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
