Meaningful data is of great value to organisations who are looking to accelerate their business, gain a competitive advantage, and bring down costs. But the only problem is that huge amounts are data is scattered everywhere. It is difficult for all the stakeholders with no technical background to comprehend this data and organisations require highly skilled data scientists and analysts.

Many organisations are gradually realising the need to take a collaborative approach towards data that can be understood by all levels in their organisation and this is what data democratisation is all about.

What is Data Democratisation?

Data Democratisation is the first step towards driving digital transformation in any organisation. This term refers to the ability for data in a digital format to be accessible and understood by a layman. It helps non-specialists gather and analyse data without any complexities. Data democratisation helps break down silos and provides access to data when and where it is needed. In recent years, the industry has been flooded with tools that help understand data more easily.

According to a prediction by Gartner, by 2017 most business users will be able to access self-service tools for data preparation and analytics and gain access to multi structured data. A recent research from MIT Sloan Management Review predicted that the democratisation of data is ever growing and 77% of respondents felt that this growth was a result of an increase in access to useful data since last year.

Some Areas of Concern Around Data Democratisation

While organisations are convinced about the need of data democratisation, there is still a debate around the increased use of data democratisation. Prominent among them are security risks associated with allowing unrestricted access to potentially sensitive data. Many organisations believe that there is a need for skilled professionals with the adequate knowledge to analyse the data without which, business leaders, when left to their own devices, will draw inaccurate conclusions from the data and, as a result, take incorrect decisions.

When you allow unrestricted access to data for larger groups, there are security concerns around maintaining the integrity of the data. In addition, fear about how people would use and interpret data is a big concern and does not allow widespread adoption of democratisation of data. Another potential hurdle is the availability of appropriate tools to analyze the data and easily extract meaning from the data.

Growth Drivers of Data Democratisation

In spite of these concerns, there are some tech solutions that have been growth drivers for data democratisation.

  • Data virtualisation software – This software makes it possible for an application to retrieve and transform data without knowing the technical details about it, such as its physical location. A good example of data virtualisation is that of a user uploading photos on any social networking site, such as Facebook. While uploading photos from a desktop or mobile, the user provides the uploading tool with the file path or location of the photos. But once the photos are uploaded, Facebook uses an abstraction layer that hides that technical information, so that a user that downloads the photos from Facebook does not need to know the location of the photos. This abstraction layer is the data virtualisation.
  • Data Federation – This helps organisations create virtual databases by aggregating data from varied sources so that it can be used for BI analysis. This virtual database does not contain any data but points to the location of the actual data.
  • Cloud storage – Cloud has brought about a revolution in the way data is stored and accessed at central locations on the cloud instead of siloed locations.
  • Self-service BI applications – These applications provide non-technical users with tools that make data analysis easier to understand.

According to a survey conducted by Economic Intelligence Unit on big data and democratisation, it was found that allowing more workers to access big data helped organisations quickly identify new business opportunities and make better and faster decisions. It also helped improve customer retention and improve the financial performance of the organisation.

Organisations need to weigh the benefits and concerns while enforcing democratisation of data. Data democratisation needs to have adequate policies in place to ensure that sensitive data is protected and not misused or misinterpreted. The users must undergo appropriate training so that they are trained to extract meaningful information out of the data and also understand expectations.

The democratisation of data is the future and game changer for organisations provided it is implemented in a right way. It will allow stakeholders derive powerful business information from the data and make their business more agile, competitive, and productive. After all, as we always say – all enterprises have to become data-driven enterprises.