We all know these PowerPoint decks that emerge from corporate boardrooms and that tell the tale of a new business strategy. As a given, revenues will grow and/or cost be reduced. But how often have you seen those plans be complemented by a proprietary vision on data? Or, without becoming malevolent, what insight could lead to a change of course?

And that’s where the difference between ‘data-driven’ and the rest of the world does appear. Sure, we all base our thinking and acting on paradigms, sometimes implicitly most often quite explicit. But very few companies have really validated these pillars of policy with quantitative proof. Which is an odd observation in the era of ‘Big Data’ ! Never before so many data could be made available. To put it differently, more than ever data relevant for the execution of a strategy should exist.

If we look into the nature of data-driven entities we can distinguish between an ‘old school’ and a ‘new school’. In the former we descry formal data warehouses that harbour carefully selected data with eternal value. Typical applications were in customer relationship management for instance at financial institutions. The latter consists mostly of players that arose from the worldwide web and the data flood it caused. Focus on optimization became the leading theme of so called web analytics. Conservation of data is of less interest.

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The evolution of computational power opened up the possibility to convert ubiquitous clickstreams into customer journeys. Instead of preaching how it should work – since invented here J – nowadays we are able to really understand how customers behave in practice and what drivers are of excellent customer experience. Indeed, it’s not about the Net Promoter or Customer Effort Score, it’s about what the hurdles are you need to overcome to improve them!

Although the role of the evolution of computational power should not be overlooked, Big Data above all is about more data that may allow for better insights, facilitate new points of view, or just harness already available predictive models. This development gives way to reach the ambition of a 360 degree customer view.

As a basic example, it will not surprise that combining a Next Best Action model with contact data predictive scores can be lifted (e.g. the page yesterday clicked is likely to be of more influence than the flyer sent out last month).


Crucial in building up 360 is integration of data from disparate resources. Lessons learnt on data processing still apply, the importance of data quality or the veracity of data come to mind. But also new necessary conditions and rules must govern, how to deal with unstructured data or obsolescence of data for example. And last but not least: privacy revisited.

Needless to underline that the art of master data management is the foundation for any Big Data initiative. Unfortunately this is not a capability to acquire through distance learning. To that extent it compares to running in mud. Once you are aware where footprints have future, your shoes and feet will be less wet.

However, the good news is that thresholds to becoming data-driven are lower than ever. So determine which data could do what for the execution of your strategy and it might be your first step in becoming one of the new entrants in the world of data.


By: Herman Huizinga, Principal Consultant Business Intelligence.