Data Doghouse - performance management, business intelligence, and data warehousing

Seven Misconceptions about Data Quality

The narrow definition of data quality is that it's about bad data - data that is missing or incorrect. A broader definition is that data quality is achieved when a business uses data that is comprehensive, consistent, relevant and timely. If you focus only on the narrow data definition you may be lulled into a false security when, in fact, your efforts fall short. We will address several more misconceptions about data quality.

In order to fix a problem you have to recognize you have a problem. According to recent Gartner research, 25 percent of Fortune 1000 companies are working with poor quality data. The Data Warehousing Institute (TDWI) estimated that data quality problems cost U.S. businesses $600 billion each year. Regulatory initiatives such as Sarbanes-Oxley and Basel II dictate that companies must provide transparent data. But even with the documented high costs of poor data quality and the tight regulatory environment, many companies are turning a blind eye to their data quality problems. Why? Perhaps it is because of their mistaken belief that bad data is the only data quality issue they need to worry about.

A corollary to the above: to fix a problem you first have to take responsibility for it. That's the rub. Taking responsibility is the biggest roadblock to dealing with data quality. In order to achieve a high level of quality, data has to be viewed from an enterprise and holistic perspective. Data may be correct within each data silo, but the information will not be consistent, relevant or timely when viewed across the enterprise. To make matters worse, you've got each report or analysis interpreting the data differently, so even when the numbers start off the same in each silo, the end results will not be consistent. Data is a corporate asset and has to be consistent across the entire corporation, not just within the business function or division where it originated.

Misconception #1: You Can Fix Data

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Maximizing the value of on-demand Business Intelligence for Small and Medium Enterprises

View my latest webinar on the value of on-demand BI for small and medium enterprises (SMEs).

ABSTRACT:

Small to medium enterprises (SMEs) are just as concerned with maximizing the value of their data as large enterprises. This webinar will explore the business and technology trends that have made BI accessible to SMEs.

Historically, large enterprise firms were the early adopters of BI and they paid top dollar for it. Today BI is mainstream at most large enterprises. However, in the past when SMEs tried to buy into these large enterprise versions of BI, they were only met with frustration and unsuccessful project implementations.

Things have changed and now there are many options for on-demand BI for SMEs. Listen in to learn about the latest BI trends that benefit SMEs, including self-service BI, integrated BI suites, increased integration capabilities, more effective analytics and cloud BI.

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Be Prepared to Duel with Data Quality

Plenty of business intelligence (BI) or data warehouse projects have been blindsided by complications related to data quality. Sometimes these issues aren't apparent until business users start testing the systems just before going live with the projects. What causes BI project teams to get caught off guard by data quality issues? Why do these problems surface so late in the projects?

There are two common pitfalls: defining data quality too narrowly and assuming data quality is the responsibility of the source systems.

People often assume that data quality simply means eliminating bad data - data that is missing, inaccurate or incorrect. Bad data is certainly a problem, but it isn't the only problem. Good data quality programs also ensure that data is comprehensive, consistent, relevant and timely.

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A must to avoid: Worst practices in enterprise data governance

More and more companies are recognizing that they’re accumulating ever-increasing amounts of data but not necessarily gaining business insights from it. The missing link is the transformation of data into information that is comprehensive, consistent, correct and current. That isn’t a problem technology can solve for you: The key step is establishing a data governance program to help your organization truly treat its data as a corporate asset by enforcing consistent definitions, rules, policies and procedures.

That’s the goal, at least. Although enterprise data governance efforts have been launched at many companies, the success rate of these initiatives hasn’t been encouraging. There’s a lot of advice available on data governance best practices that should be adopted. The following discusses the top “worst practices” and pitfalls that enterprises need to avoid. Consider it a roadmap of red flags to alert you that your governance program may be heading down the wrong path.

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