7 Quality Dimensions

In order to improve data quality and maximise its insight potential, the Forrester report identifies seven quality dimensions that marketers should align their data across for best results:

  • Timeliness: Timely data comes from sources that are up to date. Access to faster data enables relevant insights that meet business objectives
     
  • Completeness: Complete data records are ones where all expected attributes are provided. A complete customer and marketing data set ensures that all behaviors, intentions, permissions, and sentiments are captured for robust analysis, such as understanding channel halo effects or how customers feel about your brand
     
  • Consistency: Consistent data references a common taxonomy across platforms, channels, and campaigns. Having consistent data for things like campaign codes and customer identifiers can help marketers speed up the data collection process and analyse trends over time, without worrying about data being labeled correctly
     
  • Relevance: Relevant data directly relates to the analysis being performed. Adding a slew of data into the system won’t help solve the business problem if it’s not relevant. Relevant data helps answer marketing business problems, address customer behavior questions, and make day-to-day decisions
     
  • Transparency: Transparent data refers to data whose sources are easy to trace and identify. Marketers who understand the data nuances from first-party and media sources, such as ad servers, will be able to determine if specific streams of data are necessary for their marketing performance analysis
     
  • Accuracy: The adage “garbage in, garbage out” has never been more relevant in today’s data-rich world. Only accurate data can reflect true actions
     
  • Representativeness: An important part of targeting, representativeness ensures data collected and leveraged for insights accurately reflects the marketplace or an advertiser’s target audience