- Corporate boardrooms will talk about data capital, not big data. Data is as necessary for creating new products, services and ways of working as financial capital. CEOs, this means securing access to, and increasing use of data capital by digitizing and datafying key activities with customers, suppliers and partners before rivals do. For CIOs, this means providing data liquidity
- Big data management will grow up. Hadoop and NoSQL will graduate from mostly experimental pilots to standard components of enterprise data management, taking their place alongside relational databases. Over the course of the year, early majority firms will settle on the best roles for each of these foundational components. The demand for data liquidity will compel architects to find new ways to make the full big data environment
- Companies will demand a SQL for all seasons. SQL is not just a technology standard. It’s a language based on 100 years of hard thinking about how to think straight about data. Applications, analysts, and algorithms rely on it daily to run everything from fraud analyses to freight forwarding. Companies will demand that SQL works with all big data, not just data in a Hadoop, NoSQL (Oh, the irony!), or relational silo. They’ll also demand that thisbig data SQL works just like full-fledged modern SQL that their applications and developers already use.
- Just-in-time transformation will transform ETL. New in-memory streaming technologies change the rate at which we can act on data, causing a re-examination of extract, transform, and load (ETL) activities. Data scientists will increasingly opt for real-time data replication tools instead of batch-oriented ones to get data into Hadoop, which has been the norm. They’ll also take advantage of distributed in-memory processing to make data transformation fast enough to support interactive exploration, creating new data combinations on the fly.
- Self-service discovery and visualization tools will come to big data. New data discovery and visualization tools will help people with expertise in the business, but not intechnology use big data in daily decisions. Much of this data will come from outside the firm and, therefore, beyond the careful curation of enterprise data policies. To simplify this complexity, these new technologies will combine consumer-grade user experience with sophisticated algorithmic classification, analysis and enrichment under the hood. The result for business users will be easy exploration on big data, such as knowing where the oil is before digging the well.