Here’s an unfortunate truth: today’s asset management solutions are unable to completely validate the lifecycle status of an asset, which puts the business at financial and operational risk.
The Critical Nature of the Asset Lifecycle
The lifecycle of an IT asset begins with sourcing that asset and ends with its disposition or disposal. All of which sounds simple and without risk—until you introduce the Sarbanes-Oxley (SOX) Act into the equation. SOX requires that a company’s executives be held responsible for accurately reporting financial information, which in turn requires a governance process that records information correctly about every asset in their IT estate. In addition, the company must be able to demonstrate movement, control and support responsibilities within the process.
What’s Missing – And What it Can Cost You
If everything needed to prove good governance within the process could be discovered by an asset management solution, executives could relax. The problem is that areas within the governance processes are manual or non-electronic, requiring people to inspect, document and report that the governance process is being followed and accurately represents the state of the asset inventory. Manually validating asset data becomes increasingly difficult over time because data errors occur at change points during the lifecycle. To find these errors many companies make shallow attempts to compare individual attributes of a device with software applications like Excel, looking for exact matches. Not only is this process tedious and slow, it is also highly unreliable. In short, asset management software can’t provide the complete picture that a company needs to protect itself—and its assets. And without complete and accurate asset data, it is impossible to govern IT resources effectively.
The Blazent Solution for Asset Lifecycle Management
Blazent assimilates key asset data sources (e.g. Asset Management, discovery, operational tools, etc.) into its Big Data engine and applies a patented Data Evolution Process which analyzes the incoming data, and breaks it down into its most granular form for processing. Master data techniques are then applied to associate the data records. Cleansing rules and other data transformations enable a comparison of differing values across multiple sources and create a purified data record. Once this evolution process is complete it provides a thorough, auditable account of a company’s IT infrastructure and data, enabling it to make informed decisions about the future needs and relevancy of all its assets while setting the company on a solid, verifiable governance track.