Should You Say ‘Yes’ or ‘No’ to Data Democratization?
The current industries are becoming more data-centric with every passing day. The scenario is such that most organizations are sitting on a gold mine of data but are unable to utilize and optimize it. There are organizations in each industry that collect data comprehensively, analyze, and derive insights from it. Such data is used to drive decision making and strategy for financial growth, increased efficiency, profitability and overall growth of the organization. However, within each organization there are various departments and group of users who have access to different type of data as owners, integrators or as consumers. And the siloed structure of data makes the process of data utilization ineffective. The purpose of sharing the data owned or accessed by multiple functions in an organization is a cultural change at the organizational level, and to the existing processes in businesses. An organizational setup that allows efficient sharing of data with the bigger team to ensure the optimization of data usability is called as the “data – driven culture”. The organizations that are on the path of data-driven culture would need to decentralize the data systems, with respective roles, and teams holding the ownership on sharing of the data, vs a centralized data management team. This will be required to be implemented ensuring democratization of data with appropriate policies, procedures, and security applied. Data Democratization can create an ecosystem of shared data where multiple organizations and entities can collaborate to bring great benefits in the economic, socio-economic and welfare domains. It can be in form of providing good governance, well-structured health care, national and personal security, agriculture and farming growth, and many others. All and many of these will lead to some great socio-economic transformations to poorest societies through richer nations, equally, and unquestionably. The data collected and curated can be utilized for valuable insights, which is in accordance to the objective and usability of different groups and departments. Post data valuation by the teams, the data could be shared, and used in a unique way. The sharing of data at initial stages or later stages gives the power to the organizations or teams to view the data closely but from different viewpoints, thus bringing greater benefits. In the political-administrative world the concept of data democracy is known as “open government data”, and inside the organizations it is coined as “open data”. Data being the “oil” the benefits should be shared freely with all types of users in an understandable format. This data could be further refined or consumed for appropriate data – driven decisions. However, the challenges lie in providing benefits for social good, without breaking the privacy or political – legal rules of a group, society, organization or governments. In a nut shell “data democracy” is all about “free access to data, all types of data, for the right type of users.”. The process of data democracy starts with nurturing a data driven culture in an organization where the principle would be “data for everyone, acquire, process, leverage the value, and share structured, and reusable data legally” for multiple benefits. Data Democratization doesn’t happen in isolation with a framework built, data lake implemented, and a group of users or line of businesses making use of it. It is a process and has to be embedded and called out into the regular Big Data Development Life Cycle. It involves people, process, and technology to arrive at the innovative, valuable business decisions from the insights gained. To bring shape to this entire lifecycle, practices like DataOps could catalyze the ability of democratizing, which mostly focuses in streamlining the goals of business and bringing in more collaboration across teams. All this is monitored and mentored by CDO in an organization, and this would be the goal of the Data Driven cultured organization. The enterprise data lake is the core and future of the Modern Data warehouse architecture which is complemented by the components of metadata management, master data management, data governance, and security across the layers Data Lake allows data to be stored in the native form and therefore broadens the horizon of usage and increase flexibility and adaptability as per the requirement. Therefore, Data lake as a technology or platform, vs. Data Warehousing helps in implementing data democracy more efficiently and effectively. The idea of data democratization promises many benefits, the challenges with Data Democratization, however, lie in sharing the data without breaking the legal and privacy policies of an individual or organization. The way to go about it is to draw the front lines, agree all the way from individuals through the Governments adhering to the well laid broad policies, and make these implementable in every “digitalized /digitized” data that will benefit the social – economical – political landscape. The future is “democracy” and “data democracy” is one of the paths for “better living, healthy living, and prosperous living” in this digitally driven world.
The current industries are becoming more data-centric with every passing day. The scenario is such that most organizations are sitting on a gold mine of data but are unable to utilize and optimize it. There are organizations in each industry that collect data comprehensively, analyze, and derive insights from it. Such data is used to […]
The current industries are becoming more data-centric with every passing day. The scenario is such that most organizations are sitting on a gold mine of
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["big data", "Go", "AI", "ML", "Git", "RAG", "data governance", "R", "data lake", "data warehouse"]
["AI", "ML", "RAG", "R", "Go", "Git", "big data", "data warehouse", "data lake", "data governance"]
Shyam Viking is a technology enthusiast and a hard-core data professional. Currently exploring Data Architecture for complex applications in an enterprise landscape involving Digital Transformation, and Big Data implementations.
In the last 17 years, he has worn multiple hats of Web Programmer, Database Programmer, Database Administrator, Technology Specialist and Data Modeler.