I have had the opportunity to work often with Chief Data (and Analytics) Officers. I’ve watched as the data department in the business has grown from being an embryonic, highly specialized function to the point where data teams are a critical part of the organizational infrastructure. For the most part, this has been a natural evolution. But in some ways, the COVID-19 pandemic accelerated this change.
According to McKinsey, COVID accelerated the digital engagement companies had with their customers here in Asia-Pacific by more than 4 years. During COVID, companies’ collection and interpretation of data and their ability to respond to it was critical in being able to anticipate wildly changing market conditions and rapidly evolving customer needs. But it ended up going farther than that. Companies needed better, more timely financial data for real-time forecasting. Historical data wasn’t as relevant during unprecedented times. Supply chains, globally disrupted by production delays, material delays, transportation delays, became heavily dependent on real-time data. Across the business, we have seen a rising demand for access to data and a growing realization that it is key to answering critical questions.
Data has become the foundation of growth and resiliency regardless of function. But there is a gap that is growing.
The Analytics Gap: A Word of Caution
Even as a historically large number of enterprises invest heavily in the areas of AI (Artificial Intelligence) and analytics, the gap between an organization’s investment and their return is increasing. While more than 90% of organizations are now treating AI and analytics as a priority, the number who describe themselves as “data-driven” has unfortunately declined from 24% to 19% in just two years. This decline is in part due to our changing expectations and understanding of what is truly possible with data.
Enterprises are investing in analytics, but the analytics gap is only widening.
To understand this gap, Alteryx surveyed 2,800 people – data leaders across teams – in multiple industries worldwide. Our goal was to dig deeper into organizations’ data and analytics culture and the role that data now plays in decision-making. One of the striking learnings from our survey: some 80% of respondents said access to data has positively impacted their own decision-making. A majority of respondents called the impact not only positive but significantly positive. So why is the analytics gap increasing?
The Importance of Collaboration
We started to see where the gap was coming from during our research. If most of us agree that data helps us make better decisions, but we don’t all have access to that data, there’s a problem that needs solving. What if, we asked, more employees had access to more data? People enthusiastically listed a number of anticipated benefits – at the top of the list was better collaboration.
Benefits of More Employees Having Access to Data
This is inconsistent with what I’ve heard from customers. One function in a business will often express frustration that they need to explain or respond to questions from another function around the data they are using. They want common data with a shared understanding so they can have conversations on neutral, common ground and talk about real issues – not perceived issues or, worse yet, the data itself.
Experts and Non-Experts: Overcoming the Divide
To become analytically mature, it’s important to close the gap between people who work with data every day and people in business functions who often possess enormous knowledge around their line of business.
In a balanced organization, every function in the business gets value from data in some way.
A small number of people work on your biggest problems and deliver large ROI (return on investment). This team has highly developed skills and benefits from the big investments made in technology.
Outside the data team, your many knowledge workers use data to deliver incremental improvements across thousands of fronts. When organizations go on this journey and balance their investment, we see a powerful network effect that goes beyond immediate ROI.
The Role of the CDO and the COE
So, what about CDOs? As Bill Groves, a veteran who held the role at Walmart, Honeywell, and Dun & Bradstreet put it, “[The CDO function] is not a service organization; it’s a transformation organization.” According to research by HBR and AWS, 69% of the 250 CDOs surveyed said they were focused on building a data-driven culture, and 55% said it was one of the primary things holding them back in meeting their business goals.
What is your role if you are the Chief Data and Analytics Officer? Is it to control access to data or to lead your organization’s transformation to become a data-driven business? Research suggests you need to transform from being defensive – focusing on restricting data and access – to going on the offensive. That doesn’t mean abandoning governance and security. It means identifying areas where you can get quick wins, and showing where and how you can drive business value from the data.
In our experience working with customers on this journey, one of the key initiatives is fundamentally changing the role of the Center of Excellence’s (COE) Most organizations who are data-oriented will have a COE within their data team whose responsibility is to have best practices when it comes to data and be a point of expertise in the business.
But to become analytically mature, the capabilities and responsibilities of this team need to shift. COEs (Center of Excellence) still need to be data experts, managing data for the organization and solving those big challenges. They also need to enable others in the business to answer their own questions. They need to build data independence, which drives value for the organization from their investment in data and accelerates employee engagement as they build data skills for a digital future.
Customers will often tell us, “We see the need for data access at scale, but we can’t have anarchy.” Giving more people data access isn’t anarchy; it’s a controlled scale and doesn’t diminish the role of the COE. It instead amplifies the power of the COE because there are now a whole lot more people on the path to getting value out of the data. That’s why one of the first things we do when we work with companies is to work with the IT and data teams to ensure we have governance and control in place.
The Data Access Journey
None of this happens overnight. To become a data-driven organization, technology is just one piece of a larger puzzle. Change management is the real goal. For organizations with a centralized model, you have created an organization dependent on you doing their basic tasks for them. You need to find ways to loosen your grip.
Help knowledge workers learn to manipulate data to automate a process. Help them learn a little bit of analytics. Over time, your data scientists will appreciate how many low-value tactical questions this removes from their plate, allowing them to focus time and effort on the questions that are going to have the biggest impact on the business.
If you’re going to take your company on a journey to analytic maturity, you have to take everyone on the journey, a few highly skilled data scientists cannot carry the weight of the organization’s data expectations on their own.
Good Things Will Happen
good things happen to companies that successfully create and sustain a data-driven culture. These companies generate more revenue, drive cost savings, create operational efficiencies, and automate manual processes. Democratizing analytics is about elevating the data function so that they can enable more people in the organization to transform. That is how organizations close the gap on their investment in data and return. It is how they engage and keep the people in their business.
Make analytics easy. Cover everything. Be everywhere. Enable everyone.
Success will follow.