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Adapt or Die: Machine Learning Databases for the New Economy

Robert_BuckIn this special guest feature, Robert Buck, VP of Technology at Deep Information Sciences makes the case for new adaptive technology that couples databases and machine learning to address the demands of a data driven economy. Robert is integral at delivering and driving value into customers hands in the technology space, and specifically in the database, finance, and telecommunications industries. He leads the charge in innovation, strategy, and win:win sales.

A clear and fundamental shift in business is occurring as we speak. The buzz is around words such as foresight and insight, intelligence and predictive, real-time and analytics, speed and results. This new language of business is driving a new pulse of engagement, where decisions are increasingly evidence-based and businesses must continually adapt to changing conditions.

Our New Economy has higher expectations. It’s about delivering innovative technology to transform economic and experiential relationships between services and consumers. It’s about altering markets and reshaping entire industries. Uber is just one such example of where traditional businesses are faced with one of two choices: adapt or die.

Like humans who learn through experience, it’s information that drives informed choice in modern business. That information, if unlocked, allows businesses to draw non-intuitive insights that permit them to leapfrog competition, or be first-to-market. At the epicenter of all this are the databases companies rely upon to fuel their business-critical applications and run day-to-day operations. However, traditional databases are challenged to deal with data’s massively increasing variety, velocity, and volume (3Vs). And like humans, or like businesses, databases, too, must adapt or die.

The opposite of adaptive

The problem is that, to date, databases have been the opposite of adaptive. Because of the legacy science they’re built upon, even modern databases are fairly rigid and slow. Defined by how data is organized or processed a priori, the classical approach results in databases that hit the wall once they reach certain levels of scale, whether measured in volume or velocity. To tackle the problems of 3Vs, the landscape is now littered with purpose-built databases, each of which address just a subset of the challenges — for instance, sacrificing performance for scale or functionality — require additional in-house expertise, and additional data-integration infrastructure to create a single business view to manage and operate against.

Machine learning-driven databases

Contrast that with new adaptive databases, which use machine learning to continually, and in real-time, adjust to the 3Vs. Rather than being rigid in how data is organized or processed, adaptive databases are fluid and malleable; they automatically reorganize data or tune algorithms to suit the workload observed, the type of data and its velocity. For example, if it learns that certain records are frequently accessed together, a house-keeping process in the background can reorganize data so reads are sequential, significantly speeding operations. Another implication of machine learning for databases is that many of those purpose-specific technologies created in an attempt to overcome traditional scaling and performance limitations, can be eliminated. With adaptive technology, your operational database can now also become your analytical database, and a tool of innovation.

The New Economy is about businesses accelerating their valuation, not by focusing on the tactical, tedious, mundane tasks of operating a service, but by continually and quickly driving innovation into the marketplace and leap-frogging competition. Many of the companies I’ve had the pleasure of working with are crying for mercy; under the weight of burdensome database technologies, they’re desperately looking for a smart and, finally, effective alternative to traditionally built, albeit often new, systems.

To quote an esteemed CEO, “it’s about the data, stupid.” Indeed. The way to unleash business potential is the same as it is for human potential: leverage information to learn and adapt. Which is why machine learning is the final frontier for data science; it’s the next, much-needed, evolution for databases. When databases can autonomically tune and operate, business will be empowered to draw new-found, non-intuitive insights that enable the continuously adapting business that our New Economy requires.

 

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