Best Practice Enterprise IT Data Quality

As organizations wrestle with fast-growing data volumes, they are beginning to place responsibility for managing that data in the executive suite. According to Gartner, 90 percent of large organizations will have hired a Chief Data Officer (CDO) by 2019. While CDOs must control the costs and consequences of explosive data growth, they're also called on to help the organization capture, understand, and exploit big data for strategic gain. The first step in achieving this goal is to ensure that the organization is working with data that is standardized, complete, and up-to-date. This paper discusses how focusing on data quality for enterprise IT allows the CDO to improve data maturity rapidly and create early wins that support broader data quality initiatives.

Why Is Data Unreliable?
The success of IT processes and projects depends on reliable data, but unfortunately, much data is unreliable. In fact, Gartner estimates that more than 40 percent of business initiatives fail to meet their objectives due to poor data quality. Moreover, when process and project owners try to address data quality issues, they often do so in isolation, which further fragments data across the enterprise and makes it even more difficult for solutions to exchange information.

In enterprise IT, three persistent issues drive this problem:
Lack of vendor standards
Hardware and software innovation is moving at such a rapid pace that managing an enterprise IT portfolio is an exercise in chasing a constantly moving target. Every day brings new products,versions, editions, sub-versions, and bundles. As of March 2017, there were 649 new manufacturers added in 2017 who brought 1,045 new solutions to the marketplace.3 In addition, 85,626 new software and hardware models and products were introduced in 2017. As these products, from security to deployment to finance, make their way into the infrastructure, organizations must categorize and uniquely identify them to manage them effectively.
Hardware and software innovation is moving at such a rapid pace that managing an enterprise IT portfolio is an exercise in chasing a constantly moving target. Every day brings new products, versions, editions, sub-versions, and bundles. As of March 2017, there were 649 new manufacturers added in 2017 who brought 1,045 new solutions to the marketplace.3 In addition, 85,626 new software and hardware models and products were introduced in 2017. As these products, from security to deployment to finance, make their way into the infrastructure, organizations must categorize and uniquely identify them to manage them effectively.

Market velocity
In addition to tracking individual hardware and software solutions, organizations need to understand the shifting IT market and its impact on their IT portfolios. Clear visibility into enterprise IT data demands awareness of new companies,new technologies, and mergers and acquisitions, all of which can have a dynamic effect on rolling up total vendor spend.
Market velocity also pressures technology vendors to reduce time-to-market, regardless of the effect on data quality.Even within popular technology stacks, product groups racing to integrate applications introduce inconsistencies, like feeding data from a configuration management database to a software asset management repository.

Technology debt
The increasing cost of maintaining older systems further exacerbates enterprise IT data issues. Legacy systems that have not yet been retired contain legacy data that still has value for the enterprise. The company cannot avoid spending money to access this data. Its only choice is whether to invest in maintaining the legacy systems or instead to invest in translating legacy data into a form that new systems can process. IT organizations, under pressure to respond to business needs while remaining within budget, often release capabilities without developing comprehensive test suites or achieving clarity about business requirements. They then iterate to add capabilities rather than refactoring applications or configurations to meet changing functional demands or timelines. As a result, data is often trapped in silos, making every integration scenario more costly and complex

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