Data Management in the Digital Age

Arthur Cole

The enterprise quite naturally wants to get the most performance out of available infrastructure. But in addition to deploying the appropriate hardware and layering it with advanced architectures, it also must implement the correct data management strategy for high-speed digital environments.

Data, after all, is the single most valuable asset these days, so carelessness in the way it is processed and distributed across the IT landscape can make or break the business model.

According to Ashish Gupta, vice president of engineering and head of India R&D at Rubrik Inc., most organizations are sitting on a data mismanagement time bomb because the tools they are using for data management in legacy infrastructure are nowhere near capable of handling the load in a digital service-based environment. With multiple data frameworks existing between, and sometimes within, disparate data centers, the need is growing for an integrated, end-to-end management solution similar to the way early search engines were able to unite the fragmented data silos on the internet.

An effective data management strategy should focus on four key objectives, says 451 Research’s Henry Baltazar. In a recent interview with Network World, he pointed out that increased visibility, broad asset integration, cloud and/or object-based storage, and intelligent automation will simplify the management burden and allow it to scale to meet modern demands. With these four components working in tandem, IT staff will be able to focus on strategic objectives rather than serve as data traffic cops.


A major consumer of data going forward will be the analytics engines that guide IoT operations, which is why we are seeing increased synergy between analytics platforms and data management. Syncsort, a New York developer of data integrity and integration solutions, recently acquired Metron Technologies, a specialist in capacity management software and services. The plan is to incorporate Metron’s athene forecasting and modeling platform to improve Syncsort’s resource optimization capabilities for both Big Data and Big Iron solutions.

Yet another challenge is the need to manage data across hybrid infrastructure. As Actian CTO Mike Hoskins recently explained to IT Pro Portal, enterprises risk obsolescence in an increasingly data-driven economy if they cannot marshal the information spread across disparate infrastructure. This is leading to the creation of “hybrid data,” which Hoskins describes as data with multi dimensions allowing it to span multiple data types and formats, usage requirements, connectivity patterns and infrastructure models. In this way, the enterprise gains a single view of its data assets, allowing them to be managed, integrated and analyzed across the entire data ecosystem. This will likely involve a multi-stage conversion process, but in the end will put the enterprise on a more solid footing for the emerging digital economy.

Improving infrastructure while neglecting data management is like widening city streets but taking out all the traffic lights. You spend a lot of money and everything looks nice and new, but you are still left with poor results.

Data management is likely to be an ongoing challenge, but it is a necessary burden as the enterprise makes the transition to a new digital entity.

Arthur Cole writes about infrastructure for IT Business Edge. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly. His contributions have appeared in Communications Today and Enterprise Networking Planet and as web content for numerous high-tech clients like TwinStrata and Carpathia. Follow Art on Twitter @acole602.


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