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The right data visualization for an efficient workflow

It’s possible to get a complete picture of current business situation using data visualization. This is especially useful when there are complex datasets and unrelated information. At the moment, there are many types of data visualization. A large number of data visualization options (arc, tagged, waterfall, violin, etc.) provide many ways to analyze data, share information, and discover new ideas. However, each information requires a certain way of visualization in order to effectively present data and meet information needs. For example,

Slope Chart

This chart shows the change between 2 points. It is effective when there are 2 time periods or comparison points and it is necessary to show an increase or decrease in different categories between 2 data points. This type of chart is suitable for visualizing changes in sales, costs, profits in order to obtain information about which indicators increased, which decreased, and how quickly this happened.

Calendar Heat Map

Heatmaps show the changes in a data set over specific periods (months, years). The data is superimposed on the calendar, relative values ​​are displayed in color over time. This option is suitable for visualizing quantity changes depending on the day of the week, how it changes over time (retail purchases, network activity, etc.).

Marimekko Chart

A diagram is used to show the relationship of parts to a whole. It compares groups and measures the influence of categories within each group. It is commonly used in finance, sales and marketing.

With Qlik, it’s possible to create any visualization that will be most effective for achieving a goal. Interactive charts, tables, and objects give an ability to explore and analyze data in depth, that helps to generate new ideas and make the right decisions.

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

Data Governance to improve data quality and security

The key to effective work and data analytics is data quality and security. Decisions quality  and actions efficiency directly depends on data quality used to make them. This, in turn, affects the efficiency of the business as a whole. Poor quality, incomplete and inaccurate data undermine the entire business chain and prevent from achieving the desired results. In this case, user doesn’t have a complete understanding of a current business state, makes wrong decisions and develops a strategy that will not only be ineffective, but may also lead to losses. If there is no trust in the data, nothing else matters, even with a good information system.

It’s possible to provide full control over data assets using Data Governance and Data Integration. These are processes that include tracking, maintaining and protecting data at every stage of the data life cycle.

Data Governance is processes, policies, and tools implementation to manage data security, quality, usability, and availability throughout its lifecycle.

All data management processes should be automated to prevent errors and inaccuracies that occur during manual processing. With automation, it is possible to implement rules and policies to manage data discovery and operational quality improvement. The managed data catalog allows documentation and control of each data asset, definition and control of each user rights. Through profiling, cataloging and access control, user gets the access they need to well-structured datasets and accurate information at the right time.

Data Integration is a platform that automates the entire data pipeline, from ingesting raw data to publishing analytics-ready datasets. Deduplication, standardization, filtering, validation etc provide clean data delivery. The platform includes a data catalog with robust content for data analysis and exploration.

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

Qlik vs Power BI

Let’s continue the comparison of leaders in BI and data integration. Below is a comparison of Qlik and Power BI 12 key features.

  1. Interactive dashboard
  1. Data visualization
  1. Deployment flexibility
  1. Total cost of ownership (TCO)
  1. Scalability
  1. Self-service
  1. Data integration
  1. AI-based analytics
  1. Advanced analytics
  1. Use cases
  1. Mobile business intelligence
  1. Information literacy support

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

Qlik vs Tableau

Qlik, Power BI and Tableau are leaders in BI and data integration according to Gartner report. Each tool has many benefits. However, in order to make the right choice, it is necessary to clearly understand business needs, its tasks and goals, as well as a potential value of BI introducing into different departments workflows, etc. By understanding the business needs and knowing the capabilities of each tool, it is easier to make the right choice.

Comparison of 12 key factors of Qlik and Tableau

  1. Data visualization – data visualization using interactive charts, graphs and maps. This allows to study data in detail in any direction, identify relationships, etc.
  1. Interactive dashboard – the ability to create dashboards for more convenient and free data study.
  1. Total cost of ownership (TCO) – accounting for all costs associated with BI solutions usage for 3-5 years (infrastructure, system configuration, application development, system administration and support).
  1. AI-driven analytics – new insights and connections discovering, quickly data analyzing, team productivity increasing, informed decisions based on data.
  1. Different use cases (on the same platform) – many use cases for BI, working with the same data and platform.
  1. Managed self-service – data and content control with centralized rule-based management and unlimited user power.
  1. Mobile business intelligence – the ability to explore and analyze data from any location.
  1. Scalability – complete and up-to-date presentation of data, processing it at any scale without affecting performance and increasing costs, data integrating and combining from different sources.
  1. Embedded analytics – the presence of full analytical capabilities in other processes, applications and portals in the company for effective decision-making by employees, partners, customers, suppliers etc.
  1. Data integration – combining and transforming raw data into data ready for analysis. Modern tools allow to make data available to the entire company using real-time integration technologies (data capture, streaming data pipeline).
  1. Flexible deployment – an independent multi-cloud architecture that will allow deployment in any environment.
  1. Data literacy – improving the information literacy of employees at all levels, the ability to work with data and make decisions based on them.

