When Chief Data and Analytics Officers come into large enterprises, whether they are in consumer products, food service, airlines, hospitality or financial services, they are likely to hear a similar set of comments about the company’s data from their new colleagues. Here are six typical ones:

  • Our data quality is poor.
  • We can’t find the data we need.
  • There are so many duplicates and versions of our data. Which one can we trust?
  • Our data doesn’t serve our customers well. 
  • I have to do too much work to make our data useful. 
  • We spend too much time and money trying to fix our data, and its value is hard to quantify. 

Where the data change management logjam can appear, ironically, is when you start to offer solutions to fix the data and need some peer and executive support. A fundamental misunderstanding of the CDO’s role clouds the waters at these moments. The data issues facing companies are not solely the responsibility of the CDO, but also depend on the rest of the organization. Sales, marketing, operations, finance, HR, and other departments often create data in unmeasured and unmonitored ways that create problems. The CDO is there to set the strategy, shepherd the process, orchestrate the work, and lead the creation of solutions for the data issues. Yet, there’s often resistance to this positive change from key stakeholders when people realize the amount of work involved in change management for data, with commentary such as:

  • I’m really worried how much effort this is going to be.
  • This sounds like a lot of change.
  • I’m used to having free-reign access to do a bunch of things and now we’re being told we don’t have that freedom anymore.  
  • This build is going to take 20 percent longer than it used to take.
  • My team has to learn new skills but they are flat-out right now.
  • We’re doing fine with revenue, the process works, and I can get products out the door. Why mess with it?

You would not be faulted in this situation if you were to wonder, “Wait a minute, you just spent all this time telling me about how bad everything was. But you don’t want to help fix it, either?”

It’s human nature in our busy world to expect that technology change can happen at the flip of a switch. But real transformation requires technology’s users, the humans, to change too. Here are a few ways you can break the change management data and analytics logjam.

  1. Work with senior leaders as change agents.
    My experience, especially in large organizations, is that shaking up the status quo is not a solo pursuit. You need the CTO or CIO, the CEO or the COO, and your business line directors, all working as active champions for change. If you can’t recruit all of them, find a few who are influential and will benefit substantially from your efforts. Wins with these colleagues will pay off in spades and have a trickle-down effect. 
  2. Show how “the pain of change is less than the pain of the same.”
    This is a successful sales technique, especially in technology, but I believe it makes sense because you often wear a sales hat as a CDO. So change the narrative. It’s almost always the case that the status quo of most data environments is far more expensive and painful to work with than using new data and analytics tools, even if the shift comes with some training burden and a bit of time.

    If you can, I’d encourage you to quantify the cost of the status quo. For example, if you build a product or run analysis using the current data or system, it may take two sprints of one to four weeks, but if you want to tweak that or add something to it, it might take you another two to four sprints. If you use the new data or system, it might take you four sprints to build a product, but tweaking anything will only add one day. 

    Emphasize to people that the first new build with the new technology might take 20 percent longer, but builds could be 30 percent faster every time after that, and the results will have greater impact. The temporary trade-offs will be more than worth the effort.
  3. Use encouragement and maniacal prioritization.
    Encourage (but also enforce) that your colleagues take up the new platform, that they document and catalog data, that they use data governance tools, and that they eliminate duplicate copies in favor of shared versions in the cloud. The old way is no longer optional given your investments and goals. The message you send should be consistent, simple, and repeated constantly: we have to do this the right way to tackle the hard problems, and that might mean blowing some things up.

    The other principle I use here is maniacal prioritization, incredible focus on one thing or two things that will change the game. What would they be in your organization when it comes to data? Prioritization is especially important in times like these, with a recessive economy and renewed scrutiny on budgets and hiring.

So while the data change management obstacles may look unbreakable when you first encounter them (or when you land in a new company and encounter them again), the right advocates, the right approach, and the right priorities can get you over the hump. Once people start to see what’s possible with the new way of using data, they are unlikely to want to stick with or go back to the old way.