Don’t Let Data Become a Problem … Like a Stack of Dirty Dishes
If your family is anything like mine, your kitchen occasionally gets a little bit … well, messy. It’s not that we don’t appreciate a tidy kitchen; it’s just that we get busy with other priorities and the little things start to pile up. Before we know it, there aren’t any clean plates and we’ve got a sink stacked full of dirty dishes and a cluttered countertop. The kitchen is a mess, and we find we have to spend our weekend cleaning it up.
My dirty kitchen is a great analogy to the condition of data in many of the enterprises my team has worked with. Even at companies that are the most attentive to data quality issues, we’re all bombarded by projects that come up and divert our attention. One little issue here, another small fire drill there, and before you know it, your organization’s data is riddled with inconsistencies and errors. Ultimately this starts to erode the confidence your teams have in the data they rely on.
So, what’s to be done? It’s never too soon (or too late) for a new year’s resolution. Here are five things you can be doing so you don’t have to compare your company’s data to a sink overflowing with dirty dishes.
Start by cleaning up your existing mess the best that you can. In my kitchen, this means loading the dishwasher with the dirty plates, glasses, and utensils and running a cycle. For data, this means identifying the easiest-to-fix, most broadly observed issues and addressing them with a one-time update that corrects the problem. For example, are your ZIP Codes missing leading zeros? Tack on the zero! Do you have phone numbers that are all 9s? Blast them away!
Move On to the Bigger Issues
Once I’ve begun to make a dent in my dishes, I’m going to work on the pots and pans next – paying special attention to each item, getting the scouring sponge out, and making sure baked-on food and grease gets removed. Similarly, with data, we can look for a few tougher-to-solve issues that have a big impact and clean those up, too. The 80/20 rule applies here – we’re not trying to return every pot back to its pristine state or how it looked when we first bought it, and we’re not trying to get every record perfect, but if you can weed out those bogus emails such as ‘firstname.lastname@example.org’, or get rid of ‘Mickey Mouse’ and ‘Donald Duck’ in your contact fields, you’ve made some progress!
Now it’s time to clean off the countertops! I’ll put the spices back where they belong, put the bagels back in the breadbox, and wipe down the work surfaces and sink. I want to give myself a clean surface for safer food preparation. It’s also more pleasurable to work in a clean, uncluttered space, so I’ll have an incentive to keep things clean in the future. With our data, we can do something similar: Wipe out old or unused records, leaving only what we need to accomplish our business goals. Have a prospect whose phone is disconnected, an email address that bounces back, or a customer who hasn’t purchased in years? Try deleting or archiving those records so you can focus on where the value is.
Make a Plan
Next, it’s important take a look around the kitchen and evaluate how it looks. Is the faucet dripping? Do I need to call in a plumber? Would the seating area look better with a fresh coat of paint? Similarly, take stock of where you are with your data. Are there outstanding issues? What kinds of resources will you need to start addressing them, or are they things that can wait? Maybe you’ve noticed that some data has been arriving with occasional strange characters lately, or that you haven’t updated your sales territory assignments in a while. Commit to getting these important tasks done, and figure out what and who you’ll need to help you.
Keep It Clean
Cleaning up my kitchen certainly makes me feel better, but unless I have a plan, it’s only going to revert to its former chaos. How can I keep that from happening again?
First, I set a protocol for me and my family; every dish we use has to be taken care of every day. It goes directly in the dishwasher or into the sink to soak. Then every night, I run the dishwasher and clean the pots and pans by hand. Every morning, I put them in the right cabinets before I start my day. We can do the same with data, building our data quality rules and stewardship capabilities into automation tools.
Second, I address smaller issues as they come up. If I see a glass looking clean but a little cloudy, I give it a good scrub by hand before it goes in the dishwasher. Similarly with data, if we notice a quality issue, we can queue it up for manual stewardship so that the issue and its impact don’t get lost.
And finally, I wipe the counter down every day so I can see what’s on it and feel like I’ve got a clean slate to start from every time. With data management, we should make our issues lists visible – not just to us, but to our stakeholders, too – so we all know what we’re dealing with and just how much of a mess we’ve got to clean.
Data Is Your Asset – Take Care of It!
A good set of dishes will last for generations -- we don’t use a dish or pan once and throw it away, nor do we wash it once and consider it clean forever. Our data is our asset and it requires regular care and maintenance, too. Keeping the little things from piling up keeps things humming while preventing bigger issues from developing the road.
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