Maximum value: Why and how should you make data analytics more accessible?

The secret to getting maximum value from your data, according Mark Hinds, CEO of Polymatica, lies in empowering a greater number of users/employees to analyse it

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.

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.

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?

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.

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?

The first and most obvious step in terms of encouraging wider engagement with analytics is to put in place the right tools.