Data for Data’s Sake? Don’t Do It!

I’m passionate about data – but only relevant & believable data. Six Sigma followers know relevant data to be that which causes a new decision to be made or actions to be taken.   

In business, and especially product development, things can sometimes look black & white. It’s on/it’s off. It works/it doesn’t. We hit the target/we missed, etc. This disciplined approach helps when you’re building (or measuring) something. Like motivating you to read the instructions when putting something together. Or programmatically testing new software so you don’t miss any errors. 

But used in the wrong places, that rigorous approach can divert focus from the primary purpose and slow down success. Consider these examples I've run into recently:

·      A price look-up table is required to do sale calculations. Do you add the entire product set, even retired, or spun off products?

·      A product has been renamed and needs to be changed in the system/s. Do you go back and clean all history, even non-active records which might only show up in random reports? 

·      An employee rate table is needed because some of them work on capitalizable products. Do we enter the entire department just in case?

I’ve seen many initiatives get bogged down with practices which focus on data completeness rather than data relevance. And once you commit to completeness, keep in mind the ongoing effort to keep it current! 

So every time I build a table, analysis or process I first ask – what’s relevant? Then the execution path can be designed. If an entry is missing later, I let the process tell me by failing. The missing data can quickly be added and the process re-executed. 

This approach has resulted in getting initiatives in place quicker and delivering value faster. One of my driving principles (obviously inspired by Stephen Covey) came from overcoming this tendency on my teams – “Begin with the end in mind - even if you’re already in the middle.”

But not everyone is comfortable doing this. 

Let me know what you think.