How to Manage the Next Generation of Big Data Solutions

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Listening to the pundits, you can be forgiven for thinking that the unstructured, “cloudified,” out-of-network data tsunami is poised to sweep through and shake enterprises out of their comfortable, relational worlds. But there’s more to the story than that. Enterprises still, and will likely continue to, rely on relational database systems as their transactional workhorses. These systems continue to evolve and adapt to today’s new data realities. Many relational database and data warehouse environments are opening to unstructured data, running in clouds, and supporting caches that enable real-time—or near real-time—decision making.

Relational Databases Still in Wide Use

A recent survey of 304 data managers finds close to two-thirds of respondents were planning to employ their relational databases to meet the big data challenge. In addition, two-fifths of organizations also rely on archived or historical data—for initiatives such as data warehousing—as the foundation of their big data efforts. (“2013 Big Data Opportunities Survey,” sponsored by SAP and produced by Unisphere Research, a division of Information Today, Inc.)

While all the excitement is currently focused on new-age solutions that have surfaced in the past few years—NoSQL, NewSQL, cloud, and open source databases—there is still a great deal of uncertainty and consternation among corporate and IT leaders as to what role new data sources will play in business futures. Still, there’s no denying that the pressure is on organizations to evolve, as quickly as possible, into data-driven concerns, basing decisions on information that was unreachable or unavailable until recently.

The Need for New-World, Nonrelational Technologies 

To become these analytical enterprises, decision makers need access to the power of new-world, nonrelational technologies, supported by relational transactional data engines already well established in enterprises. The challenges seen in enterprises over the decades—performance, availability, flexibility, and scalability—still remain and are even growing in intensity. To be clear, the analytical enterprise didn’t just spring out of nowhere, it sits on the shoulders of giants. The analytical enterprise of today is not only dealing with the new streams of big data seen today, but also the systems and transactional engines and analysis tools that have been built and put to the test through years of work.

In addition, many high-volume sites are shifting to newer types of solutions, such as appliances and data virtualization to move data faster, the Unisphere survey of 338 data managers shows. (“Moving Data: Charting the Journey From Batch to Blazing,” sponsored by Attunity and produced by Unisphere Research, a division of Information Today, Inc.)

How to become an Analtical Enterprise

To succeed in today’s hectic and ever shifting global economy, the budding analytical enterprise needs to have the following elements:

  • A strong foundation of trust, ensuring that all information reaching decisionmakers is as reliable, up-to-date, and as contextually relevant as possible.
  • An understanding that there is no one single system or type of platform that can deliver all the capabilities needed. There are many technologies—some decades old, others new on the scene—that will help the organization move forward.
  • Technology that is designed around, and with the close cooperation of, business end users to best address what they require to do their job and make the best decisions.

For more articles on this topic, access the DBTA Thought Leadership Special Report - Managing Big Data: The Next Generation of Solutions

Image courtesy of Shutterstock

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