A Configuration Management Database is only as good as the data inside it. Unfortunately, most CMDBs are filled with
data that's outdated, inconsistent, or incomplete.
This report highlights the findings of a close look at the data sources and patterns in the IT hardware and software
market. What a careful look at the numbers tells us is this: data in most CMDBs is not clean - and without clean data, you cannot get the
results you want and need from your CMDB.
The problem isn't the CMDB software or the processes you use to populate and manage the CMDB. Nor is it the fault of the
vendors of the various assets. It's simply an unfortunate side effect of the complex, dynamic IT world that we live in today.
In fact, according to Gartner, Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits.
This whitepaper will look at why clean data is essential for you CMDB and how to cost effectively accomplish this goal
Bad data is like a bad habit - it drains your resources while you're not looking. Businesses are losing millions of dollars every year due to bad data. But most organizations either don't think they have a data quality problem, or don?t understand the actual financial impact of bad data.
This white paper will look at what bad data is, what it is costing you, why your current solutions are not working, and how you can solve the bad data problem, with specific focus on IT product data.
IT decision support impacts all aspects of technology management, from governance and strategy to budgets and
resource plans. The effectiveness of IT decision support all too frequently falls prey to data-driven challenges - like
overwhelming volumes, heterogenous data types, and complexity - that make it difficult to understand the data in
context. Just as the cobbler's children in the old fable had no shoes, IT leaders are falling foul of the very same decision
support challenges that IT works hard to avoid across their firms' other organizations. And the really bad news: This
challenge is expected to get worse - and to spread beyond the IT organization.
In the summer of 2013, BDNA commissioned Forrester Consulting to assess how data impacts the effectiveness of IT
decision support. To that end, Forrester developed a survey to test the hypothesis that data challenges limit the
effectiveness of IT decision support - and that dealing with these challenges will lead to large companies depending on
To test this thinking, Forrester conducted surveys with 115 US senior-level IT decision-makers (IT and corporate
management leaders) - adding in-depth follow-up interviews with some of them. Forrester found this hypothesis to be
true: Data-driven challenges limit the effectiveness of IT decision support - and many users expect to turn to thirdparty
vendors for help.