Wherever I’ve brought my data skills – a home construction company, a pediatric health system, a multinational manufacturer of confectionery and pet food products, or my own consulting practice – I’ve always talked about the art of the possible. 

For me this means helping every organization maximize the data tools at their disposal, ideally with a persona-based view that centralizes all analytics in one place. Centralization is key because a shared data platform allows everyone to understand what’s happening in the business versus looking at siloed metrics here and there. This data-driven approach ensures decision makers can see the bigger picture. That’s because you never know what could connect to your area or your department next week or next quarter, even if the link isn’t clear today.

Possibilities versus reality

Now let’s drop down to where many organizations are today, too busy trying to do what’s always been expected. By which I mean building legacy reports that affirm what companies already know or following outdated ten-step processes to provide an email about some minor data point. Culture, people, process, and organization all play a role in creating barriers to making the leap into becoming a data-driven business. 

That’s why it’s important to look at what you can do to reverse that momentum and put these four elements to work for you instead.

Data culture: Empower your leaders.

The most important thing I’ve seen when it comes to building a data culture is empowering senior leadership to support you. When they act as your champion, that takes the flag up the hill (and takes arrows along the way). If there’s not someone in the C-suite saying, “This is valuable work” about a data initiative, it can get shut down. This is especially important before the value of a data initiative starts to materialize. Organizations that are successful usually have brave champions willing to say that the effort is worthwhile. It’s not always an easy battle, but these are the organizations that actually move the needle. 

People: Ask better questions.

Ask employees across teams what they’re trying to do with the reports they create. What action do they want people to take based on their reports? If the intent doesn’t match up with the report, what can you do to design a platform that better informs it? In too many organizations, frontline workers don’t get an opportunity to lift their heads up because they need to submit another monthly report or another quarterly visualization. Because the process is so inefficient, they are often critiqued for metrics that are off. They need to get to a stable state to ask more interesting questions and do more interesting work. As a data leader, you’ve got to help people step off these treadmills and get them focused on providing value. Of course, it doesn’t hurt to hire smart, capable people and trust them to find a solution versus prescribing what that solution should be.

Process: Streamline to find gaps.

I believe it’s a mischaracterization when people say that frontline employees’ fear of data is what slows process. The fear is not so much of the data, but the tedious process they have to go through to derive insight. To use a common buzzword, although everyone talks about data as the new oil I think the real fear is of the cumbersome processes people need to follow to mine it. Once you can streamline a data process, you can really take inventory of what’s available to you, understand the questions the team is trying to ask, and see the gaps. Once you find those gaps, you can ask whether you need to go search for third-party vendors to help fill them –  or whether there are other creative ways you can actually do these things.

Organization: Prioritize change management.

One of the biggest challenges I’ve seen to becoming data driven is change management. That’s the responsibility of middle management and senior leaders. If good change management is in place, your data team not only delivers something, but it actually stands a good chance of getting adopted. The inertia of getting people to change behavior is very difficult. Too often you see a heavy investment in infrastructure, tools, or external data sources, but not  project management or product management. Once you deliver a capability, how do you ensure that it’s adopted and that it delivers on the value? That follow-through is often the hard part. You might spend 80 percent of your time working on the initial portion of the work, but it’s very difficult to get everything across the finish line because that final 20 percent requires the work that most people don’t want to do: meetings to train people, or to actually document where the data’s coming from, how it’s being used, or how things are calculated. That’s where a lot of the enthusiasm falls off. Ensuring data literacy across the organization can make this decision-making process more seamless.

The enduring value of human-centered design

Meeting the four-fold data driven challenges I’ve outlined above also requires a focus on human-centered design, because all data is meant to be consumed and used by human beings. Human-centered design requires ongoing conversations among all levels of people – frontline, middle management, and senior leadership – about who should get what value from the data. It requires UI and UX research because any report, dashboard, or platform you’re building to provide insights will hit each user differently from you – often completely differently. All of this work is essential because it addresses any potential mismatches in expectations. At the base of every technology problem there is likely to be a communication problem.

Data-driven cultures: who stands out?

How many organizations are truly data-driven and excel in customer satisfaction? The number is growing, but it’s still not high enough. Delta is one. They’ve done a great job of embedding valuable, actionable insights into the customer journey. They also do a great job of syncing that from their website to their app to the passengers on their planes. Another company with a great data ecosystem is ADP. Their ability to understand touch points from the call center and how that translates into additional services their clients may pursue and purchase is terrific. They also use data well to circumvent attrition and prevent churn. 

Every organization should be in tune with their data ecosystem the way these two companies are. Take a fresh look at your culture, people, process, and organization, and you’re already on the way to getting there.