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Can we please address the elephant in the room?

And talk about data quality?

It seems we are circling around it, talking about the right tools, the right structures of data bases and lakes, the data analysis and of course my personal favorite: Data Storytelling!

But what is the big data and all its supposed insights and shiny presentation decks if the data quality is bad?

I know! I’ve been there! I had to manually clean up project master and forecast data to get to the insights I needed to drive the business. With 450+ active projects in the portfolio on a mostly manually maintained excel sheet.

And it is a pain in the backside.

When the data lake came in, it took us hours and hours of plausi checking, correction, training and purpose sensitization to get the people there.

I always had to remind myself how it was, when I was the one entering data into the ERP and other tools. I was taught a basic idea, but it was not really checked and there was no focus on it. What was different then?

It was a different time. It wasn’t so crucial what data was in the system (other than the audit relevant data). If there was a major question on mass effects in the project portfolio, we would get an excel table from management to fill manually with the required data.

That needs to change - still. Data should be touched only when it changes.

All global requesters should then grab the data and should be able to analyze it from there, without harassing the data suppliers again.

And potentially, data lakes make that possible even though data suppliers are decentralized and spread over the globe.

Mind you, the job is not over after the mapping is done!

Data suppliers neeed to understand how to enter what data correctly. If not, as a business leader who needs to analyze the data, you will spent late nights at the computer trying to make sense of senseless information.

I had the chance at one point to speak to everyone in the finance community who was entering data into the systems of the company I worked for. And storytelling helped me to convey this message.

“Imagine a project is a basketball game. We are playing 450+ matches at the same time. Each in a different status. We need to know whether we are winning, what one team can learn from the other and how to analyze data of our opponent to use it in the another match. And most of it in real time!

We have to observe the statistics of each game, imagine a scoreboard. We combine all 450 scoreboards to one (dashboard) to see if we are on track with all our strategic decisions and then we can drill down to each individual one to focus on those games where we are not performing too well on to support the team. We can identify trends and learn from failures and successes.

But it is all worth nothing if the data - YOUR data - is not correct! We need you to be precise! We need you to win the game(s)! You are a crucial part of our success!”

So help the data suppliers to help you. Explain and train. Sensitize and motivate.

When we do not have to talk about whether or not the data is correct, we can finally talk about content and make the right conclusion for our businesses.

If data insights are strategy, then data quality is culture. And we all know: culture eats strategy for breakfast.

Source picture: Adobe Stock

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