Organisations are collecting more and more data, and locked within lies untold business value. So say the endless stream of data gurus that have appeared in the last few years just as data has hit the headlines.
More often than not though the stories create a view that data projects are all about gathering ever more information about customers so they can be better marketed to. This feeds into the data guru's battle cry: 'Look at how Facebook uses data to target customers effectively, you can do the same'!
Well no, not really. Not many organisations have the vast data sets that these companies have. Sure, anyone can derive some insights about customer interests by looking at webpage analytics and twitter comments, but these are hardly the business transformational insights we're being promised.
Because of the media attention on these big digitally focussed companies, many assume that marketing value is the only benefit companies get from having access to large pools of information.
This is not the case. Organisations shouldn't assume data is simply an advertising tool, nor should non-digital businesses dismiss data as the preserve of the new, digital kids. Marketing is not only a very small subset of data's potential; it is also a completely different approach to analytics to the vast majority of data projects.
The primary focus for firms such as oil drillers, pharmaceutical companies, engineers, service providers and manufacturers isn't better targeted consumer marketing, meaning they need data for different purposes to digital companies. These businesses can harness value from the digital revolution, but their challenges, along with their business needs, are very different to those of organisations such as Facebook.
If these companies want to derive business value from data, the challenge is finding where the benefits lie in their information. This is about finding direct causal effect between one event and another - a change indicating a problem that could shut down operations, a new material which improves the efficiency of a jet engine, the optimal way to transport water around a city or manage transport operations around the country. The Amazon approach of 'if we do this, 1% more people will buy things' won't cut it. Knowing 1% more will buy is good for Amazon, knowing 1% of plane parts will fail in the next six months isn't - we need to know which parts and when. We need to establish causation between the data and the outcome - not just a correlation.
This takes more than simply hiring a new digital graduate. Companies need to start with an understanding of the business decision they need to inform - and find people who understand both the business and the technical challenges of their industry. It requires industry experience and expertise alongside deep technical skills to secure value from data.
An organisation may have a wealth of information at its disposal, but unless there is a clear idea within the business of what the end goal is and what you are looking to find, this data is meaningless. Companies need to know what it is they are looking to gain from use of data analytics and ensure that this is explained clearly to everyone involved in the project. This will mean that the data scientists will be able to hone in on the key areas of the information far more quickly and that money isn't wasted looking for things that aren't needed.
Almost every organisation, regardless of size or sector, has the opportunity to benefit from the data they hold in many different ways, and the myth that it is just a marketing tool must be dispelled. Not all businesses are Facebook and shouldn't try to be. Through having a clear vision of what it is you want to achieve and finding the right expertise organisations across the business spectrum can improve massively thanks to data analytics. The opportunity shouldn't be missed just because data gets pigeonholed as a marketing tool.