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Category Archives: data quality methodology
As expected, customer data quality remains at the top of list with regard to having the most issues. Ironically, this domain has been at the forefront of the data quality industry since its inception.
One reason for the proliferation of concerns about customer data quality could be its direct link to revenue generation.
Whatever the reason, this poll seems to indicate that services built around the improvement of customer data quality will be well founded.
Once again there are no surprises when looking at what data improvements are desired. Data owners seem to be interested in a centralized, synchronized, single view of their data, most notably customer.
The good news that can be gathered from these polls is that as an industry, data quality is focused on the right data and the right functionality. Most data quality solutions are built around the various aspects of customer data quality and ways to improve it so there is a master managed, single version of a customer. The bad news is we’ve had that focus for quite some time and data owners are still concerned.
In my opinion, this is due to the nature of customer data. Customer data is at the core of every business. It is constantly changing both in definition and scope, it is continuously used in new and complex ways, and it is the most valuable asset that an organization manages.
One thing not openly reflected in these polls is that it is likely that the same issues and concerns that are present in the customer domain are also present in the employee and contact domains. However, they tend not to “bubble up” to the top of list due to lack of linkage to revenue and profit.
I’d encourage comments and feedback on this post. If we all weigh in on topics like this, we can all learn something valuable. Please let me know your thoughts on the poll results, my interpretation of the results and opinions.
Recently a reader asked me if I had any posts on “data quality basics”. Turns out, I didn’t. So I’ve decided to put together a series of posts that covers what I feel are the basic essentials to a data quality program.
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The many sides of data quality
It is generally accepted in the data quality world that there are seven categories by which data quality can be analyzed. These include the following:
- Conformity – Analyzing data for conformity measures adherence to the data definition standards. This can include determining if data is of the correct type and length
- Consistency – Analyzing data for consistency measures that data is uniformly represented across systems. This can involve comparing the same attribute across the various systems in the enterprise
- Completeness – Analyzing data for completeness measures whether or not required data is populated. This can involve one or more elements and is usually tightly coupled with required field validation rules
- Accuracy – Analyzing data for accuracy measures if data is nonstandard or out-of-date. This can involve comparing data against standards like USPS deliverability and ASCII code references
- Integrity – Analyzing data for integrity measures data references that link information like customers and their addresses. Using our example, this analysis would determine what addresses are not associated with customers
- Duplication – Analyzing data for duplication measures the pervasiveness of redundant records. This involves determining those pieces of information that uniquely define data and identifying the extent to which this is present
- Timeliness – Analyzing data for timeliness measures the availability of data. This involves analyzing the creation, update or deletion of data and its dependent business processes
Data Quality Program Best Practices
Implementing an enterprise data quality program is a challenging endeavor that can seem overwhelming at times. It requires coordination and cooperation across the technology and business domains along with a clear understanding of the desired outcome. A data quality program is fundamental to numerous other enterprise information management intiatives, not the least of which are master data management and data governance. In fact, you’ll recognize some of the same best practices from those disciplines.
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1. Establish Sponsorship from the Business
Gaining business level sponsorship for the data quality program is essential to its success for many reasons. Not the least of which is the fact that poor data quality is a business problem which negatively impacts business processes. Sponsorship from the business provides a means for the communication of these problems and impacts. Business level sponsorship should be built upon data quality ROI and provide the direction for what data resides in the scope of the data quality program.
2. Establish Data Stewards
Data stewards are business level resources that represent individual data domains and provide relevant business rules to form remediation steps. They also develop business relevant metrics and targets that enable the measurement of quality and trend reporting that establishes the rate of return on existing remediation techniques.
3. Establish Data Quality Practice Leader
Data quality is not a one-and-done project. It is a cycle of activities that need to be continuously carried out over time. Enterprise data tends to be an evolving and growing asset. Assigning a leader, or set of leaders, to a data quality program ensures the data quality cycle of activities maintains consistency as the data landscape undergoes this evolutionary growth.
4. Establish Data Correction Service Level Agreements
Defining service level agreements with the business data stewards provides a basis for operational prioritization within the data quality program. As new data defects are discovered it will be critical to determine which to schedule for implementation. Service level agreements provide direction from each business unit and enable appropriate change management scheduling.
5. Tightly Couple Data Quality Processes with Data Management Practices
Common data management practices such as data migration and data archive scheduling need to be taken into account when determining when and what data to assess and remediate. Profiling and assessing data which is scheduled for archival would be an egregious misappropriation of resources. By aligning the data quality program with these types of data management activities this type of mistake can be avoided.
6. Approach Data Quality Proactively
A proactive to data quality increases data consumer/business confidence, reduces costs associated with unplanned data correction activities and fines associated with failure to meet regulatory compliance. A proactive approach also establishes, with the business, a level of domain expertise that fosters the necessary buy-in. Fundamental to this concept are data quality assessments and data quality trend reporting (score-carding).
7. Choose Automated Tools over Manual Methods
With the maturation of the data quality vendor market, it is now possible to implement enterprise capable data quality software that offer a full range of features to management data defect identification, remediation and reporting. Automated tools are more comprehensive, consistent, portable, and include built-in modules, such as address validation services, which reduce code development.
8. Establish Data Metrics with Targets
Metrics, and their associated targets, are the cornerstones to developing an assessment and remediation process that fosters affective change. The definition of metrics and their targets needs to be centered on data elements that are essential to the core set of business processes. Targets should be divided into three groupings which are reflective of ability to support these processes. At a base level these groupings should be “does not support business function”, “minimally supports business function”, and “supports business function at a high level”.
9. Address Data Quality Issues at the Source
With numerous applications consuming and delivering data across the enterprise, it can be a daunting task to decide where to start correcting quality deviations. In an effort to reduce this complexity, it is a best practice to institute data quality activities where the data originates. The origin point of data is commonly referred to as the system of record.
This practice not only provides an answer of where to begin implementing data quality practices, it also proactively addresses the issue of defect proliferation. This ensures that these activities are not duplicated numerous times and are implemented in a consistent manner. As a consequence, data needs to be measured for quality upon creation and/or migration into the data landscape.
10. Focus on Mission Critical Data
Focusing data remediation efforts on mission critical data is the control that ensures a return on the investment of the program. Identifying the data that support core business functions requires careful examination of the process and participation from the business data stewards. Often times this process also requires prioritization of critical elements in order to schedule remediation efforts. The identification of this data is vital to the success of the data quality program.
While there are many more best practices in the data quality domain, these ten form a solid foundation for the implementation of a data quality program. This practices, as you may notice, are more focused on establishing a data quality program rather than the remediation efforts within the program. In a future post, I’ll examine some common remediation techniques which are universal to data quality programs.