Improving data quality — this is how it works

As a first step, it can be useful to think about the status of the current data and to derive measures for further action. Because the better the data quality, the better the results (“garbage in, garbage out”).

How to improve data quality in your company:

• Define at the start Sub-goals, such as cleaning email addresses for a better delivery rate in marketing. This makes it easier to keep track and stay tuned.
• Get yourself one overview About the status quo: 
 What formats is the data you work with available in? 
 Is there any unused information you'd like to include?
• Review your data regarding topicality (Is the data still relevant for current issues?) , distinctions (Are all fields correctly named?) , completeness (Are there empty fields?) and syntactic correctness (Are all entered date fields, names, etc. in the same syntactic format?)
• Check your data for significantly different values, so-called “outliers.”
 Depending on the question, these can be very informative, but they can also falsify the entire analysis. Visualizations of your data can be very helpful here — using classic scatter or bar charts, you can Outliers swiftly identifying.

You can see more about the different forms of presentation and what each visualization is suitable for here.