Data democratisation, along with online and open source tools, can bridge the gap between large and small companies, say experts.
We already have seen the tremendous growth of data velocity and volume of available data, which is estimated to cross 175 zettabytes by 2025 because of the connected devices, and the constant streaming of information that is stored, shared and generated across different processes in businesses and everyday users’ life.
The utilisation of data in an enterprise environment is based on operations that govern things like recommendation engines, visualisation tools that help in decision making. This is valuable to enhance creativity, efficiency and productivity of employees. In a data-driven organisation, the processes of each department, whether be it sales, marketing, or customer support, is driven by the veracity of data.
So, why should organisations care to have data democratisation? The answer lies in the fact that if they don’t they are bound to be left behind companies that leverage data to gain a competitive edge. Data democratisation is the capability of organisations to have access to data for all employees in a format which is usable for an average person. The objective of data democratisation is to have everyone, technical or non-technical personnel, to gain insights for better business outcomes and decision making.
Currently, this means that business intelligence is dependent not only on the hands of data scientists or IT but all kinds of business users in the enterprise. Salespeople require real-time data on their clients prior to the meeting; marketers need access to data streams to plan targeted campaigns on different social media platforms and channels. Similarly, inventory data and sales predictions can be of great value to the finance department in making the budget requirements for purchase orders.
Data Democratisation: The New Norm To Prepare For AI Adoption
The reason for an emphasis on data democratisation is because while most of the competitive businesses today are data-driven, several companies still lag in data democratisation where managers restrict individual employees to have access to actionable data fearing non-technical staff would have the needed abilities to interpret data. And so does the senior executives, data scientists and information technology (IT) people, who can access the data for business intelligence.
This, according to experts, is not going to work in the coming age driven by artificial intelligence, where each worker’s job role will be impacted by machine intelligence and advanced analytics. Data may be stored in silos, which makes it challenging for workers in various departments to access data. Also, it’s possible that the datasets require cleaning or changing the format before it could be utilised efficiently by everyone.
Data democratisation, along with online and open source tools, can bridge the gap between large and small companies. In fact, in some cases, smaller companies that are tech-savvy can have the edge over large legacy businesses which still have data silos. Plus, data democratisation is also expected to bring in new business opportunities by changing the way data supply chain is impacted within enterprises and outside.
Cloud business intelligence tools give a unique value to an organisation when it comes to data democratisation; providing tools can be readily available from any place. So companies are not restricted by resources for their data analytics workloads. But, the first step towards data democratisation has to be getting rid of silos and having better data collection, storage and access that makes agile business processes for real-time decision making.
Overview
An effort focused on granting everybody accesses to enterprise data, and analytics tools can help everyone do their jobs better. For those organisations that ignore this may find themselves at a disadvantage as lack of data democratisation can impede the core capabilities of making critical real-time decisions. This would lead to a situation where management would have to depend on analytics experts and data scientists to build reports and dashboards. At the same time, analytics and data science teams could be caught up in doing basic work such as data cleansing instead of adding value to business strategy.
