“The Data Should Already Be Clean” — Famous Last Words Before Every Analytics Disaster

Read time: 2.5 minutes

Why “Clean Data” Is Mostly a Myth

1. Business data has been created for operational use, not for analytic use.
👉 Reporting has typically not been a primary focus.

HOW TO FIX: Design your systems with data accuracy standards from the very beginning.

2. Excel is a tool that allows teams to consistently break business processes.
👉 Manual changes can affect consistency in a matter of minutes.
HOW TO FIX: Minimize your dependency on spreadsheets wherever feasible.

3. Different departments have very different definitions for the same data.
👉 “Revenue” is a highly variable metric based on the department defining it.
HOW TO FIX: Establish a set of standardized definitions for key performance indicators across all departments.

4. Dirty data will typically scale at a faster pace than clean data ever could.
👉 The use of automation typically compounds the number of errors.
HOW TO FIX: Make sure that you validate any data prior to automating your data pipelines.

5. Data cleaning is not a separate task for many organizations.
👉 Data cleaning is the primary responsibility of every employee.
HOW TO FIX: Look at the quality of your data as infrastructure instead of merely a clean-up.

💡Key Takeaway: 

The majority of analytics issues originate from messy processes embedded in normal business operations rather than from ineffective dashboards.

The truth is that clean dashboards are created from clean systems, not better visualizations.