also be set by default, e.g. through bookmarks, meaning the constant reports which we use, but the greatest value lies in the fact that you can select a given area and view it from various perspectives and explore it deeply, to understand this data better.

How do employees utilize these tools? Are the primary end users of these visualizations top management, or can every employee use these tools at their operational level?

Andrzej Czechowski: Here, the aspect of customized analysis is very important at every level of access. Data should be available, but not everything for everyone. A Product Manager has different needs than an Account Manager, and the needs of a division head or president are different still. The level of knowledge, the level of information that a person would like to have, varies in scope and in detail. As a result, we also have a variety of tools to meet these needs.

Is there a need for employees to ‘drill down’? Are these tools sufficient? Do they make suggestions for how to change things?

Andrzej Czechowski: Habits are an important aspect of it. And of course you have to change these habits if it can bring benefits for your organization. On the other hand, from experience, I know that you should not change visualization tools too often. The user is used to them, knows them well, and it is precisely the use of proven, well-known tools that produces results and improves efficiency. Of course, new visualizations are still emerging, and there are plenty of additional features we add, such as campaign analysis and marketing promotions, which also require good visualization, making it much easier to understand and plan. Yet there are some elements that we created some time ago, let’s say a year or two ago, which work all the time, look good and should not be changed. As I said, business is growing, new areas are emerging, and it’s there where we add new functionalities.

Which areas of business do you most benefit from using these tools in? Of course, we know that they should be used everywhere, but which areas are fastest to see discernible results?

Andrzej Czechowski: As you mentioned, all areas of the company can benefit by analyzing data and gaining information, but the biggest beneficiary is usually the sales department, as there they need to analyze their activities on a daily, mainly quantitative basis. The Product Manager will also see results quickly, for example, if they analyze the price from the point of view of how market trends have changed and add an analysis of market and marketing trends to their data, such as market research agency data. Another such area is Supply Chain Management, where you have to deliver the product optimally, so you also need the relevant reports. On the other hand, the managers of teams and divisions are an important group of beneficiaries, because they must constantly analyze and review key KPIs from their own point of view.

And the areas that still have some developing to do in this respect? Those that still don’t take advantage of these opportunities 100%?

Andrzej Czechowski: In general, analytics are still developing in all areas. On the other hand, the one in which it is particularly visible, in our company too, is the digital sphere and above all e-commerce. I see the potential for a truly huge revolution in this area, from an analytical standpoint as well. Because we have more and more data here. These areas are still learning how to operate. Opportunities and needs in these terms will certainly grow. Soft information is becoming increasingly important, meaning what the customer thinks, likes, dislikes, an analysis of what is happening on discussion forums or vlogs. It certainly opens up vast possibilities for text analysis and analytics in general.

What do you think are the most important technological trends in Business Intelligence?

Andrzej Czechowski: Mobility is definitely at the forefront. Let’s admit that most of Samsung’s tools, such as phones, are already small computers. The mobility of analytical solutions goes hand-in-hand with the development of the devices we use, which have ever-increasing amounts of computing power, virtually unlimited resources. The devices that we carry with us become the centers of management and it is certainly clear that there are almost no analytical solutions that would not be suitable for mobile devices. Analytics are becoming more and more popular. At the moment you can have a really good, really cheap application that can handle most analytical challenges.

On the other hand, more advanced tools are also emerging. Self-learning systems are becoming popular and offer even greater opportunities, such as reliable sales forecasts and anticipation of sales trends. Cognitive systems are generally a highly fashionable and media-driven topic, sparking the imagination, because we can already see that cars are slowly starting to drive themselves. Which raises the question: where is it heading? Will it be like “2001: A Space Odyssey” where ‘Hal’ takes over the business? One thing is certain, cognitive systems are developing, and decision-making is increasingly automated because it is certainly more reliable than any analyst. And of course, technology will take over more areas of our lives and business, so more and more data on these activities will be analyzed. We can see it today, for example in cloud services, and IoT. It’s already happening.

Could you tell us more about the Samsung Business Consulting service?

Andrzej Czechowski: At Samsung we have seen everything mentioned here, the strength of analytical solutions, and how they change the organization. And since Samsung cooperates very closely with B2B partners, the concept of supporting our partners from the analytical side arose, so as to share the experience we have gained with them. This is a “practitioners for practitioners” format. We have gained experience, we see the pros, cons, and we know which solutions work and how quickly they can be implemented in the organization. For us it is also a very big advantage because firstly, we are building a long-term business relationship with a partner, and secondly it is also a much simpler platform of communication. It’s highly convenient to work on the same set of numbers. We go to our partner and talk about e.g. sales data, trends in the market, and we see the same numbers because we work on the same data systems and our knowledge is convergent. Only 18% of larger companies in the Polish market have a mature BI system. So we see that the demand for such systems, as well as the knowledge of how to use them, is great – among our partners too. This is why the Samsung Business Consulting concept, which will support them not only through tools but also through analytical solutions services, was created.

You cooperate with JCommerce in the areas of Custom Development and BI. Could you tell us more about these joint projects?

Andrzej Czechowski: We have been cooperating with JCommerce for several years now – I think that this cooperation is really effective and I hope we can continue to cooperate. We always set ourselves specific requirements and goals which are achieved, and long may it continue.

OK, that’s all the questions I have. Thank you very much, it was great to hear your insights.

Andrzej Czechowski: Thank you very much.