Editor’s Note: In a recent post, Sanjay Mathur and Scott Kurth wrote that CDOs have 100 days to get the ball rolling downhill on digital transformation. CDOs know they need to move beyond the traditional focus of data governance and work to create new data products and value for the business, but there are some very real challenges to doing this efficiently. The following are some of the frequently asked questions we’ve received on this topic.
1. What are some techniques the CDO can use for building strong partnerships and/or achieving consensus efficiently? Just getting all the stakeholders in a room and hashing out the different priorities can take weeks or even months, let alone getting everyone on the same page. And building relationships as a new executive often takes time, too.
There are two aspects to these relationships that are important: building trust with individuals and gathering consensus. As you’re building trust in these 1-to-1 relationships, the most important thing is getting meaningful conversations started. Make them meaningful by offering the other person something of value. Share your thoughts, early plans, and how you believe those plans will generate impact, but more than anything else—listen. Understand their metrics and where they are focusing their own initiatives. What matters to them?
Gaining consensus in organizations is usually harder. If you can, try to gather all your stakeholders in a room together. You’ll get a better response if you can articulate how critical this is to not just your own success, but the success of the company. Ask your CEO to make it a priority for all. It can be difficult to schedule, but there’s no substitute for those face-to-face conversations (or debates) to reach a shared understanding of what matters most. This is a technique that we at SVDS use in nearly every one of our data strategy engagements. Achieving shared understanding, and shared goals, is a great way to help you manage the inevitable trade-offs that will emerge later as you’re executing your strategy.
2. How do I build data literacy in my organization when every department uses a different set of tools (for example, one team prefers Python while another wants to look at data in Excel)? Do I have to centralize all teams on the same toolset?
A lot of CDOs that I talk to are grappling with this question. As you’re thinking about helping teams to work effectively, a single toolset shouldn’t necessarily be the goal, but smart rationalization and supporting those tools from a common platform should be. You’re always going to have users with different needs and different levels of sophistication. Forcing everyone onto a single tool risks alienating certain groups—or worse, sapping their productivity.
Smart rationalization is a worthwhile pursuit though. No one benefits from the wild west approach. Emphasize and communicate the value of sharing data (and analyses) among and across data scientists and teams. Reuse and repeatability benefit everyone, and help you build sticky communities that increase knowledge and capability, too. Done right, the teams will see the benefit in it.
However—data literacy should be about much more than getting all your data scientists to use Python (or any other tool). It’s about ensuring that business leaders understand the data available to them and the ways that you can help them make better decisions. It’s also about ensuring that they ask others for data to support new strategies, new directions, and new choices. And real data literacy means that they’ll want to use data to measure how those decisions pan out, too! Data literacy shouldn’t be limited to only your engineering or analytics teams. It is critical that the mindset span the organization.
3. How can I be an efficient change agent when my colleagues’ incentives are aligned differently? My CIO is measured on efficiency so they’re not interested in spinning up huge new data clusters, and department leaders aren’t currently incentivized to share data with each other.
First, talk to leadership. See whether you can get metrics better aligned. It’s fine to have metrics that are complementary or ones that—at first glance—appear to be tangential. Conflicting metrics, though, need to get resolved. If the CIO is still measured on outdated metrics that treat data as a cost center, you’ll have a tough time swimming upstream.
Second, show them how data impacts their metrics. CIO metrics focused on efficiency aren’t automatically bad; many CDOs have savings targets, too. You can work together to consolidate data platforms, find ways to migrate workloads to lower-cost infrastructure (e.g., data warehouse rightsizing or data lakes side-by-side with a distributed data platform), or sunset legacy systems.
Finally, business unit heads will always be focused on their revenue targets, as they should be. Seek out opportunities where data and analytics unlock new value for their organization. Build the value propositions and start experimenting. If you help them unlock a new $100M value proposition in their business, they’ll do more than just open up to sharing data—they’ll be your biggest advocate.
4. There’s a lot of conversation about not getting stuck playing defense, and going beyond data governance. But I work in a highly-regulated industry and we have real data governance imperatives and challenges. How can I still make progress on being “offensive” and looking for new value streams when so much of my focus has to be on compliance?
So first off, governance is a very real concern, especially in heavily-regulated industries like financial services or healthcare. I don’t want to make light of it. But the reality is that you’re going to have to do both. The CDOs I’ve spoken to have repeatedly told me that their partners in the business don’t see value in policies. “The business doesn’t care.” This is where most governance initiatives fail.
To succeed, you’re going to have to make the business stand up and take notice. So, how are you going to do both? Delegate. You need lieutenants who can take the burden off of you. It requires attention, certainly, but if you let it consume all of your time, you’ll never achieve your primary mission: generating value for the company. At the recent MIT CDOIQ Symposium, Venkat Varadachary, CDO of American Express, estimated that he only spends 5–10% of his time on defensive activities—but his business wishes it were zero. That’s only possible when you have delegates who can multiply your impact.
5. Often with data, there is a delay before you can reap what you have sown, so to speak. Experiments must be run and it can often take time to discover where the real value lies. How can I expedite this process to have impact more quickly?
Your company wasn’t standing still before you took on the CDO role. Your organization has experiments running already. Uncover them, embrace them, and support them—or redirect them to something more fruitful. And don’t be afraid to kill projects. Failed experiments are successful projects, too, but only if you fail fast and learn from the experiment.
Do that in parallel with developing your plan. Your first 100 days shouldn’t be about finding all the answers. Instead, it’s more about identifying the right questions. What are the experiments or proofs of concept (POCs) that you should be running? Where are the fruitful targets? This is what ensures that your first 100 days is the warm-up that lets you accelerate out of the gate.
You may also be interested in our upcoming series of webinars called Data Dialogues, which will focus both on Data Strategy and Data in Practice. Register now to reserve your seat and take part in the live Q&A at each session.