Indiscriminate Data Cleansing may be injurious to your companies financial health!

My recent experience of owning a customer database and actually living with it for a year and a half has given me some insight on data quality and need for data cleansing.

Typically when it comes to poor decisions in business, the often blamed entity is the data used to make those decisions. And it is true that over time data tends to become inaccurate. People move, things change, data goes missing. In the end conclusions become erroneous and models do not work.

Does that mean that the solution is to indiscriminately cleanup your entire database?

Many organizations try this at great cost. Sometimes the results can be profitable but in general it would not fetch a decent ROI. Often done as a mandate from the senior management, because money is there, no thought is put in regarding the best way to define this project and save money in the process.

I suggest a different approach.

Step 1: Segment your database. It does not matter how you segment it, objective is to find that 20% of your customers who are contributing 80% of the returns for the organization.

Step 2: Get your business leaders to define goals or business objectives targeting this segment for specific returns. For any company this would be the most important segment and if there is one project that must be approved and funded that year, it would be this one.

Step 3: Decide what existing and new data elements will be necessary to support the goals and objectives of the step above and target these for the cleanup phase.

In the next blog we will discuss further how to effectively cleanup the select data for your prime segment and do this at a budget your CFO would love.