• 2. 2. 2024
  • Ing. Jan Zedníček - Data Engineer & Controlling
  • 0

Before, it was a common practice that the management and access to corporate data for internal customers (employees from different departments) were exclusively under control of the IT department. If you wanted to request any report or data set, you had to contact IT. Then you waited for a few days until your turn came, and eventually, you received some data. Fortunately, this is no longer the case in many companies today, thanks to the process known as data democratization. This need arises from the requirement to manage the company (or teams) with a data-driven approach, which relies on empirical data rather than the subjective impression of a manager.

What is a Data-Driven Approach?

A data-driven approach, also known as data-driven decision-making, is a strategy or method of decision-making that relies on the collection, analysis, and interpretation of data to inform and guide decisions. Instead of basing decisions on intuition, assumptions, or personal experiences, the data-driven approach emphasizes the use of quantitative information and empirical evidence.

It doesn’t mean that we ignore the experience of managers (as it remains a key aspect of decision-making process). Using data driven approch we want to avoid decisions based on lack of information and context which can cost a company economic value.

Implementing this approach is not straightforward and involves

The implementation of a data-driven approach and data democratization enabling self-service access to data (without the need for IT intervention) is a gradual process that occurs on two levels.

  • Adoption at the level of thinking and company culture (embracing this way of management throughout the organizational structure).
  • Technical adoption (ensuring the technical infrastructure to make data available).

Data Democratization and Key Aspects of the Data-Driven Approach

Data democratization goes hand in hand with a properly implemented and conceptually grasped data-driven approach. Of collecting, storing and interpreting data. Permission to access (be able to see/access) underlying data is important aspect of data democratization approach and self- service business intelligence

  • Data Collection: Systematic gathering of relevant data from various sources, including internal systems and social media, is related to the proper configuration of internal corporate systems. If there are multiple corporate systems and IT is complex, it is advisable to consider a centralized data warehouse – data warehouse. Technical implementation is carried out by BI Developers/SQL Developers, and the architecture is designed by BI architects. The size of the team depends on the complexity of the project. The data warehouse is then open to new internal customers in the form of prepared datasets, reports, OLAP cubes, and employees/managers have free access to them (if they are not sensitive data). The important part is an implementation of data security principles.
  • Data Analysis: Using statistical tools, machine learning, and data analysis to interpret useful information and insights from the collected data. These activities are carried out by specialists – Data Analysts.
  • Visualization and Interpretation: Representing data in easily understandable formats, such as graphs, charts, and dashboards, that help interpret data and reveal trends, patterns, and correlations. Tools like Power BI or Excel are used for this purpose.
  • Informed Decision-Making: Using insights from data to support decisions, strategies, and initiatives within the organization – managed by managers in collaboration with data analysts/financial controllers.
  • Continuous Improvement: Using data to monitor the results of decisions and processes, allowing for quick iterations and improvements based on feedback from real-world results – a joint effort by all.

Data Democratization and ETL Pipelines

Technically speaking, the most complex part is the first one – Gathering data into a centralized repository. The process of collecting data from a data source, transforming it, and storing it in a central repository before reporting is called ETL (Extract, Transform, Load) processes. This part used to be the domain of programmers in the past. Today, thanks to data democratization, this part can be managed without the use of complex tools and programming languages (most commonly using Python).

Today, there are already cloud-based tools such as Fivetran or, for example, Czech Keboola, which allow you to automate data flows without the need for programming knowledge. These tools offer a wide range of pre-built connectors to data sources and can significantly speed up implementation. They are not tools for novices, but on the other hand, you don’t need to be a programmer to use them.

Another group of ETL tools (on-premises), which greatly simplify data integrations includes, for example, Kingswaysoft (an add-on for Integration Services) or the young generation of modern tools with excellent developer experience like Mage.ai – a whole category of guides here on the blog and others.

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Ing. Jan Zedníček - Data Engineer & Controlling

My name is Jan Zedníček and I have been working as a freelancer for many companies for more than 10 years. I used to work as a financial controller, analyst and manager at many different companies in field of banking and manufacturing. When I am not at work, I like playing volleyball, chess, doing a workout in the gym.

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