The Self-Service Analytics Evolution

Friday 10 June 2016
Reading Time: 2 minutes

Today we live in a world of self-service.

More tasks than ever before can be completed without even a hint of social interaction; buying fuel, withdrawing cash and even brewing fresh coffee can be achieved without the forced conversational drudgery from a fellow human being.

This changing nature of user interaction is not due to the unsociable appeal, however. It instead represents a desire for speed; a faster checkout, a faster refill, a faster double espresso skinny decaf organic white chocolate vanilla gingerbread-infused Frappuccino.

Clearly, the influx in user-friendly time-saving technology is a direct reaction to the changing nature of consumer interaction. In technology, this is represented by the instant gratification provided by smartphones and tablets that allow users full access to the internet, leaving a long trail of data ripe for interpretation in their wake.

The big data landscape represents not only a shift in data quantity, but in its perceived quality to individuals and businesses alike. By harnessing this surge in data, real insight can be gleamed from customer behaviour to enhance services and target specific groups based on emerging patterns.

With this in mind, the notion that 90% of data available has been created in only the last two years is not to be taken lightly. This surge in data, with the right analysis, should pave the way to business efficiency savings, improvements in targeted marketing and developments of more streamlined user experiences in every sector.

When it comes to the term ‘big data’, what does this mean for businesses and how must the practise of data analysis adapt to meet the increasing data-driven demands?

The answer is self-service business analytics.

It is no secret that traditional tools rely on professional analysts to interpret data sets through dedicated knowledge in SQL (Structured Query Language) across multiple disparate systems such as OLTP (Online Transaction Processing) or OLAP (Online Analytical Processing), systems that are far from straightforward. Although these technologies deserve credit for the level of analysis they have provided over the years and have been stretched to an extent, the huge increase in data is showing no signs of slowing. This poses new problems to technologies that were never designed to analyse such a wealth of diverse data sets.

As the data increases, so do waiting times and cost of operation. With analysis limited to analysists, there will always be a performance bottleneck. This all changes when users are given the power.

Self-service tools built for the analysis of today with user experience a fundamental component not only allow cost savings, but improve the quality of analysis. With search-based technology, for example, users can quickly find the data they require through natural language search, enabling drastically improved data-access times.

With the barriers of old technology removed, a new generation of analysis can be heralded; one that allows the wealth of data available to be instantly viewed, digested and analysed for future improvements based on timely insight.

The case for self-service analytics is a compelling one; a genuine technology evolution that allows more time to be spent improving services rather than waiting for data to be processed by experts.

By allowing data analysis to bypass analysts, a data-driven culture can thrive, ensuring that the big data challenges of today inspire improved decision making based on the data that is easily accessible to all.

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