CIOs Should Incorporate These 3 Ideas in Data Analytics Strategies

May 22, 2019 - CAMBRIDGE—CIOs and other IT leaders should keep these three ideas in mind when developing their data analytics strategies, advised Barbara Haley Wixom, PhD, principal research scientist at MIT Sloan Center for Information Systems Research.

These ideas are: 1) generate top-line returns from data, 2) build information business capabilities, and 3) prepare corporate culture for the algorithmic economy, Wixom told the MIT Sloan CIO Symposium held here May 22.

Wixom said that, based on a MIT Sloan survey of 315 C-level executives in 2018, more than half of data monetization returns come from operational efficiencies, the rest of the returns come from top-line activities. Top-line activities are becoming more important because they are contributing to revenue.

“When compare top performers to bottom performers, the top performers on average realized 10 percent more of overall company revenues from data than bottom performers. So, we need to be thinking about how to generate top-line returns from our data,” she said.

Wixom said that business models across industries need to change to become more like information businesses, which know how to provide valuable data analytics to the marketplace.

READ MORE: Healthcare Data Analytics Having Big Effect on Clinical Workflows

She identified five capabilities that successful information businesses have: data that people can find, use, and trust; data platforms that serve up data reliably and fast; data science that can detect insights that humans cannot; deep customer understanding of core and latent needs; and data governance that oversees compliance and values and ethics.

“When we looked at out data across all companies, we found that these capabilities worked not just for information businesses, but for all companies. If you are investing in these capabilities, then you will be seeing much more activity, progress, and economic returns from your data monetization efforts,” Wixom said.

The algorithmic economy has arrived. Organizations that are leaders in this economy are providing data science training for all employees, using advanced techniques like machine learning, and methodologies for evidence-based decision making.

“Companies with these characteristics are seeing payback that is nine months sooner from their data analytics projects and 6 percent more in ROI [return on investment] from those projects,” she said.

Panel of CIOs Share Insights on Their Data Strategies

Following her presentation, Wixom moderated a panel of CIOs and other IT leaders to discuss their successful data strategies. While there were no panelists from the healthcare industry, their insights are applicable across industries.

Elena Alfaro, head of data and open innovation, client services, at Spanish bank BBVA, said that her bank began integrating data science into its operations back in 2012 and set up a separate legal entity to drive data strategy change.  

Alfaro headed the legal entity, which set about attracting and retaining data science talent. The entity’s workforce grew to 50 people. “We tried to prove to the rest of the organization that there was a lot of value in doing things in a different way, having new technologies, having ways of using data,” she said.

Alfaro explained that the bank did an economic impact analysis and found that that new strategy increased revenues, reduced costs, and improved the bank’s reputation.

Donna-Maree Vinci, chief digital and information officer at Australia-based Bank of Queensland, said that when she joined the bank four years ago, she was “perplexed” that it had not yet started on the data science journey.

Vince identified three things that her bank had to get right about its data strategy: alignment to corporate purpose, building out data analytics enablers, and execution.

Part of that effort involved using a broad array of partners. But Bank of Queensland found that these partners, particularly startups, did not have secure IT infrastructure. So, the bank set up its own secure IT infrastructure for them to use.

Mark Picone, vice president of information and data services at Adobe, explained that the company's back office was not prepared to structure and use data generated by its subscription services.

“With subscription services, you have to demonstrate your value every month. That value is only proven with deep levels of customer engagement. If you can’t measure customer engagement, if you can’t tell what they are doing with your products, you are in the dark, literally flying blind,” he said.

The company decided to create a data-driven operating model, which was an initially an IT-led initiative designed to break down data silos that were creating problems.

Adobe had a lot of data science expertise, but it was distributed in these siloes. So Picone’s job was to bring the data science experts out of the silos and together onto one data strategy playbook.

Mark Meyer, chief information officer for food processing company Tetra Pak Group, said his company has been proactive in developing a 2030 data strategy.

“You come to a board meeting and everyone has a different version of the truth. This is because all the information not only comes from different sources, but it is also managed in a different way. How do you make decisions at a corporate level if you have different versions of the truth?” Meyer observed.

To implement a successful data strategy, “you have to be stubborn and patient,” he said. Tetra Pak’s goal was to ensure that there was “one single way of talking about how we run our business and one copy of our operational data in same format where all the definitions are always the same. With that data, we can drive better decision making.”