Data quality strategies are sustainable over the long run when they are approached with programs that provide a framework for data-centric projects. However, the absence of such programs in your organization should not make you exempt from embracing data quality as an important part of your marketing database. If you are building your marketing database, or are re-evaluating an existing one, consider the following items as you drive data quality into the plan:
Data quality is relative. In physics, the ‘Theory of Relativity’ posits that all motion must be defined relative to a frame of reference. The same holds true for data quality; your baseline should be measured relative to the purpose of its use. In other words, data lacks quality to the extent it does not satisfy the intended use.
Evaluate quality from different angles. Data have many properties that define its quality, and these properties will likely vary greatly depending upon where the data originates from and whether those sources are verifiable and accurate. Suffice to say, to better understand and gauge the overall quality level of the data you are working with, you should focus on the properties that are most important to the marketing programs leveraging the data. Accuracy, timeliness, completeness, reliability, relevance and clarity are good measures when assessing data quality.
The journey is the destination. Teams embracing data quality at a systemic level from day one and maintaining it throughout the lifecycle of the system will make a greater overall impact on the efficacy of the solutions they are building. This is based on the premise that data sources, rules and uses will shift over time. As such, data quality cannot be a static measure and must be revisited regularly. In contrast, organizations who engage in data quality initiatives as one-time side projects, are more likely to experience limited benefits with a short lifespan. After you understand which aspects of data quality are relevant to your marketing strategy, do not limit yourself to just initial data quality analysis. Instead establish measurable baselines of quality and track progress in a way that identifies gaps and facilitates defect prevention.
People, process and technology. You need the right balance of skills, technology and processes in order to be able to implement a sound data quality framework; a weakness in any of these areas will prevent your team from achieving sustainable success. Some companies naively believe that investing in best-of breed tools alone is the key to solve data quality issues, failing to understand that a tool by itself does not represent a solution. As you plan your data quality efforts, be sure to create the balance between all three focus areas; people, process and technology.
