5 Ways to Build Enterprise Data Accountability
Pick a handful of companies that compete in a consumer market – home goods retailers, video entertainment, air travel – and compare their performance today to what it was ten years ago. You’re likely to find big winners and big losers, companies that were paying close attention to signals of what was happening in their environments and those that weren’t.
A strong data strategy isn’t the only thing that separates these winners from the rest, but it’s certainly a big part of it. It’s one reason we formed an Enterprise Data and Analytics Office (EDAO) and why a similar entity may work for your organization. The office formalizes accountability to govern and manage enterprise data assets and platforms and provides analytics services across the company. It includes defining roles and responsibilities for Data Owners & Stewards, that are the subject matter specialists within the line of business and reiterating that data is a shared responsibility. Additionally, it raises the importance of data quality and data security in our internal work and our work with customers.
Having data accountability can help organizations determine:
- Who’s accessing data
- What they’re using it for
- Where sensitive and confidential data lives
- How that data is protected
- How you’re complying with regulations
- What data is needed to answer business questions
The ‘enterprise’ in EDAO puts an appropriate emphasis on collaboration. Data can’t be the responsibility of one person, so information security, compliance, legal, risk management, business lines and other teams must work together to ensure that everyone shares accountability for the company’s data.
Building a force for clarity
My experience with an EDAO is unique, as it would be in any organization, but here are five best practices you can consider.
- Assess your data maturity and define a common language. One thing an EDAO can do is reinforce clarity in dealing with your data. This applies to a clear, shared sense of your organization’s data maturity as well as agreeing on shared definitions of concepts like governance or AI. Determining data maturity may require a stakeholder assessment of where you need to be versus where you are today compared to your industry standard, and may involve dozens of stakeholders.
Creating shared definitions is key because it removes the ambiguity when these terms come up. AI, for example, is high on many hype cycles today, but people also mean machine learning when they say AI, and that’s been around for quite some time. And one area you definitely don’t want to open the floodgates on without rules of engagement is AI. With generative AI and large language models, you have to understand effective and efficient uses of it, and more importantly, what aligns with your business strategy and core values.
- Create roles and responsibilities. Data governance is especially important when it comes to enterprise data accountability because it helps to ensure you have the data quality you need to drive your business. Often, defining roles and responsibilities requires collaborating with the lines of business to ask, “What are the important data elements for you?” Collaboration makes it easier to reveal where your data is, who has access to it, and how you can best communicate with your colleagues so they aren’t making decisions based on inaccurate data.
Effective data governance also makes sense because people aren’t wasting time gathering, cleaning, and second-guessing the accuracy of data. Ideally you have subject matter specialists in place that verify your data lineage. You’re handling confidential and secure things the way that you’re supposed to be, and then you’re able to develop the analytics needed whenever the business has a question. You’re also creating places for people to go when they aren’t sure if they should be using data in a particular way.
- Set a vision that makes sense for your business strategy. In financial services, we want to create more relevant communication for customers and do it as efficiently as possible. Our EDAO tracks closely to this personalization strategy, and is allowing us to recommend products or offers we believe customer segments are interested in based on our data insights. This helps us better service our customers and be a trusted advisor. That’s how our vision is a direct outgrowth of our business strategy.
Of course, it’s impossible to make technology investments in every area of your company all at once, so you need to align your decisions with your strategic plan and core values. If thinking about the customer experience is one of those, you need to think about how your technology decisions contribute to optimizing that value. If greater data self-service for employees is important, consider investments there. Pick the top two or three investments to start, and complete, show some progress that validates your strategy, and then go to the next thing in your priority list.
- Create buy-in by taking people on the journey with you. Communicate constantly. In my own case, I used a similar message to build buy-in for an enterprise data accountability vision with my own team as I did with our business partners and our executives. This presentation laid out the vision I was communicating and the path that leaders were excited about. You may feel like a broken record at times but remember that not everyone is living in your world along with you. It's also important to give your teams enough information along the way to keep them engaged and excited, but at the same time not to overpromise. It will be much more successful to lay out what they can expect in the short term as well as some of the longer-term goals. Once the time comes to implement changes, people are more likely to be on board because they’ll understand where the change is coming from.
- Expect a few bumps. Remember that change is incremental, not linear. You don’t implement a plan and the next day everything soars. You’ll have ups and downs. The throughline should be your vision of what lays on the other side: your world will be more efficient and you’ll be able to work more effectively with different groups and make better decisions. Consider a quarterly meet-up with your executives to discuss their data priorities. Lay out where you are now, when the next update is going to be, and why this part of an initiative may have been a little slow. Remind colleagues of the overall vision and they’re much more likely to go along for the ride with you.
At the end of the day, building an Enterprise Data & Analytics Office is a function of top-down buy-in and grassroots support. When you do it successfully, you can say with confidence that you’re not the only one who believes data is important: your Executive Management is excited about it as well. Your data strategy belongs to the whole organization, because you’ve been transparent about what you're trying to do. You haven’t created expectations about overnight results, because people know what to expect. You’ve pivoted when you need to because you’ve listened to feedback. You’re relying on the data, just as your enterprise is. But because they’ve all been involved in making that data better, you’re moving ever closer to the successful data-driven organization you envision by building a culture that recognizes the shared value of data and views it as a strategic asset to enable business decision making.