Analytics: the £25 billion mistake

The following article was published by Lovely Mobile News on September 21st 2018, and written by Caroline Worboys. 

Caroline Worboys, Chief Operating Officer at Outra, discusses the power of analytics. 

In just two very short years the analytics market in the UK will have doubled to be worth a staggering £25 billion.

It is already the second largest market in the world, and the biggest in Europe. UK boardrooms are awash with data and insight all designed to help them perform better than their competitors.

However, sadly this isn’t always the case. In fact research from McKinsey shows that despite significant investment and the promise of the holy grail of competitive advantage most analytics projects fail.

Almost 9 in 10 (86 per cent) executives say their organisations have been only somewhat effective at meeting the objective of their analytics programs.

Whilst a quarter of them go even further and explicitly say that it hasn’t been worth the time, money or effort.

Not a glowing endorsement of the industry. Yet despite this two thirds of companies expect to increase their analytics investment by 26 per cent over the next year.

So what’s going wrong? Why are so many organisations failing to capitalise on their investment?

The major issue is a common one – a lack of planning. The old adage plan to fail, fail to plan is true. Most organisations, unless they are a unicorn business, have legacy IT systems from a cornucopia of vendors meaning that data sits in silos. Bringing it all together to be analysed is a headache which starts with the wrangling of data so that the real analytics and data science can begin.

Wrangling is the process of transforming and mapping disparate data from one ‘raw’ form into another format with the intention of making it more valuable and appropriate for downstream processes, including analytics. Wrangling should consume around 80 per cent of an analytics project resource, but rarely does.

The reality today is that data is often optimised by channel, and as a result many organisations have become quite sophisticated at optimising search, or web behaviour based on key words or last clicks.

However, as we all know to our cost, sometimes we click the wrong thing and we get funnelled down a route that, whilst being logical, doesn’t meet our needs.

Alternatively an organisation might take a product strategy approach. This means that the customer experience is fuelled by previous purchases. The organisation uses these to push similar products to the customer. However, as Amazon discovered early on, often customers aren’t buying for themselves and don’t want to receive recommendations for future purchases based on Great Uncle Fred’s military history interest.

Therefore, fit for purpose wrangling needs to become the foundation for more a customer centric approach – a blend of product, channel and insight based optimisation – and not just for regular customers; but for every customer. Google and Facebook are prime examples of this. They have made it their mission to understand the purpose, motivation and needs of every person on their platform, no matter how often they interact in order to enhance the customer experience and bolster their ad revenue.

For non-ad funded brands this holistic vision needs to be replicated and will become the basis for competitive advantage. Understanding a customer’s life story through their data footprint and being able to react to this better and faster than the competition is, very simply put, how analytics can boost the bottom line.

Customer centric oriented data wrangling represents a daunting amount of work and time.

And some may question if it is worth the effort particularly as in the initial stages there can be little to show for the hard work; unlike the cascade of results that occur during the data modelling phase of an analytics project. But it must be remembered that once the code and data infrastructure are in place analytics will deliver results quickly.

I’ve read numerous articles about the power of analytics and the potential they have– and it’s all true. But analytics can only ever be as powerful as the machine behind them and if the foundations aren’t correctly laid then organisations would be better placed putting their 26 per cent budget increase towards something else entirely.