Efficient Data Management with Data Fabric

Modern companies often deal with large and complex data sets from different and possibly unrelated data sources (CRM, IoT, streaming data, marketing automation, finance, etc.). Large companies often have branches in different geographic locations. This can complicate the process of data using or storing (in the cloud, hybrid multicloud, on-premises, etc.). Data Fabric will help to combine data from different sources and repositories, transform and process it for further work. As a result, users get a holistic picture of the current situation, that allows them to explore and analyze data to conduct effective business activities.

Data Fabric is a data integration architecture using metadata assets to unify, integrate, and manage disparate data environments. The main task of Data Fabric is to structure the data environment, and it doesn’t require replacement of existing infrastructure. Metadata and data access are managed by adding an additional technology layer over the existing infrastructure. Standardizing, connecting, and automating of Data Fabric data management practices improves data security and availability, enables end-to-end integration of data pipelines and on-premises cloud, hybrid multicloud, and edge device platforms.

Benefits of Data Fabric using:

Data Fabric simplifies a distributed data environment where they it can be received, transformed, managed, stored. It also defines access for multiple repositories and use cases (BI tools, operational applications. This is made possible by continuous metadata analytics to build the web layer. It integrates data processing processes and many sources, types, and locations of data.

Differences Data Fabric from the standard data integration ecosystem:

The Data Fabric architecture depends on individual data needs and queries of business. However, there are 6 main levels:

  1. Data management (ensuring management and security processes);
  2. Receiving data (determining the relationship between structured and unstructured data);
  3. Data processing (only relevant data extraction);
  4. Data orchestration (data cleansing, transformation and integration);
  5. Data discovery (identifying new ways to integrate different data sources);
  6. Access to data (the ability of users to explore data using BI tools).

 When implementing Data Fabric, you need to consider:

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

Visual analytics – definition and benefits

Currently, one of the most promising and rapidly developing areas is visual analytics. Its advantage lies in the ability to work with large datasets, combining graphical visualization and powerful analytical calculations.

Visual analytics is the process of using sophisticated tools and methods to analyze data using visual data representations in the form of graphs, charts, and maps. This allows users identify patterns and insights that help them make better data-driven decisions.

Visual analytics is not just a graphic data representation and should not be confused with data visualization. State-of-the-art interactive visual analytics makes it easy to combine data from multiple sources and in-depth data analysis right in the visualization. The use of artificial intelligence and machine learning algorithms generate recommendations for a more detailed data study. The main task of this tool is to turn large data amounts into successful business ideas.

Visual analytics advantages:

Key recommendations for the qualitative use of visual analytics:

The large number of vendors that offer visualization features as part of the software makes it difficult to choose the right tool.

Modern data analysis tools include the following features:

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

More possibilities with Qlik AutoML

Machine learning applications have reached ubiquity (from solving health problems to choosing music or products). Business is also an active user of machine learning tools.

Today, companies, including, which are interested in expanding the capabilities of their teams and specialists who know how to work with data. For example, a BI engineer who participates in the analytics process could develop features, train, automatically select a reliable model and help deploy it without the involvement of data scientists and machine learning. Qlik AutoML helps companies maximize their data and analytics strategy.

Qlik AutoML is an automated machine learning platform designed for analysts to create models, forecasts, and conduct business scenario testing. A simple, no-code user interface makes it easy to work with data by identifying its key drivers, make predictions with a full data understanding, publish and integrate models into Qlik Sense dashboards for interactive analysis.

This tool identifies key drivers of historical data and builds machine learning models using the best algorithms. Qlik AutoML allows to:

Using Qlik AutoML:

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

What is ESG?

Nowadays, it is important to create a sustainable business in accordance with the rules of sustainability and sociality. Until 2030, the UN has identified 17 goals and 169 targets for sustainable development, which are a benchmark for modern business. Modern business strategy must include ESG goals.

