Today, data is the most valuable asset businesses hold, with more information at their fingertips than ever before. Ideally, every part of an organisation should be able to analyse this vast wealth of data at every level to make more intelligent decisions; examining live data and adapting their operations in an agile fashion. However, this very rarely happens.

Volume, control or both?

One issue is the sheer quantity of data being analysed. As data volumes double every 18-36 months, traditional Business Intelligence and analytics solutions are simply failing to keep pace.

Analysis is often measured in days, rather than seconds, minutes or hours. Businesses, therefore, face a stark choice: restricting themselves to narrow subsets of data in order to receive limited insight in a timely fashion, or performing a more in-depth analysis that cannot necessarily give insight at the speed the business needs.

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Another issue is who can actually perform the analysis. Most organisations have a significant pool of staff with deep expertise in different areas of the business. If these subject matter experts could analyse business data directly, they could drive enormous additional value and revenue. So why do businesses almost never achieve this?

Analytics: Difficult and slow?

Data analysis has a reputation for being difficult. Most traditional BI and analytics tools require specialist skills – including in-depth knowledge of mathematical modelling, an understanding of machine learning techniques, and coding languages such as R or Python. It’s not hard to see why this might be off-putting to someone whose expertise lies in logistics or purchasing.

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As a result, most businesses rely on trained data scientists to perform analysis. This presents a bottleneck, increasing the time analysis takes and reducing the organisations’ agility. After all, these trained specialists will probably lack detailed knowledge of specific business units – meaning any insight will need to rely on a back-and-forth with business specialists, or risk insights that are incomplete at best, and misguiding at worst.

So how can businesses empower subject matter experts to analyse data directly?

Beating the fear factor

The first and most obvious step in terms of encouraging wider engagement with analytics is to put in place the right tools. Most organisations understand that speed and power is critical to digest enormous amounts of information in near-real-time, with no pre-filtering. Yet just as important is technology that feels accessible, and that can offer step-by-step support for the non-professional. Any such platform should make the common functions easy – with templates for complex tasks, as well as tutorials, dashboarding and easy-to-use visual modelling.

Even so, as analytics can seem intimidating to the uninitiated, you’ll need to address employee ‘fear factor’. In truth, all that business users really need to get started is an understanding of spreadsheets and basic maths, and a willingness to give it a try. The trick is in helping them to recognise this, and encouraging them to engage with data.

>See also: Data scientists: What they do and why businesses need them

The first step is identifying those individuals who are willing to try using data to their advantage. Businesses should also investigate who, whether existing staff or a new hire, can bridge the worlds of data and the business. This person can help employees identify what insights they can gain from data, identify the kinds of queries to make, and how to make them. This won’t be an overnight process. Employees need time to recognise the benefits of engaging with data – to the point that they start to become internal advocates and spread their message within the organisation.

Businesses will also need to engage with their IT department to ensure that data is not exclusively ‘controlled’ and ring-fenced. Taking up data scientists’ and IT professionals’ time to help other employees access data directly should be framed in terms of the clear long-term benefits. Ultimately, it will free up your data scientists and IT professionals to do more valuable, skilled tasks – in other words, a win-win.

New possibilities

Opening up access to real-time data analytics opens up a whole host of new possibilities for businesses. The sales team in a bank, for example, can identify opportunities to cross-sell products, such as credit cards, based on live purchasing information.

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A supermarket purchasing manager in a Monday morning sales meeting can explore sales data on-the-fly, drawing on the business knowledge of everyone present to make intelligent purchasing decisions instantly. Merchandising managers can analyse store footfall as it occurs and amend parts of the in-store experience across the day.

The possibilities engendered by such systems, and total access to them, are almost endless and are more than worth the cultural shift required to make them happen.

Sourced by Mark Hinds, CEO, Polymatica