Before we answer that question, let’s clarify the context. Bad data leads to bad forecasts, which erodes trust in every number you present. To combat this, many believe you must first introduce more disciplined data management practices, including cleaning your data, connecting your systems, and enabling everyone from finance to operations to work from a single source of truth.
While that’s all good advice, the statement that “you can’t automate bad planning data so you must start with clean planning data” is false!
Surprisingly, the best-practice approach to fixing your planning data is to use modern planning technology that will reveal the problems in your data, accelerate data cleansing, and improve planning accuracy.
As a business advisor, I’ve never had a client who was able to get all their data “squeaky clean” before they began their planning process automation journey. My advice is to prime stakeholders to expect dirty data in the beginning, start the journey, and clean the data along the way. Yes, it’s a bit like building the aircraft while you’re flying it, but clients who take this approach get off the runway faster than those who don’t.
