Money and resources wasted; sales missed; extra costs incurred. Recent research by industry analyst firm Gartner shows that the shocking price that companies are paying because of poor quality data adds up to a staggering $8.2 million annually.

That number is the average loss estimated by the 140 companies Gartner surveyed in August 2009. Twenty-two percent of respondents thought it was closer to $20 million and 4 percent even put the figure as high as $100 million.

It’s a sobering thought. But do business managers really appreciate the scale of the problem? Not according to a 2006 survey from The Data Warehousing Institute. More than 80 percent of the business managers surveyed believed that their business data was just fine, yet half of their own technical people took a very different view to their executives.

The truth is that few business managers appreciate the extent to which data quality issues impact their companies since typically no quantified measurements are made. This means proposals for data quality improvement projects fall on deaf ears.

To have any chance of budget approval or executive buy-in, data quality project proposals need an assertive business case. They need the backing of metrics that communicate evidence of a real problem that business managers readily understand -- a problem that poses a risk to the business and to the key performance indicators by which its success is measured.

Make the Case with Data Quality Metrics

By developing a program of data quality metrics, measurement and regular reporting, organizations can build increased awareness of what data quality means for the business. Metrics can help demonstrate what risks or issues might be presented by any decline in data quality levels as well as what opportunities might be gained by investing in improvement. Metrics also support objective judgment and reduce the influence of assumptions, politics, emotions and vested interests.

It is important to note that there’s no point in measuring and reporting on all of an organization’s data or every aspect of that data -- be selective. A metric showing that 9 percent of customer records in a marketing database lack a middle name is likely of little consequence to KPIs.  But if 5 percent are missing a postal code, then it could be of some importance, because if one million mailings are sent annually, then 50,000 would be returned. And if the metric referred to a billing database, it could make a strong business case for a data quality project, because invoices worth millions of dollars might not be reaching customers – thus delaying or even threatening receipt of revenue.

Where to Measure: Key Processes

For most organizations, business KPIs and the executive decisions aligned with them will most likely relate to cost, revenue, profitability, procurement, logistics, products, customers, suppliers and other important assets. Identifying the processes supporting these KPIs, the data required for these to operate effectively and the quality of that data enables organizations to determine the impact of poor quality in tangible terms. The result is an improved ability to gain business understanding and support for building the business case for data quality.

For example, you might establish that:

  • With direct print and mailing costs being significant and increasing, and the CEO keen to show green achievements in the annual report, the current 100,000-plus pieces of mail returned per annum must be cut down. Customer address data accuracy is a key issue.
  • In the last six months, 8 percent of online customers had to wait longer for delivery than expected, despite the products being in stock. Product codes in the order system are inconsistent with the stock system, requiring manual inspection and resolution.
  • Senior management is considering rationalizing product lines. But the sales ledger reports show discrepancies with the marketing department’s business intelligence system. Management cannot trust the cost/sales figures, delaying decisions and incurring unnecessary costs.

What to Measure: Dimensions

After identifying which data to produce metrics on, the next step is to define which of the many aspects of its quality to measure. These dimensions might include:

  • Structure: Is the data in the right format for it to be usable?
  • Conformity: Does it comply with critical rules?
  • Accuracy: Does it reflect the real world?
  • Completeness: Is business-required information present?
  • Timeliness: Is it sufficiently current?
  • Uniqueness: Are duplicate records creating confusion?
  • Consistency: Is the data the same, regardless of where it resides?
  • Relevance: Is it useful to the business in its pursuit of objectives?

Defining which dimensions are important, prioritizing them and producing data quality metrics that are meaningful for business owners is typically the job of one or more data stewards. Data stewards are individuals who understand the key business processes, the role of data in those processes and the intricacies of what makes good data.

How to Measure: Using Rules

Having determined the data to be measured and which dimensions to measure by, it is then possible to build a set of data quality rules against which to profile the data and compute compliance metrics. For example, if repeat sales from customer service representatives are key, and customer contact information must be present and accurate for the CSRs to make sales calls, then perhaps completeness of fields at original purchase is important, together with accuracy and structure of telephone numbers. If customers are waiting too long for the delivery of goods that are supposed to be in stock at time of order, then data consistency should be measured  because there may be a disparity between product codes in the order processing system and the warehouse stock and dispatch system.

In producing metrics, it’s best to be very focused at first, concentrating on just a few areas where data appears critical to business performance. It’s also better initially to generate just a small number of metrics on important characteristics that have real meaning to business managers in their roles and responsibilities.

Indicating and proving that the business cannot be confident in the data it relies on for certain important processes, decisions or compliance reports should justify further investigation. Success in one area can then be used as a reference to help communicate the value that could be won from metrics in other areas of the organization.

Making Data Quality Metrics Relevant

Once the use of data quality metrics becomes accepted and management backing is secured, extend the practice to cover wider data sources and report in more detail. It’s then important to define who requires what level of reporting.

Management may desire a high-level overview, so a data quality dashboard will be helpful. The dashboard should be able to group and aggregate scores into meaningful metrics associating data quality with key business processes or functions. It should also provide drill-down functionality to multiple levels of detail and permit custom reporting, allowing business owners, data stewards and data quality analysts to visualize data quality metrics and trends appropriate to their needs.

A facility to monitor and measure data quality over time is fundamental too. Only then is it possible to prove that investments in data quality are making a difference.

Data quality metrics, when aligned to business KPIs have the power to increase business user awareness, understanding and support for data quality investments. Using data quality metrics to identify the real issues that impact cost, revenue, profit or other important business metrics will most certainly help drive the business case for data quality improvement.

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