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Understanding the Value of Data

Understanding the Value of Data

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.

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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.;



Data Science Congress 2017

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