The terms democratising data or democratising analytics are growing in popularity amongst business owners, especially those hungry to use data more pervasively and gain insights into their organisation.

By Tony Nkuna, senior presales solutions consultant at TechSoft

But there is some confusion about how this plays out and some kickback from data scientists who don’t believe it is as easy as the marketing material would have you believe.

The theory behind democratising analytics starts with making data more accessible to as many people in a company as possible. The idea is that by making data available, people can drive decisions that are tangible and better informed. However, the mechanics are reliant on putting several processes and tools in place to ensure that the right data is served to the right people at the right time.

Analytics is a big deal

Over the last year, we have heard from industry peers how data and analytics literally saved their bacon during a time of business upheaval caused by the pandemic. Using data many businesses were able to pivot traditional physical retail strategies into online consumer facing ones, and others were able to analyse the cost of real estate investments and shrink these to accommodate a burgeoning remote workforce.

One thing they all had in common was their use of analytics to guide their decisions. Another is that they have started involving more people in this process by providing them access and insight into all aspects of the business. The adage states that you don’t know what you don’t know, which is where data comes to play. It gives you a view of the past and the present – where analytics gives you the map to the future.

Culturally businesses need to shift their thinking away from data only being understood by the analysts. Instead if we embrace an approach where if they give this knowledge to all employees, we may be surprised by the value and input they can offer around things we may not have considered.

Marrying people, processes, data

Pulling your business into the new data era is a little more challenging. Legacy systems can prove to be a big hurdle to organisations looking for better access to their data, whereas SaaS and cloud-based apps might be flooding your data intake. Knowing what is important and what is actionable data becomes a critical balancing act.

Before we even start to spread data around an organisation like confetti at an 80s wedding, we need to get the technology and processes that support the ingestion of data right. This requires developing a data fabric, using API-led integration to pool data sources, and then overlaying this with analytics tools that don’t need remodelling every time you want to run an analysis.

The concept behind value analytics is derived from the interplay of data, technology, statistics, and business processes. In essence, to derive value analytics, a company needs to embed analytics in their DNA. So they build services/applications based on the goal to either probe responses or derive data, and they put these processes into the hands of more people. Most importantly, the need for data and analytics are driven down from the leadership to employees.

Tooling the democracy

Democratising analytics is not a case of pooling data into a central place and saying to employees they can now “have at it”. As mentioned earlier, creating a data fabric is the most critical part of establishing analytics as a best practice in your business. Start with fixing, cleaning, and structuring data – use data virtualisation tools where appropriate and curate the right tools to build a data foundation.

Let your data scientists build reusable models and applications that shorten the time to insights and that more people in your organisation can use. There are tools to help this process; you don’t have to employ a team of 100 data specialists to get value from your data. The right tools will help you connect people, data, and systems and then unify this by offering access to this data to your people, allowing you to analyse or predict from this data comfortably.

You will always need experts to build out a view of your data and then tools to deliver these analytics. Democratising the process is less about letting everyone run amok building their own. It pertains to training leaders and managers to ask the right questions from data and is then achieved by disseminating the right knowledge, providing the proper training, and then passing on the tools.

A recent article by Shawn Rogers, vice-president: analytics strategy and corporate marketing at Tibco, speaks to how hyperconverged analytics requires a village. The same could be said that villages run better as a democracy – so should your analytics.