Are You a Data Quality Denier?

Organizations with data quality problems transition through several steps before they are ready to tackle the issues preventing them from reaching their business goals. The first stage is denying that a problem even exists. As more data enters the organization from an increasing number of sources, however, this head-in-the-sand attitude allows data issues to grow. Data quality denials eventually cause significant waste, errors, and re-work. At that point, companies can no longer ignore it.

The reasons for poor data quality are varied, making it easy for corporate executives to overlook:

• Data entry errors caused by employees, customers, partners, or vendor mistakes doom the data right from the beginning. When companies do not adequately monitor data input or neglect to use tools to normalize or correct data as it enters the organization, downstream remediation is much more difficult.

• Inconsistent data definitions across applications can lead to multiple data formats, each tuned to serve a specific purpose.
• Re-purposing data from one system to another can multiply the impact of bad data or prevent data from being loaded into some databases.
• Data decay is always a problem. Customer postal addresses are good examples. About 11% of the US consumer population moves every year. Companies rely on their outsource print and mail vendors for address verification so the mail gets delivered. However, the new address data rarely makes it back to the source database. Uncorrected address information foils attempts to merge files, a necessary step towards building “golden records”. Other data like sales volumes, product purchases, or email addresses also decay.

How Do You Know if You Have a Data Quality Problem?
Here are some symptoms of data quality issues. If you notice conditions like these in your organization, it is time to make a thorough data assessment (Firstlogic offers data quality assessments at no charge).

• Employees are inventing home-grown workarounds to deal with data inconsistencies.
• Reports generated from different systems or divisions don’t match as they should.
• An army of staffers goes over reports and charts and “fixes” them before passing along to media, investors, or company executives.
• You’ve had to reject requests for information or you made purposely vague statements because you didn’t trust the data.

Do You Really Need to Fix It?
Data quality isn’t just about the wasteful practices of paying for duplicate mailpieces or excess data storage. It isn’t confined to personalization errors. An organization might have tolerated a certain degree of dirty data in the past when organizations used data for a single purpose. Today, a piece of data might support many actions, most of them unpredictable. Flaws inconsequential in one case can severely impact outcomes in another.

Staffing decisions, site location selection, money management strategies, and inventory management are just a few examples of business operations adversely affected by unrecognized data quality issues. Once an organization discovers their data governance policies are not supporting their mission, they must address the issue. Continuing to deny the problem is irresponsible.

OK, Maybe We Have a Data Quality Problem!
Once your organization acknowledges it is time to address your data quality situation, your company must perform an assessment. You’ll want to understand the extent of the issues, set priorities for remediation, and estimate the ROI of improving data quality.

Firstlogic Solutions has identified the steps companies must take to ensure a successful data quality improvement strategy. Executive sponsorship is essential, as are clearly defined goals. We frequently work with our clients as they progress past the denial stage and proceed through a defined process towards successful implementation. We can help you work through your data quality challenges. Contact us to learn how.