The New Database Technology Landscape From Relational to Blockchain

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An astounding array of new technologies and approaches have emerged on the database scene over the past few years that promise to turn the next 12 months into a time of unprecedented transformation for the database landscape. There are new developments, along with reinforcement of tried-and-true technologies, some of which may help make the jobs of data managers just a bit easier.

 “Gone are the days of a terabyte of data sitting in a relational database accessed by a few analysts using BI tools,” said Chris Doolittle, principal consultant of Teleran. “Big data, IoT, specialized database platforms, AI and machine learning, and the cloud are driving a generational transformation in data management.”

There is still plenty of hard, brain-twisting, arm-twisting work ahead to get this next generation of technologies into and aligned with organizations. “We are on the cusp of an unprecedented intelligence revolution, and a lot of the enabling technologies—cloud, machine learning, artificial intelligence, real time databases, next-generation memory technologies—are already available,” said Leena Joshi, VP of product marketing at Redis Labs. “What is needed is for enterprises to develop stacks that can tie all the piece parts together without generating layers of additional complexity.” This, more than anything, describes the job of data managers in the year 2018.

Here are key developments that need to top data managers’ to-do lists in terms of technology focus this year:

Analytics with a Purpose

For a number of years, the goal of many enterprises—egged on by vendors and analyst groups—was to find ways to disperse analytics across the enterprise, a kind of “data democracy.” Now, it may be time to shift gears on this vision, employing analytics not to empower single individuals, but to build a collaborative culture. “The trend toward self-service analytics is not panning out,” said Jon Pilkington, chief product officer at Datawatch. “Putting analytics power in the hands of the business user was supposed to create agile companies and deliver analytical, data-driven decisions. Instead, companies are in worse shape than ever before. IT has lost control over data usage, and analysts are working in silos, duplicating work efforts and experiencing a severe lack of trust in their data and analytics outcomes.”

Pilkington urges data managers to move away from the self-service goal and work toward more collaborative “team-based, enterprise data preparation and analytics.” Such collaboration “will create a data-driven culture by bringing analysts together for the common purpose of getting answers—answers that are founded in the cross-business insights necessary to profoundly impact operational processes and the bottom line. Teams will be able to create, find, access, validate, and share governed, trustworthy datasets and models for true enterprise collaboration and faster, more strategic decision making.

Artificial Intelligence to Improve Artificial Intelligence

Artificial intelligence may go a long way in helping businesses understand and predict their futures, but AI is only as good as the data feeding it. Ironically, AI will help organizations achieve better AI results. “Many companies are faced with challenges around whether their data is current, complete, and consistent,” said Doug Rybacki, VP of product management at Conga. “When you apply intelligent tools against data that is lacking in these components of data quality, the result is disappointing and potentially misleading.” Ideally, said Rybacki, intelligent tools must be used for data hygiene so that the larger benefits of machine learning and artificial intelligence applications can be realized.

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