Big data initially was characterized in terms of “the three V’s,” volume, velocity and variety. Nearly five years ago I wrote about the three V’s as a way to explain why new and different technologies were needed to deal with big data. Since then the industry has tackled many of the technical challenges associated with the three V’s. In 2017 I propose that we focus instead on a different letter, which includes these A’s: analytics, awareness, anticipation and action. I’ll explain why each is important at this stage of big data evolution.
Big data has become an integral part of information management. Nearly all organizations have some need to access big data sources and produce actionable information for decision-makers. Recognizing this connection, we merged these two topics when we put together our recently published research agendas for 2017. As we plan our research, we focus on current technologies and how they can be used to improve an organization’s performance. We then share those results with our readers.
Topics: Big Data, data science, Data Governance, Data Integration, Data Preparation, Information Management, Internet of Things, analytics, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
The business intelligence market is bounded on one side by big data and on the other side by data preparation. That is, to maximize their performance in using information, organizations have to collect and analyze ever increasing volumes of data while the tools available are constantly evolving in the big data ecosystem that I have written about. In our benchmark research on big data analytics, half (51%) of organizations said they want to access big data using their existing BI tools. At the same time, as I have noted, end users are demanding self-service access to data preparation capabilities to facilitate their analyses.
Data preparation is critical to the effectiveness of both operational and analytic business processes. Operational processes today are fed by streams of constantly generated data. Our data and analytics in the cloud benchmark research shows that more than half (55%) of organizations spend the most time in their analytic processes preparing data for analysis – a situation that reduces their productivity. Data now comes from more sources than ever, at a faster pace and in a dizzying array of formats; it often contains inconsistencies in both structure and content.
The big data market continues to expand and enable new types of analyses, new business models and new revenues streams for organizations that implement these capabilities. Following our previous research into big data and information optimization, we’ll investigate the technology trends affecting both of these domains as part of our 2016 research agenda.