Data democratization refers to the process of making information and data available and accessible to a wider audience, rather than limiting access to a select group of individuals or departments within an organization. This concept seeks to empower more people within an organization by providing them with access to meaningful data and the ability to use it to make informed decisions.
In this way, the democratization of data is intended to remove the barriers to consuming information and get more and more people in the organization to consume the information generated and make informed decisions.
We have all heard or read a lot about the ‘democratization of data’ in recent years. Probably only a few, if any, can talk about it in a straightforward way. The concept is rather ambiguous and sounds rather theoretical.
At Keepler we have developed a method with the objective of analyzing in a simple and measurable way the degree of democratization of data that any company has, identifying areas for improvement and providing the logic to continue monitoring its evolution over time: the Data Democratization Index.
Data Democratization Index
In our view, having true ‘data democratization’ in any organization means that everyone knows what data they need for their specific use, they can access to that data and, once they have access, they can meet their needs autonomously, with little or no ‘technical’ help.
To define our Data Democratization Index (DDI) we start from this simple hypothesis, we divide our logic into what we consider to be 3 main pillars of a true “democratization of data”:
- Knowledge around data
- Access to data
- Use and consumption of data
Why is DDI useful?
Today many companies aspire to a true “democratization of data” throughout their organization. Probably the most common goal of all CDOs and data leaders along with ‘self-service’ analytics or ‘self-consumption’, which are more or less the same concepts, data democratization is much broader than these.
So, in order to improve any ‘data democratization’ capabilities and plans, you need to measure it, somehow.
This is where DDI comes in: to help companies measure a term as complicated and ambiguous as “data democratization.” Our DDI approach makes it simple and understandable for any profile within the organization, and provides the possibility to keep monitoring metrics and set next steps and goals.
Difference between DDI and Data Matutity Assessment
While the Data Maturity Formula (DMF) is designed to take a more holistic view of data management and maturity, the DDI only focuses on what is relevant in terms of data democratization (knowledge, access and consumption).
While DMF is considered very theoretical and helps to identify areas for improvement and to establish a roadmap to increase the degree of maturity, DDI is very practical and is calculated by some specific metrics on tool usage and system exploitation. DDI focuses more on what is available today and how it is exploited.
While the DMF is a deliverable meant more to help the CDO and give him/her a broad view on data maturity across the enterprise to establish an improvement plan, the DDI starts to generate a real impact on the way the enterprise uses/accesses/knows data, and means working together with the CIO or HR, as it is an issue that involves both a technical landscape and a cultural mindset.
Why analyze the degree of democratization with our method?
At Keepler, we develop our entire portfolio of services based on realistic approaches tailored to the reality of the organizations we work with. DDI is no exception, our formula analyzes the three basic pillars of data democratization: knowledge, access and consumption and we adapt it to the reality of each corner of our clients, giving a current score, defining a future objective and implementing a plan to achieve it giving benefits in the short, medium and long term.
Image: Unsplash | Alina Grubnyak
Data management associate at Keepler Data Tech, certified in DAMA (CDMP). I have developed my professional career in the field of Data Governance, Data Quality and Data Management in general.
"Squeezing all the business potential that data has is my main objective when we face a new project with my clients"
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