Conducting an analysis is useless if the quality of your data isn’t up to par, everyone knows that. Any worthwhile data must be accurate, complete, consistent, credible, current and secure. But how can we avoid the notorious “garbage in, garbage out” cycle, which makes everyone lose time and money because of bad data?
Here are some tips to tackle the issue at the source and build strong foundations to correctly exploit self-service BI.
Verify data at the source:
If the data sources which feed your data lake or data warehouse are old and resistant to change, it is paramount to verify that there aren’t any duplicates, and that the data is coherent and concordant. These verifications will allow you to intercept any bad data before they pollute the rest of the pipeline, and/or alert users.
Solve issues in existing tables:
As IBM explains, a simple data profiling can give a lot of information about the data present in a table. If you detect an issue in the data, you can isolate it, find its source, and solve it. For this type of operation, dashboards are useful to visualize data and spot outliers, gaps, inconsistencies and biases. On the other hand, it’s recommended to industrialize this process to preserve data quality on the long term and save time.
Recreate poorly designed pipelines:
Data issues can sometimes come directly from your pipeline. Indeed, poorly designed pipelines lead in some cases to bottlenecks, thus slowing down data treatment. These pipelines can also produce errors and inconsistencies during complex transformations or integrations. Analyzing pipelines is thus crucial to detect this type of malfunction and maybe redesign the pipeline, to simplify it for example.
Use machine learning:
Training models with historical data makes ML useful to identify usage patterns and detect anomalies more rapidly. ML is also useful to automate some parts of data cleaning, like the addition of missing values or formatting correction.
Data will never be perfect (it is not what we want anyway) but putting a constant and precise control in place will allow you to avoid the pollution of the data pipeline and an alteration of the decision-making down the line. After all, when the data team wins, everyone in the company wins.
