Data is the fuel necessary for the survival of any organization. Being the most critical business asset, it becomes important for organisations who want to not just survive but thrive, to be able to unlock its full potential. Data democratisation does exactly that – liberates the data to be accessed and leveraged by all.
It is indeed a game-changer since it removes the dependence from IT for any and every data need which any other department may have. With the newly acquired access to data and its easy-to-understand analysis, teams within the organisation become more agile and the final result is – faster and informed decisions.
Modern-day technologies have made possible this erstwhile seemingly impossible task of making the data and its analysis easily accessible & understandable respectively, by all data consumers – not just a data scientist but even a business manager, analyst, or subject matter expert. It can be likened to converting everyone in the organisation into a citizen data scientist.
Despite the vast wealth of benefits of data democratisation, some resistance still persists owing to concerns like data hoarding instances, fear of incorrect interpretation, creation of localised silos or even low levels of data literacy in the organisation. All these issues can be bundled up under the maintenance of data integrity and governance. After all, data trust is the glue that holds its democratic setup in place.
Bypassing all these apprehensions, the number of organisations getting on board with this revolutionary trend outnumber those who are still sitting on the fence. It’s simply because the benefits outweigh the challenges by a massive margin. The benefits of this cloud-based approach go beyond the availability of quality data and insights to individuals and teams. It accelerates organisational growth.
Laying the foundation of data democratisation
While the end result of this exercise points in the direction of time-saving in terms of enabling faster decision-making, initially you’d need to get into the grind of budget allocation, software purchase and employee training planning to put this automatic engine in place.
A quick breakdown of the process
You’ll then begin with breaking down data silos with the help of customizable analytics tools and creating role-specific visualisations of the data.
Then will begin the most important step – training of individuals in order to actualise the concept of self-service analytics which is the heart and soul of data democratisation.
Lastly, comes the governance radar. This is again a critical step in order to prevent mismanagement or misinterpretation of data.
One more additional step is there which takes data democratisation to a completely new level and ensures that you’re ready for the future in the face of ongoing digital disruption. This step is about leveraging machine learning & artificial intelligence to continuously learn from human asks and keep on creating thousands of questions and their resultant analysis by itself.
These related sets of questions and analysis pave way for humans to dig deeper and the virtuous cycle continues. The more questions humans ask from the data, a greater number of branching questions and analysis keep on getting added, and so on and so forth.
The entire ballgame of data democratisation requires both, technical as well as perspective changes. Once the initial toil is managed, the data engine becomes a company-wide tool reaping benefits that could have not been comprehended earlier.
Best practices to make it work for you
Understanding Your Own Home-Game
With the organisational growth, the volume of data also keeps on increasing. It’s important to have a complete understanding of the internal data ecosystem in order to pass on the right information to the people who’d be working on to create the democratic data engine.
Let Go Off Tight Control
Give complete data access to the analysts since without a full spectrum of information, you risk getting incorrect conclusions from data. This applies not just to the present and continuously incoming data but also to the trapped legacy data.
Be thorough with the part where the information designers come into the picture. The visualisations which they draw from the data will ensure that non-critical info is sifted from the important one.
Train the employees in understanding not just the self-service analytics but also prioritising which analysis to focus upon and which to ignore. Failing to do the same, there are high chances of them getting overwhelmed & stuck in analysis-paralysis kind of a situation.
Data coupled with analytics is a disruptive force that can propel organisations in the future. With data democratisation, the whole idea is about making even the non-technical users discover and decipher data easily and thus, apply the insights for faster decision-making. In essence, it’s like a catapult throwing you upwards to touch new heights of performance by unlocking the true potential of data.