The Big Data discussion between Rabobank’s Harrie Vollaard and Sogeti VINT’s Sander Duivestein was extremely intriguing for a start . . . In his keynote speech VINT analyst Sander Duivestein was eager to stress that data measurement now is the modern equivalent of the microsocope, invented some four hundred years ago. Meticulously zooming in on essential heterogeneous data, we can check back on both what has happened before plus what is actually happening in real time today. All for the better economically, and to predict, or at least anticipate on, what will be occurring next. Such a breathtaking ‘Recorded Future’ will be the New Normal when organizations will devote themselves to Big, Extreme or Total Data analysis and management. Gigantic opportunities lie ahead. Five exabytes of information were created between the dawn of civilization through 2003, according to former Google boss Eric Schmidt. That much information is now being created any two days, and the pace is increasing. Soon, Digital Age evangelist Don Tapscott says, there will be five exabytes every few minutes! What, Sander Duivestein wondered, would happen when the predictive capacity of organizations would improve dramatically? Would for instance, privacy concerns pose only a temporary problem or would they halt the current Big Data craze altogether? And should for that matter organizations throw out their traditional knowledge systems? In favor of completely new ones? Far more questions than can be answered. In response, Harrie Vollaard, Innovation Manager at Rabobank, explored the role of Big Data in being the New Gold Rush. As for relevance: Big Data is supposed to be the new virtual wave. And for insight: Big Data might well be the new decisive decision tool we all are waiting for. Finally efficiency: Big data simply shouts for a new IT infrastructure consolidation phase. But how will this all play out in future? Perhaps not that previously ‘Recorded’ at all . . . ? According to Gartner, Big Data analytics and the Hadoop open source project are rapidly emerging as the preferred solution to address business and technology trends. Enterprises can gain a competitive advantage by being early adopters of Big Data analytics. Why not? In this view, any conceivable question can be posed since the amount of data as well as technological and capacitative barriers are irrelevant now. Rabobank’s Big Data Roadmap to date Rabobank however remains cautious. Their Big Data Roadmap to date is very much Learning by Doing as the maxim goes. Their advice to organizations: by all means start small and add more complexity step by step. Rabobank started with internal data and thereafter some of their web click data. Eventually, selected social data, open data and trend data were integrated. So as to not ‘oversurprise’ their proven data approach with a deluge of semi and unstructured mess. Serendipity is cool – as long as it does not run out of control of course. Rabobank operates with a highly skilled and multidisciplinary Big/Extreme/Total Data team. They carefully experiment in short cycles to keep control. Rabobank’s calculated focus Their calculated first focus lies on tackling business process issues with Big Data. This is because the IT impact is low and pilot investments relatively easy can be earned back. Examples for Rabobank include risk and fraude analysis, rogue transactions, incidents in the chain, and process optimization. Second comes exploring new business opportunities with Big Data. The IT impact is moderate and the value certainly worthwhile. Examples include customer behavior, influencers marketing, sentiment mining, customer risk valuation, and payment behavior. A high Big Data promise is the value of developing new business models. Comparably high however is the IT impact. Currently, Rabobank figures, it is too early to strategically combine internal and external data on a Big Data scale in this area. Finally, bottlenecks in the data infrastructure naturally are of constant concern. These however should not be approached through Big Data experiments but by improving the existing Business Intelligence methods and tools. Rabobank’s conclusion In order to reach the aforementioned objectives, Rabobank proactively aims to combine algorithm skills, software skills and marketing skills in its Big Data Competence Center. Their conclusion for the time being is threefold:
- Big/Extreme/Total Data technology and management complements transactional relational databases and traditional BI and datawarehouse efforts. Big Data solutions are already avaliable at low cost and with high scalability.
- Assisting business users in their driver seats is Big Data’s main challenge!
- Skilled and dedicated multidisciplinary Big Data teams are key to success.