A strong big data strategy can cut costs and increase revenues, according to research from MIT.
Evan Huggins of Pernod Ricard spoke with TechRepublic about how organizations can get started with data by taking inventory, triaging it, and finding the right tools.
Only 25 years ago, it was extremely difficult to get CIOs to pay attention to their data. While that is clearly no longer the case, many organizations still lack an understanding of how to create a data strategy that will cut costs and increase revenue, Barbara Haley Wixom, principal research scientist at the MIT Sloan Center for Information Systems Research, said in a session at the 2019 MIT Sloan CIO Symposium on Wednesday.
“We are swimming in data, and we know we need to make money from it, but not the way we used to,” Wixom said. “In ’94, we kept data inside the organization, we cut costs with it, and we restricted access to it. That doesn’t work today. Today, our data strategies require bold moves and new ideas.”
SEE: Special report: Turning big data into business insights (free PDF) (TechRepublic)
To create an effective data strategy, CIOs and other tech leaders must include three ideas, according to MIT research:
1. Generate top-line dollars from data
Organizations tend to be skilled at cost-cutting with data. In a study examining 315 CXOs, MIT researchers found that 51% of data monetization returns came from operational efficiencies. For a bank, that means doing things like optimizing branches, which the bank BBVA did and saw $40 million in savings.
Nearly half (49%) of our current returns in data come from top-line activities, Wixom said. For BBVA, that meant doing things like creating a data science team and using machine learning to add new customer value to banking tools.
Top-performing companies—in terms of revenue, growth, and agility—generate 10% more of their total revenues from data, as compared to bottom performers, the research found.
“We need to be thinking about how to generate top-line returns form our data,” Wixom said.
2. Build information business capabilities
“As we rely more on data for overall company revenues, what that means is our business models need to morph to become more like those of information businesses—think Lexisnexis, Google, Bloomberg,” Wixom said “Those companies know how to deliver data analytics to the marketplace in ways that are highly valued by customers.”
MIT has studied such companies for the past decade, and determined that they have five capabilities that help them achieve success:
- Data that people can find, use, and trust
- Data platforms that serve up data reliably and fast, both inside and outside of the firm
- Data science that detects insights that humans can’t
- Deep customer understanding that identifies important needs
- Data governance that oversees both compliance and ethics
“When we looked at our data across all companies, we found these capabilities work for all companies, not just information businesses,” Wixom said. “If you’re investing in these capabilities, you will be seeing much more activity, progress, and economic returns from your data monetization efforts.”
3. Prepare your culture for the algorithmic economy
The algorithmic economy is here, and involves a world in which organizations need employees to habitually use data algorithms to inform key tasks, Wixom said.
Companies that are leading the movement on this have data science training for all employees, use advanced techniques like machine learning, and have methodologies for evidence-based decision making in place, Wixom said.
Companies with data-driven culture habits generate payback from data projects nine months earlier than those that do not, and earn 6% higher ROI, MIT’s research found.
For more, check out How to become a data scientist: A cheat sheet on TechRepublic.