We discuss the concept of data democratization, which we mentioned briefly in “ Data Science Trends We Will Hear Frequently in 2021”.
What Is Data Democratization?
Data democratization generally defines the processes that enable almost everyone in the organization to access the relevant data, make more accurate decisions, or reach results by obtaining insights from the data. It is aimed that business users, regardless of their role in the company, have the competence to access and effectively interpret data using analytic tools considering their needs.
Our product and services are developed to enable all business users to analyse their data by connecting these data easily. We discuss the democratizing data process, which we support with our products, further in the article.
Why Is Data Democratization Important for Institutions?
Reading, analysing, interpreting, and obtaining information from data is defined as data-driven decision making, and business transformations are moving in this direction as we all know. This transformation process necessity needs for data to be accessible and to be interpreted much faster. Moreover, institutions require to evaluate their data for different purposes as much as possible. Data democratization began to emerge precisely in line with this need. With technical improvements to democratize data, business users can access and analyse the data they need, even if they do not have comprehensive technical knowledge. This approach also paved the way for a more transparent and accessible data policy.
When we consider the importance of data-driven decision-making for institutions, the ability to access and interpret data instantly, enables users to reach results quickly, supports a more agile corporate culture, and provides corporations with a competitive advantage. For example, predictive data analytics supports identifying business opportunities ahead of competitors.
Before the emergence of the data democratization concept, organizations entrusted their data to a few data analysts, who have the technical knowledge to extract value from data. All departments requested their reporting needs from data analysts and data were evaluated by a few people in the company. This process takes too much time, so the decision-making processes take a long time too. In addition, the fact that the decision-maker did not evaluate the relevant data directly was an obstacle to proper decision-making processes. With the emergence of technologies that make data shareable and interpretable for non-data analysts, the way for the democratization of data has actually been paved.
There are counter-arguments, such as only IT departments or particular individuals should manage the data. The reason for this concern is that making the data available to everyone may cause a security threat. In addition, misreading and misinterpretation of data may cause wrong business decisions. Nevertheless, efficient analytics and data security systems, and training processes relieve these concerns.
With an analysis system that provides real-time data flow support, any business user can access data instantly and enable them to make real-time decisions. The real-time decision-making ability is crucial in terms of converting an instantaneous customer movement into sales, preventing a crisis, or carrying out the field operations efficiently.
Where to Start?
Know Your Institution
First of all, companies should determine their data analytics objectives, and discover the right analytics system accordingly. Evaluations should not only be carried out throughout the company but also within each department. Especially each department should observe how the process progresses and detect the problems if any. Necessary enhancements should be completed, and necessary precautions should be taken by identifying if there are any security vulnerabilities.
More Understandable Data
The purpose of data democratization is to ensure the easy interpretation of the data by everyone, so the data should be organized understandably. Otherwise, misleading analyses will lead to wrong decisions. Therefore, before using the analysis system, the data must be cleaned and modelled.
Necessary Features of the Analysis System
First of all, the system should have the ability to access all data, manage data from a single interface and allow instant data flow. At the same time, it should have an authorization mechanism to ensure that the relevant data within the institution reaches only authorized persons. No more talking about the need for these systems to offer enriched data visualization options that will enable users to gather insights easily 😊.
Training Process
After establishing infrastructures that meet these requirements, the next step is the training process. Due to self-service analytics tools are the basis of data democratization, users should have the proficiency to use the systems. However, we mentioned that these user-friendly systems do not require extensive technical knowledge. Therefore, short-term training will be enough to ensure competence. The training content should cover the importance of the data itself and interpreting them.
Leverage the Power of Machine Learning
Artificial intelligence support, which provides smart automation solutions, allows analysis to offer much more results than inquired.
If you are an institution that would like to transform to get the maximum benefit from your data, you need to have a strong infrastructure and choose the most accurate analysis system. In this way, “democratic data” provide agility to your institution and enable you to gain a competitive advantage. You may contact us to learn how we support your data democratization transformation.