More data is available to enterprises than ever before and advanced tools capable of analyzing it exist. So, it seems like a no brainer that every organization, regardless of size or sector, would be eager to capitalize on this important asset. However, challenges still remain. For instance, 69 percent of business leaders report their organizations are still not data-driven — despite the fact companies across the board are investing more toward analytics programs.
What issues are currently holding businesses back from succeeding in their efforts to become data-driven? First, there’s the matter of deploying the best tools. Then there’s the matter of getting workers to actually adopt them into workflows, something that requires understanding the changing relationship between the “average” worker and data.
The Past: Siloed Data & Static Reports
For many years, the typical approach to data supported by legacy analytics systems involved putting IT specialists in charge of siloed data sources. If a non-technical employee like a marketing manager had a question about customer behavior, they would generally have to request a report from this gatekeeping team — then wait as long as it took for the data specialists to pull the data, create the report and deliver it.
This model fueled high wait times due to reporting backlogs and the end result was often a static report offering little opportunity for drill-down or follow-up questions without repeating the process. Additionally, keeping different data sources siloed from one another could also mean recipients would get different answers depending on the dataset used.
The limitations with this model are apparent and can explain why data democratization — or eliminating gatekeepers and blockages by giving everyone across an organization direct access to data — became the next big thing in the world of analytics.
The Present: Accessible Data & Instant Insights
What are the potential benefits of democratizing data across an organization? InfoWorld cites author Bernard Marr in saying that, “The ability to instantly access and understand data will translate into faster decision-making, and that will translate into more agile teams.”
In other words, data-driven organizations are powered by constant data-driven decision making at every level. And, employees need direct access to data insights on demand if the hope is they’ll be able to get all the answers they need to make informed decisions that affect outcomes.
Analytics tools like ThoughtSpot’s relational search engine empower the average worker to ask questions much like they would on any search engine online. The result? Anyone from any team — with permission, of course — can engage in ad hoc analysis at any time. Rather than waiting days (or more) for reports to arrive, it becomes a matter of asking questions in the moment as they arise.
Another key difference between legacy analytics and today’s democratized approach is the interactivity of insights. Whereas static reports show a frozen snapshot in time, insights today are interactive in nature — allowing users to drill down and zoom out for a bird’s eye perspective as needed. The continual ability to ask questions and look at data from different angles drives more thorough decision making.
Removing silos and giving non-technical employees direct access to data analytics tools is an excellent start. But it’s also important to consider whether company culture is supporting the workforce’s changing relationship to data in a positive way or hindering it.
Forging a truly data-driven culture that encourages every employee to take advantage of the insights available to them requires the following:
- Strong centralized governance and cybersecurity protocols
- Treating data like a business asset rather than a byproduct
- Creating a “borderless ecosystem of data” with a single source of truth
- Providing the data literacy training employees need to feel comfortable
Provided the culture and tools are in place, the average worker is now empowered to pull their own interactive data insights they can then incorporate into decision-making. This can improve business outcomes across an entire organization.