ESG (environment, social, governance) – is a management principle that helps to achieve the involvement of the company in solving environmental, social and management problems. Such non-financial indicators allow to explore business activities through social and ethical relationships with the environment.

The approach to implementing sustainability principles may differ depending on the business area, its profitability and scale, the number of employees, etc. However, the basis and principles of ESG are common. Small and medium-sized businesses often postpone the transition to sustainable development, explaining this by the fact that they don’t have enough funds for implementation. Also, the non-obviousness and unpredictability of the transition effect stops the leadership on the transition path. However, experts argue that sustainable development will bring benefits.

ESG – key factors that determine the level of sustainability and ethical impact of an investment in a company. Investors use ESG standards to monitor the company for: nature conservation (environmental), management of relationships with employees, partners, customers, society (social), remuneration, audit, internal control (corporate). There are financial institutions and funds that monitor ethically sound companies and check them before investing.

ESG includes

Environmental factors:

Social factors:

Corporate Governance:

ESG Benefits:

ESG data is data about the environmental, social and governance company performance. These include: the amount of water consumed, the amount of CO2 emissions, the number of employees, the gender ratio, etc.

Accurate, transparent and actionable data are essential to create and effectively monitor a sustainable development strategy. Many companies store their environmental impact information in spreadsheets. This complicates and extends the process of extracting reports and analysis. However, Qlik simplifies data analytics and connects the sources of sustainable development into a single dashboard. This makes impact as easy to measure as sales, profits, and other KPIs.

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

What is Self-Service BI?

Development is taking place in all areas. So, the rules and methods of doing business have changed a lot. Business users must quickly access the data they need. This will allow them to keep up with the dynamically changing business environment and maintain their position in the market.

Some time ago, in order to get insights from data, users had to contact IT departments. Such a process was lengthened by waiting, request processing, clarifications, etc. Such a long process made received information irrelevant. On the other hand, many people didn’t use data in the decision-making process, because they didn’t have special knowledge and skills to work with them. Choosing intuition as the main argument, you can make mistakes, make an inefficient decision. Nowadays, this is an unacceptable luxury for every company.

Now it is important to be a company with the status of information literate and data-driven. Optimize data access with easy-to-use analytics applications. It is also important to introduce a staff training process to improve their information literacy.

Self-Service BI is the ability for business users to independently explore data, draw conclusions, create dashboards and reports without the participation of IT specialists, business analysts and data scientists. Self-service business intelligence involves giving employees access to information, which will help them make informed decisions, regardless of their analytics skills.

Benefits of Self-Service BI:

Recommendations for the successful Self-Service BI implementation:

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

The main approaches to data integration

Data integration is the process of data combining from multiple sources to provide complete, accurate, and up-to-date information for business intelligence, data analysis, and other business processes. The data integration process involves replicating, ingesting, and transforming data to combine different data types into standardized formats. Such data is stored in the target repository: data warehouse, data lake.

There are 5 approaches to data integration: ETL, ELT, streaming, application integration (API), data virtualization. Implementation of these processes can occur through manual architecture coding using SQL, or customization and management of data integration tools. The second method greatly simplifies development and automates the system.

  1. ETL is a traditional data pipeline that transforms disparate data. The transformation process takes place in 3 stages: extraction, transformation and loading. The data is converted in the staging area before being uploaded to the target repository. This facilitates fast and accurate data analysis and is suitable for small datasets;
  2. ELT is a more modern pipeline where data is loaded immediately and transformed in the target system (cloud data lake, data warehouse). This approach is appropriate for large datasets where timeliness is important;
  3. Data streaming – this approach allows to continuously move data from the source to the target in real time. Modern integration platforms provide the ability to deliver analytical data to streaming and cloud platforms, data warehouses and data lakes;
  4. Application Integration (API) – provides the ability for separate applications to work together, move and synchronize data between them. A common use case is to support operational needs (for example, providing the same information to HR and finance departments). Application integration should ensure consistency across datasets. SaaS application automation tools help to create and maintain own API integrations;
  5. Data virtualization – delivers data in real time at the request of a user or application. It is possible to create a single data view, make it available on demand through the virtual aggregation of data from different systems. Virtualization is suitable for transactional systems that are built for high-performance queries.

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows to find a solution in any situation. You can get additional information by filling out the form at the link

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