Why 2015 Will Be Year of Big Data: Oracle's Seven Predictions

By Chris Preimesberger  |  Posted 2014-12-16 Print this article Print
Oracle's data predictions for 2015

Data is a new form of capital. Ultimately, information about people, places and things will truly differentiate enterprises.

Oracle has been handling big data-type workloads in its parallel databases since long before the term "big data"—and even the Internet—was born.

Now that there is such a preponderance of business and personal data whirling around the globe every millisecond, a zillion more enterprises are paying attention to the potential value all this data can bring to balance sheets around the world. Oracle, founded way back in 1977, has seen and addressed this trend from a lengthy and deep perspective.

Neil Mendelson, Oracle's vice president of Big Data and Advanced Analytics, is immersed in this sector each day at his office in Redwood Shores, Calif. In a conversation with eWEEK, he offered readers some insight into what the company is thinking—and how big data trends will be evolving—as we all move into 2015.

Large companies, not just startups, are realizing that big data analytics can do wonders for them. In fact, Mendelson believes that 2015 will indeed be known as The Year of Big Data in the Enterprise.

Data: The New Lingua Franca of Business?

"Data is a new form of capital," Mendelson said. "In fact, you see new businesses today whose business is data. Data provides tremendous worth. Companies like Google, for example, are powered by the value that data produces.

"Today we have an index that talks about the value of your brand. In the future, we will see an index that talks about the value of your data. At the board level, companies are beginning to understand that they're facing new competitors, versus like competitors, and the change is very real. Retailers are concerned about Amazon intruding, banks are concerned about telcos providing banking services on your phone.

"At the end of the day, it's information about people, places and things that will truly differentiate them," Mendelson said.

With all that in mind, here are Oracle's top seven big data-related predictions for 2015:

Prediction 1: 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. For 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: the ability to get data the firm wants into the shape it needs with minimal time, cost and risk.

Prediction 2: 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—Hadoop, NoSQL, and relational technologies—act as a mature enterprise-grade system.

Prediction 3: 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 this big data SQL works just like full-fledged modern SQL that their applications and developers already use. This will put pressure on nascent Hadoop-only SQL to mature overnight.

Prediction 4: 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.

Prediction 5: 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 in technology 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.

Prediction 6: Security and governance will increase big data innovation.

Many large firms have found their big data pilots shut down by compliance officers concerned about legal or regulatory violations. This is particularly an issue when creating new data combinations that include customer data. In a twist, firms will find big data experimentation easier to pen up when the data involved is more locked down. This means extending modern security practices such as data masking and redaction to the full big data environment, in addition to the must-haves of access, authorization and auditing.

Prediction 7: Production workloads blend cloud and on-premises capabilities.

Once companies see enterprise security and governance extended to high-performance cloud environments, they'll start to shift workloads around as needed. For example, an auto manufacturer that wants to combine dealer data borne in the cloud with vehicle manufacturing data in an on-premises warehouse may ship the warehouse data to the cloud for transformation and analysis, only to send the results back to the warehouse for real-time querying.


Chris Preimesberger

Chris Preimesberger is Editor of Features & Analysis at eWEEK. Twitter: @editingwhiz
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