Data democratization is a complex concept. The concept, in any organization, rests on four major pillars: data, tools, training, and people.
Data democratization allows end-users to assess data in a digital format without requiring help (typically from IT). The culture of a company and how its employees think are driven by people who are quite passionate about data.
Today, Data democratization can be a game-changer because it makes it easier, faster, and simpler for employees to access the insights they require. Data democratization safeguards the company from becoming a top-down organization where the highest-paid person’s opinion wins. Users are given more ownership and greater responsibility with data democratization and need no longer be driven by hunches or assumptions. Let us see how this happens.
Pillars of data democratization
1. Data
A considerable percentage of data exists in silos and is spread across the enterprise. It could be stored in flat files accessed by Microsoft SQL Server; it could be saved in folders on an employee’s hard drives, or it could be stored at (and shared by) partner companies. As you’d expect, this is not conducive to viewing the “big picture.” Enterprises have created cloud-based data warehouses to tear down the silos. For data analytics, warehouses serve as a solitary, consolidated source of truth.
2. Tools and training for data democratization
Data democratization can be empowering to users, but only if the data is properly used. To make sure data democratization doesn’t lead to misinterpreting data. After training (typically by IT), users may reinforce that training by creating or joining mailing lists or chat rooms; they may even ensure that beginners and experts physically sit next to each other.
As companies identify which business users need to explore the data more deeply and freely on their own, they must also understand the different levels of user needs when it comes to data. Instead of limiting the analytics by offering just summarized or just raw data to all users, a multi-tiered approach is essential to provide the right depth of data to a user’s analytical skills and needs.
A first tier might provide only dashboards and static reports, and the second tier might add interactive, dynamic dashboards where the users can drill down to additional insights.
The third tier could include guided analysis that a senior analyst prepares for an individual user or a group of business users to work in a safe and rich environment in which technical users can follow the analysis process through annotations and explanations.
The fourth and final tier provides access to a visual data discovery tool so business users can visually explore a broad set of data (perhaps through a simple, familiar tool such as Excel) instead of using less intuitive means such as data tables and SQL queries. An enterprise will need to ensure that the more data a user can access, the greater their understanding of that data must be to avoid data misuse or misunderstanding.
3. People
Expertise in data analytics is strongly associated with open, persistent, positive, and inquisitive people. Enterprises must ensure that such enquiring minds are regularly challenged and involved. Employees need to be motivated and engaged to think, play with data, and ask questions. Engage them with regular seminars on key concepts, tools, and modern technologies.
4. Challenges faced while implementing data democratization
The main challenge enterprises face in their move to data democratization is that data teams are struggling to keep up with the rising hunger for data throughout the organization. More data demands a more complex analysis. For many organizations, moving to data democratization may require more resources than they have.
This problem can be addressed by self-service analytics. It enables everyone in the company to become a data analyst. In many cases, users need only a dashboard that provides real-time data; others need that data available in analysis tools. Putting the technology in place is not just enough to make it work. The training that is essential for the staff must be offered to bring real value. Thus, data democratization is made highly efficient by self-service analytics.
Conclusion
To enhance data democratization in your enterprise, you must keep in mind that this is a slow process in which small wins are brought via incremental transformations in a culture that drives the next culture change. Today, more organizations are trying to provide access to data to all their staff via data democratization and this, in turn, is helping them enhance the job performance and overall health of the organization.
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