Data Master or Master of Disaster?by ps

Data management is the core of any Software asset management. The equation is clear: High-end data ensures high-end decisions, not only in SAM.  If SAM data does not satisfy some data quality criteria, decision making in SAM resembles more an heuristic approach to problem solving: practical and sufficient to ease the uncertainty of not-knowing and speed up the decision finding process but nevertheless no more than an educated guess.

Even if data quality is often an unnoticed or even unloved issue – there is no way around it. It is the fundament you build your “house of compliance” on. The better it is build the more stable. But data around soft- and hardware assets have long been a negligible component of everyday IT management as IT itself has been for a long time expected to “serve and deliver”. Nowadays, as information has grown into a strategic value and IT costs have risen considerably, information about IT assets themselves is often scattered in diverse data silos of disparate organizational departments presenting itself as hardly to interconnect and manage.  So what to do?

First things first: “You cannot manage what you do not measure”. Most organizations seem to be deaf and blind against efforts to measure the quality of their SAM data neither objectively nor quantitatively because they have no realistic estimation how much it affects targeted benefits. This is not SAM specific but an overall truth: Research estimates that 40% of the anticipated value of all business initiatives is never achieved (1). So introducing a metrics-based approach to assessing data quality in SAM helps remove perceptions and gut-feelings and building a business case for formal data quality improving efforts. Once the business value is tangible, start organizing your SAM data management both strategically and tactically- which means doing more than just implementing a tool (2). Last but not least: Reap benefits!

 


 

(1) Friedman T., Smith M.: Measuring the Business Value of Data Quality, Gartner 2011.

(2) Goetz, M.: Are Data Governance Tools Ready for Data Governance? Michele Goetz’ Blog, 2014

 

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