There seem to be many reasons for this, and the first half of the research report focuses particularly on what doesn’t work well yet:
- Data is in silos, scattered across the enterprise
- No convincing business case for moving further
- Ineffective alignment of Big Data and analytics teams across the organization
- Most data locked up in petrified, difficult to access legacy systems
- Lack of Big Data and analytics skills
If you take a good look at it, there is a strong resemblance to the Digital Transformation success factors that are described in our research with MIT and the resulting Leading Digital book. There, we found that digital success does not only depend on a deep understanding of the next generation technologies and the way that they can reshape business – although it’s an area never to underestimate – but also requires change leadership: a top-down digital vision, a new digital governance, mobilization at the individual level and a technology platform that unifies business and IT and ensures future agility.
It all starts with vision. If the company executive leadership does not actively, demonstrably embrace the power of technology and data as the driver of change and future performance, nothing digitally convincing will happen. We have not even found one single exception to this rule. The CIO may live and breathe Big Data and there may even be a separate Chief Data Officer appointed – expect more of these soon – if they fail to commit their board of executives to data as the engine of success, there will be a dark void beyond the proof of concept.
Furthermore, a new governance needs to reflect the needs of a data-driven, digital enterprise under construction. Yes, appointing a Chief Data Officer, VP of Data Analytics or global Director of Data Management – whatever works for the poor individual – is a step in the right direction. But she will need at least a more centralized, Big Data shared service center, acting both as a competence hub, an accelerator for new projects and a coordination point for anything Big Data going on within the organization. Even better, our research shows that a separate data business unit – for example an analytics team that has its own profit target – shows the highest success rate. For now that is, as in many cases sooner or later data needs to be tightly integrated in all aspects of the business. Autonomy – like a startup – has its merits as a way to rapidly move forward. But a timely decision on its ‘exit’ is needed to create enterprise grade impact.
Finally, a sound technology platform is key to sustained success. Nothing worse than to have a fully committed and motivated enterprise – anxious to embark on its Big Data journey – only to find that IT can not keep up with the pace. A platform is not only about having the right, agile technologies in place while gradually dismantling the existing data landscape; it’s also about creating new skills and capabilities – even if it means that these rare Hadoop specialists, visualization gurus and data scientists only can be found through specialized service providers, startups or crowd-sourced communities.
Have a look at our findings – you can even do a short self-test – to convince yourself that there actually is a way to crack that data conundrum. It requires common sense and learning from the masters. And a little magic of course, like you would expect from a good conundrum.
Published before at Capgemini's CTO blog