Long before the Internet and the inception of “big data,” Oracle has been dealing with big datatype loads through parallel databases. With the growing supremacy of personal and business data globally, many of the world businesses have turned their eyes towards the possible gains to their bottom-line that said data could contribute.
Long before the Internet and the inception of “big data,” Oracle has been dealing with big datatype loads through parallel databases. With the growing supremacy of personal and business data globally, many of the world businesses have turned their eyes towards the possible gains to their bottom-line that said data could contribute.
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This is what Neil Mendelson, a VP at Oracle in charge of Big Data and Advanced Analytics, has been poring over at his office. As we move further into the year, Mr. Mendelson offers some predictions held by the data authority regarding evolution of the data trends.
The new Trade Language of Data
“You need only examine companies like Google to understand the true worth that data brings,” Mr. Mendelson asserted. “Where the current market is focused on brand value, future trends are expected to shift towards data value. In the final analysis, businesses large and small will be differentiated by the data they hold – information about places, things and people.”
Bearing this in mind, below are the five top predictions relating to data from Oracle.
#1: Talk in corporate boardrooms will shift from big data to data capital
Data contributes just as much as financial capital to innovation and creation of new products, services and processes. For the CEO, this implies greater focus on means to access data capital ahead of the curve while the CIO would be interested in providing data liquidity.
#2: Growth of big data management
NoSQL and Hadoop will progress from their current experimental aviator positions to typical components within business data management, right along relational database systems. During the year, firms within the early majority will decide on the best roles to assign to various rudimentary components and utilize all three architectures to comprise a mature big data management system.
#3: Greater demand for an all-seasonal SQL
Enterprises will demand an SQL that works with every kind of big data, rather than just data within RDBMSs, NoSQL or Hadoop. There will also be demand for the SQL from above to have similar capabilities as the modern SQL which are already being used by the business’s developers and applications, requiring an overnight maturity on the technology behind the SQL.
#4: Transformation of ETL by JIT transformation
Extract, transform and load processes (ETL) will be re-examined following the explosion of in-memory streaming capabilities. Data specialists will prefer real-time data replication methods over the batch-oriented ones that were the standard. Taking advantage of this in-memory processing technology, data transformation will be made quick enough to sustain interactive exploration.
#5: Big data will be characterized by visualization and self-service discovery tools
Technology resulting in the inception of data visualization and discovery tools will be useful to experts within the enterprise. The new tools will merge consumer level user interaction on the surface supported complex algorithm organization, investigation and enhancement behind the scenes. This will enable businesses utilize data from external sources according to internal scrutiny and governing data policies. As a result, big data will become much easier to explore.
