Why Organizations Struggle to Get Data Cultures Right

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A growing number of organizations are embracing data analytics and business intelligence, but a large number of those organizations struggle with how to get full value from their data. In many cases the problem lies with the culture within the organization, including who should be held accountable for data analytics initiatives.

Those are among the conclusions of Paul Blasé, principal of Global and U.S. Analytics for Consulting, at PricewaterhouseCoopers. Blase spoke with Information Management about why so many companies still struggle with getting analytics right, and when a chief data officer is needed in the picture.

The good news is that organizations are collecting and creating more data, but they also have better analytics tools and techniques available. But that can also become too much of a good thing.

“I think we have more data and more ability to use analytics techniques that can impact financial metrics through better forecasting,” Blase says. “But I think one challenge is C-suite accountability: meaning the right individual or level in the organization has to provide the mandate, the accountability, and the purposes for using data and analytics, and be very clear about the type of value they are searching for.”

The need for top-level accountability has much to do with communication and championship, Blase explains.

“The value could be from improving products and services with data and analytics-driven services. It might be from improving a long-term financial metric. It might be from improving operations. All of those have very different implications in terms of what you have to build to get the value, and I think frequently that accountability and clarity of purpose is just not well articulated,” Blase says.

Indeed, lack of clear communication can be one of the most damming factors in any unsuccessful data initiative, Blase says. The date team may certainly be up to the task of analysis, but they may not grasp the larger strategic need driving it.

“It’s very easy for organizations to say, ‘I have a business intelligence group, so shouldn’t they be the organization or the function that I depend on to advance my analytics capabilities. But very often those groups aren’t equipped with the right talent, or they have a different mandate. Their mandate isn’t necessarily to follow complex business problems. It’s to provide dashboards and reports and meet efficiency-oriented service levels,” Blase says.

“From that vantage point, it’s not a case of fault. There is just a misunderstanding on the terminology of big data and analytics, and where it potentially sits in the organization,” Blase says.

The result is a need for the C-suite to better “articulate the case,” Blase stresses.

Upper management may also have a much better idea on where the collective talent lies within the organization to assemble into a larger specific data team.

“When we look at the business side of the equation, there are a lot of function-based analytics groups – marketing departments, budgeting departments, sales organizations, service organizations, pricing, credit risk, you name it – they usually have some small group of business analysts,” Blase explains.

“In that case what we’re seeing in organizations is that they’ve got the talent spread all over the organization,” Blase notes. “They want to solve problems that are really about cross-functional improvement. It might be, ‘how do we improve conversion and value to customers across the demand funnel. Will we have to pull marketing, sales, product development and pricing all together if we’re going to build a model that tells how we do that more effectively?”

The challenge then becomes “figuring out how it should be organized, and who should be accountable for a cross-functional effort like that because when you rely on any one function to do it, that’s not how they’re incentivized, the reporting lines aren’t clear, the objectives aren’t clear, and then it doesn’t work,” Blase warns.

But there is good news in all of this too. For one thing, there is growing interest in data analytics and business intelligence at the C-suite level, Blase believes.

“We’re starting to see an uptick in the C-suite’s interest in building more sophisticated capabilities that allow them to combine the art, and what they would call their judgment and experience and intuition, with the science, which is the data and the models,” Blase says.

“In that case we see large-scale visualization; we see different types of simulation models that employ a kind-of gaming into the analysis being well received because it allows the C-suite to have a collaborative conversation. They can use their experience and judgment to set parameters on a model, run the scenarios, and then see the results.”

“For example, they can debate, ‘well why did the market grow at this rate when I assumed [it would grow] at this rate; or why did this competitor gain share versus me, when I assumed the opposite would happen because I dropped my price? It’s about combining the intuition and the experience with the science of data analytics together to help an executive team make better decisions, and that’s where we’re seeing traction.”

That can be easier said than done, of course.

“The challenge is that it requires a culture and a mindset shift starting from the top to want to operate in a data-driven way throughout the organization, as opposed to operating primarily with experience and hindsight when it comes to data and analytics,” Blase says. Which brings us to the question of when an organization should appoint or recruit a chief data officer to manage the process?

“The bad reason would be hearing about the over-used term of ‘big data’ and saying we need to have a chief data officer or a chief analytics officer because of that, and putting one in place before there’s alignment in the C-suite about what that individual is supposed to do, how they will be measured, what they incentives will look like, and what kind of accountability they need to have,” Blase stresses

“I think what needs to happen to figure out even who should undertake the data and analytics opportunity and challenge for companies is figuring out where you want to generate value,” Blase says.

“There are many cross-functional problems that are tied to a lot of business value which in many companies have a direct impact on shareholder value. Then you have to start looking at somebody owning it that sits at the C-suite table, is viewed as a peer of the leadership and helps them apply data and analytics in the most optimal way to the organization,” Blase continues.

“In that case, you start to see the need for a chief analytics officer and a chief data officer to really build the capability, and create the right balance between a centralized center of excellence model,” Blase says.

“It is a model where you still integrate the analytics talent into the functions and manage it more effectively; where they can manage data exploration and discovery and be an owner of what data gets explored coming into the organization and what problems it gets applied to; how to adopt new techniques like machine learning based on its maturity and where and when it can be used,” Blasé concludes. “All those things are good reasons to have a role such as a chief data officer at the C-suite level accountable for this.”

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