Dr. Thomas C. Redman, the Data Doc and President of Data Quality Solutions,has written another book that tackles the same issue his firm regularly deals with: Data Quality. Joining his previous works, which include Data Driven: Profiting from Your Most Important Business Assetand Data Quality: The Field Guide, isGetting in Front on Data. This time around, though, Dr. Redman is talking more about the people and the roles they should play in achieving Data Quality rather than providing a step-by-step how-to guide to the topic. DATAVERSITY® had a chance to speak with Dr. Redman, a contributor both to our website and events, about the new book and the help it offers business and IT leaders who want to make an impact on Data Quality in their organization.
DATAVERSITY (DV): Tell us a little more about the impetus for writing this book.
Dr. Redman: In the past I mostly focused on the “how” of doing the work of Data Quality. Here I wanted to focus more on the “who does what” to achieve Data Quality. With the right people and structure in place, the fact is that Data Quality improves really, really fast. So in this book I try to tease out how you get the right people in place – who they are, where they come from, what sets those who approach Data Quality properly apart from those who don’t – and tell the story from the organizational viewpoint.
DV: What data issues continue to drive the need for business and IT professionals to have resources, such as your past books and now Getting in Front on Data, at their disposal?
Dr. Redman: Bad data is like a virus. You can start at the bottom of the organizational chart and find someone infected by it. For instance, there’s the person who’s charged with fulfilling an online order but the address is wrong. That’s a problem for that person who is doing real work in realtime and who may or may not be able to correct it. Crank it up a bit and there’s the large class of knowledge workers who may spend up to 50% of their time looking for data they need or correcting simple errors or searching for confirmatory sources for things that look wrong.
Further up the organizational chart you’ll meet a senior executive who resembles someone whose story I tell in the new book: This person asked me to explain to him Data Quality in simple terms. I asked him to remember the last time he made a big decision and whether he trusted the data it was based on. He thought about it a minute and then he said that he didn’t think he ever trusted very much of the data he uses for making decisions. It was just his job to try and steer through it all.
So just up and down the organization people run into bad data all the time. They do their best to accommodate it and sometimes they do that well. But a lot of time they don’t and the results could be anything from sending a package to the wrong address and irritating a customer to making a bad business decision that has enormous cost to the company.
Bad data may simply be wrong data, like the address, or it may be poorly defined data – for instance, numbers in a report are represented as metric units but that isn’t clearly defined, so people assume they are English units and make decisions based on that assumption. Problems with unclear definitions happen a lot because companies so often are pulling data together from different sources and these sources don’t always have the same understanding of what, for example, a customer is.
DV: What changes, then, should take place in an organization’s staff to get these problems in hand?
Data Innovation Summit 2017
30% off with code 7wData
Big Data Innovation Summit London
$200 off with code DATA200
Enterprise Data World 2017
$200 off with code 7WDATA
Data Visualisation Summit San Francisco
$200 off with code DATA200
Chief Analytics Officer Europe
15% off with code 7WDCAO17