More and more organisations are looking to make better use of their data to innovate and drive growth. However many are inhibited by the traditional, proven approaches of data warehousing and business intelligence. Traditionally, organisations have captured business transaction data, eg orders, in operational systems and then extracted it into data warehouses for query reporting and operational analytics. This approach is limited to the data in the operational systems, decoupled from the organisation's interactions with its customers and the results are out of date.

In today's digital age, customers are much more demanding because of easier access to information and services through Internet and mobile technologies. This leads to customers expecting responses in near real-time. Therefore organisations are having to assess and alter their approach to engaging with customers that requires a shift away from the traditional and proven data management approaches.

The approach that I see organisations now taking is to derive analytical insight from business transactions – often combined with other (external) data sets – to build statistical models which can be used to intervene as customers interact. By projecting the use of analytics forward in this way, organisations are able to influence, encourage or prevent things happening at the time rather than dealing with the effects after the event, if at all. I see resulting improvements in organisational efficiency, operational effectiveness and most importantly customer experience.

Implementing such capability requires an ability to inform decision making and to act as customers are engaging. Immediacy is needed, but not necessarily large scale continuous processing of information. What is more important is to use analytics to gain insight from data which reflect each customer's context, and then use that insight determine and carry out the next best action. This is achieved by focussing on a specific opportunity or problem to achieve a business outcome. Only then is business value generated; up to that point in the analytics process the organisation is incurring cost.

It follows that the use of big data and analytics within IT departments alone is at best going to achieve incremental improvements, probably focussed on IT cost savings. However, the greater opportunity is in using analytical insight to transform the way business works, and this requires active business sponsorship along with cultural change to adopt an information-led approach to customer management. Examples include monitoring the condition and tailoring the treatment of patients to deliver better healthcare, redirecting public sector services to prevent situations occurring which would then require greater resources, and combining data sets to better understand customers to nurture those who are most influential or valuable over time.

In all these cases, once implemented, more experience, data and context is built up over time which inform improvements to the analytical models. Greater understanding of customers, for example, allows organisations to be more authentic in their dealings with them thereby building a stronger emotional tie, and increasing the value generated for both the customer and the organisation. However, organisations must plan for getting it wrong: they may act on insight which undermines a customer's trust or damages the organisation's reputation. It is a risk which is often underestimated.

Overall, the opportunities are significant. By pursuing specific business opportunities I have seen that big data does not require a big solution to achieve desired business value. However in taking this first step, organisations must have in mind putting in place a platform that can extend to accommodate additional types of data and a wider range of analytics.

Tags:  predictive-analytics analytics big-data thought-leadership