Understanding the Value of Data

Arthur Cole
Slide Show

Automating the Intelligence of Business Intelligence

The enterprise has been sitting on a goldmine of valuable information for several decades now, but only recently has it had access to the technology to pull it all together and make sense of it. This is leading to a shift in the way organizations value both data and infrastructure – data becoming increasingly important to the business model while distributed cloud architectures and commodity hardware are diminishing the significance of infrastructure.

But raw data is like unrefined ore: There is potential there, but first it must be retrieved, cleaned, refined and then delivered to those who find it most desirable. For that, you need a top-notch data management platform.

According to a recent study by Veritas, many organizations are still squandering the value of data simply by not having a full understanding of what they have and how it can be utilized. More than 40 percent of data, in fact, hasn’t been accessed in three years. In some instances, this is due to compliance and regulatory issues, but in many cases it can be traced to improper management. Once data enters the archives, it tends to be lost forever even though it may still have value to present-day processes. As well, developer files and compressed files make up about a third of all stored data, even though the projects they supported are long gone. There is also a significant amount of orphaned data, unowned and unclaimed by anyone in the organization, and this is becoming increasingly populated with rich media files like video chats and graphics-heavy presentations.


But while the future of enterprise data management may seem clear, the execution is not. According to Rex Ahlstrom, chief strategy officer at data specialist BackOffice Associates, initiatives like application data management and advanced governance are on the radar, but technologies and business models are changing so rapidly that it is hard to determine exactly what needs to be done, and how.

Big Data, for example, is opening up new avenues for business intelligence and analytics, but until it can be linked directly to transactional data, it will produce limited long-term gains to the business model and be cut off from line-of-business managers who can make the most use of the results. Even more established processes like data governance are not yet deriving full value from available data because they don’t stretch across all types of master, transactional and configuration sets.

Indeed, many organizations are still stuck with department-level or even business-unit-level data management solutions that fail to provide full context of both the data’s value and broader organization needs, says Lisa Morgan, an analyst at Strategic Rainmakers. And when a more universal solution is contemplated, it is usually engineered from the technology side rather than by starting with the use cases and working backward. This is a shame because the reporting and analytics tools of today are dramatically more sophisticated than just a few years ago, but without proper deployment decision-makers are left with inaccurate results and must rely on intuition rather than solid facts to guide their organizations through an increasingly complex digital economy.

So what is the enterprise to do? How is it supposed to provide a cohesive view of its data in order to present a more accurate view of reality? A good place to start is with a thorough integration strategy, says Cleo CTO John Thielens. The old days of straight-line, left-to-right digital data chains may be over, but that doesn’t mean today’s platforms are not up to the challenge of dynamic, unstructured workflows.

A first step is recognizing that change is inevitable, so architecting an integration regime that can accommodate change quickly will be much more effective than trying to identify potential trends ahead of time. As well, emerging Big Data patterns are already replacing the longtime ETL (extract, transform, load) process of traditional warehousing with a schema-on-read approach that favors ELT, so it’s important for an integration platform to reduce information degradation to near zero, or even absolute zero if you can manage it.

Of course, it’s never easy to replace discrete management solutions with an enterprise-wide platform. Simply identifying the needs of all the various stakeholders is challenging enough, let alone accommodating them. This is why many organizations opt to fund their own rival start-ups to break their established business model with an all-digital one that is built from the ground up around virtual infrastructure, app-driven services and integrated data management and analytics.

And a crucial aspect of this strategy is finding and then leveraging the valuable information you already own.

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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