Data Quality Therapy

Maggie Hepburn, Learning Experience Principal, LexisNexis:

 

A common challenge with our clients is staying on top of data quality. “We get too many tickets” or “we have so many duplicates.” Does it feel like you’re barely treading water? We’re here to say, you don’t have to feel that way, and we can help. On the InterAction Education team, we see this when we visit you and we’ve discovered over time that successfully managing your data quality is really a shift in mindset and prioritization. Control your data quality, don’t let it control you.

We’ve found that our clients needed to step back, take a breath, and focus on what’s important; slowing down to assess the situation and make a plan for moving forward by:

  1. Eliminating the Noise
  2. Identifying Priorities
  3. Getting into Maintenance Mode

 STEP 1: ELIMINATE THE NOISE:

The biggest enemy of feeling good about managing data quality is getting distracted, sometimes unknowingly, by irrelevant contacts, and feeling overwhelmed. There is just one thing you should be focused on: prioritized contacts, period. And the best way to focus on those is to first assess what else you’ve got in there and remove the noise. We can hear in our clients’ voices that they feel overwhelmed in having “tens of thousands” of contacts in their database, but are they all relevant? If you can narrow that focus to the ones that truly matter to the firm and business development, your often scarce resources and time can be better spent. Isolate and/or remove them using out of the box searches or custom searches. Time we spent with a client recently (one day) produced a 50% reduction in the overall contact collection (1.4m to 700k!) simply by deleting or archiving contacts that weren’t of relevance to the firm.

  1. How many contacts are not relevant at all? Personal contacts that accidentally got in there? Contacts with no, or very little data? Contacts no one knows? Where did they come from? Why are they in there? Consider getting rid of them.
  2. How many contacts are not relevant any longer? Contacts you haven’t engaged with in a long time? Contacts only known by alumni? Consider archiving them away.

 

Step 2: IDENTIFY PRIORITIES:

Once you’ve minimized the overall number of contacts you’re even starting with, you can now focus in on your priorities. We think of data quality priorities like the “pop-by.” Someone calls and says, “Hey, I’m in your area, can I pop-by?” After a brief thought of “ugh, I hate the pop-by,” you say, “sure!”   But then you look around your house. Do you tidy every single room? Or do you strategically tidy the few areas you know the visitor will be in?

Identifying priorities in your data quality is similar. There are things that require more attention, more frequently than others. That’s typically based on what your users look at the most, and what’s most relevant to business development. Start with your Contact type folders. Those often indicate priority just by the nature of their names: Clients, Alumni, etc.… but ask yourself the following questions:

  1. Which contacts are my users looking at on a regular basis (companies and people)
  2. Which pieces of data about those contacts are most important?
  3. Do our DCM rules reflect those priorities (or are we trying to monitor too much)?

It doesn’t mean you won’t monitor other contacts (or clean other rooms) but you’ll do them less frequently, and as time allows or per a data quality schedule. Also, by revisiting your DCM rules, you can make sure you’re getting tickets for those most important contacts, while de-prioritizing the others.

 

STEP 3: MAINTENANCE MODE:

Let’s get back to the house cleaning example. There are things we do every day (dishes) and there are things we do every week (laundry) and there are things we may often be behind on (the spare room!). But the beauty is that not many people see that spare room, so we naturally de-prioritize it. We work on it when we have time. Why do we prioritize certain things? Because the house might get smelly. Because we need clean clothes. We create and follow general schedules for ourselves every day. We should do that, too, with data quality. After prioritizing, focus on which contacts, data and tasks need to be tended to everyday, what can be looked at once a week, and what you can push off to monthly, quarterly or annually. Consider not just your ticket work, but also your duplicate and contact association work, based on your firm’s use of your data:

  1. Which contacts/data always need to be accurate and complete , every day?
  2. What contacts/data can be looked at a little less frequently but still needs some periodic attention?
  3. What contacts/data can be de-prioritized and looked at much less frequently?
  4. (and to assist with/automate some of that) What contacts/data can be trusted from other internal sources so you don’t have to focus on it at all? (Time and Billing systems? HR systems?)

Create a schedule for yourself with these frequencies. You may find over time that you can adjust what you thought would be a weekly task to monthly.

Data Quality doesn’t have to be overwhelming. You’ll do what you can do, but if you eliminate the noise, prioritize and maintain it against a general schedule, we promise, your most relevant data will be cleaner, your users will trust the system more and you’ll feel much better.