Marketers in finance need the right data in order to run good campaigns, make the right decisions, and create effective marketing strategies.
Data takes many forms including campaign analytics figures, digital engagement data, survey responses, and data gathered across all digital channels. People’s views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. There are many definitions of data quality, but data is generally considered high quality if it is “fit for [its] intended uses in operations, decision making and planning”. Ultimately, data quality refers to the state of qualitative or quantitative pieces of information.
What does good quality look like?
Good quality data is data that is fit for purpose. That means the data needs to be good enough to support the outcomes it is being used for. Data values should be right, but there are other factors that help ensure data meets the needs of its users.
Quality of data content
Having good quality data does not mean every value must be perfect; good quality will be different for different data sets. Quality can be measured using six dimensions: completeness, uniqueness, consistency, timeliness, validity and accuracy. Different data uses will need different combinations of these dimensions; there are no universal criteria for good quality data. It is important to actively manage quality, and work to improve poor quality.
Quality of data processes
Having good quality data is a great start but it needs to be maintained. When data is moved or changed there is a chance for quality problems to be introduced. Automating manual data processes together with robust validation rules can prevent errors and improve consistency. Documenting the processes used in your data’s journey improves the organisation’s understanding and helps to ensure consistency when handling the data.
Quality of data sets
If we start with values that are right, and process them well, we also need to ensure that the data we collate, package and share as a data set is good quality. Providing data in an agreed format or specification ensures consistency and makes it easier for users to process and analyse further. All datasets should have metadata – information about the data that helps people understand what it is (and is not!).
Quality of analysis
Having the right data values, good processes and well created datasets gives us the best foundations for analysis. Continuing quality assurance throughout the analytical process helps ensure quality analysis. When analysis needs to be delivered under significant time constraints, it may not be possible to carry out full quality assurance checks.
Drilling down further, those expectations, specifications, and requirements are stated in terms of characteristics or dimensions of the data, such as:
- accessibility or availability
- accuracy or correctness
- completeness or comprehensiveness
- consistency, coherence, or clarity
- credibility, reliability, or reputation
- relevance, pertinence, or usefulness
- timeliness or latency
- validity or reasonableness
In practice, data quality is a concern for marketing professionals as people’s personal data changes on a regular basis from change of jobs and email addresses to their own communication preferences.