When it comes to data quality, it’s the last mile that counts - That last mile is Operational Data Quality (ODQ)

By Michele Goetz, Director of Product Marketing, Trillium Software

Today a significant gap exists between how IT managers or employees, and that of line-of-business executives, view the overall quality of data, according to a recent study by Forbes Insights.  This chasm exists because there is a missing link between what data quality projects are designed to achieve vs. what the business expects them to achieve. 

In light of this, alignment of data quality projects needs to provide tangible business benefits that are tracked through key performance indicators rather than though data quality indicators alone.  Both IT and business users must understand that data quality success is intimately tied to business success.  The context and use of data is required when approaching data quality.

In the past, the easiest route for most companies was to approach data quality within the data warehouse, installing the black box that runs on a server tucked away in a back room. While effective for a specific task, this approach leads to a lack of enablement of data quality policies across the enterprise. 

In fact, we hear from customers that instead of improving their data quality services within their quality solutions, they turn to quality procedures that are implemented within ETL processes. This approach, in many cases, conflicts with policies within the original quality services causing further degradation rather than improvement.  Lastly, the approach is also entirely reactive, causing business processes to be less effective and efficient.

The solution is to employ Operational Data Quality (ODQ).   ODQ practices happen within a business process deploying data quality services at the point of data entry, and usage within an application and between applications.  There are a number of benefits in this approach: 

•    Data quality policies are designed and aligned to business process improvement and results, providing transparency and immediate understanding of the linkage between data quality and business goals;

•    Data stewardship and standards are maintained and remedied by data stakeholders that are most impacted by degradation, as well as having the most expertise on how to improve the data according to business needs;

•    Improvements are made as they happen and quality of data migrates throughout the enterprise, allowing improvement beyond the immediate process;

•    Success in one business process provides a framework and justification for investment across the enterprise in data quality initiatives;

•    Data Governance initiatives benefit through Operational Data Quality practices by addressing data quality policies within the context of a business process that already has roles, responsibilities, and accountability.

The quality of data is no longer just an aspect to satisfy executive level reporting and analysis.  Companies are increasingly relying on their data to make decisions within processes such as customer on-boarding and off-boarding, risk assessment, and supply chain management.  This requires increased certainty that the data available supports next steps in a process and decision to make these steps.  Rolling out ODQ within your business is imperative to your business success.

Comments

[Julian Schwarzenbach]

Michelle,

A good post which people would do well to take note of.

Implementing any kind of 'fix' or corrective practices remote from the point of data entry will not change the behaviours of users. Arguably, they could make data quality problems more prevalent as staff may increasingly believe that the 'black box' will correct their errors for them so they may get increasingly careless when entering data.

Such approaches tend to condone and increase a culture of non-compliance which can lead to many wider business problems than data quality ones!

Julian