Dr. Barbara Wixom is a Principal Research Scientist at the MIT Sloan School of Management’s Center for Information Systems Research (CISR), where she explores how organizations generate value from data assets. Since 1994, Dr. Wixom has been a leading academic voice in data analytics, with research published in top journals like Information Systems Research, MIT Sloan Management Review, and MIS Quarterly. She frequently presents her work to global academic and business audiences.
At MIT CISR, Dr. Wixom leads the Data Research Advisory Board, a group of 100 data and analytics executives from CISR organizations. This board helps shape research that advances data monetization and analytics practices. Her book, Data is Everybody’s Business (MIT Press, 2023), empowers professionals across industries to engage in data-driven value creation.
MIT CISR, a renowned research center at MIT Sloan, helps CXOs navigate digital and data-driven transformation. With a consortium of 75 global organizations, MIT CISR delivers insights on digital business, data monetization, and strategy execution. Dr. Wixom is also a passionate advocate for diversity in data careers, encouraging women, young professionals, and underrepresented groups to explore opportunities in the field.
In this insightful conversation with the CXO Outlook Magazine, Dr. Wixom discusses the broad spectrum of data monetization, from enhancing internal processes to AI-powered decision-making. She also highlights leadership’s critical role in fostering a data-driven culture and shares compelling real-world examples of companies that have successfully integrated data into their core strategy. Here are the excerpts from the interview.
Data Is Everybody’s Business challenges traditional notions of data monetization. What inspired you and your co-authors Cynthia Beath and Leslie Owens to write this book, and what key message do you hope readers take away?
I conducted academic research regarding how organizations make money from their data for three decades – and know what it takes to be successful at it. For one, it’s critical to view data monetization broadly, to include not only selling information for revenues, but also improving operations using data – and wrapping analytics around products to enhance their value.
Whether to monetize data is no longer a competitive choice. Within the past decade, using data to improve the financial health of the organization became a must. Our research shows top-performing organizations attribute 11 percent of revenues to data monetization, more than five times the 2 percent reported by bottom-performing organizations.
I decided to write the book to share what we know – to help organizations succeed in their data monetization pursuits. We hope the book helps leaders establish their distinctive data monetization strategies, for one. Also, we hope the book is a useful communication tool, one that helps a leader establish common language and simple frameworks and involve everybody in the organization’s data journey. We purposely wrote the book for a leader to read and then pass along to peers and then distribute across the enterprise.
You emphasize that data monetization is not just for specialists but for everyone in an organization. Can you share an example of a company that successfully embraced this mindset and saw a significant transformation?
Succeeding in data monetization requires that an organization can successfully move through the data value creation process: 1) turn data into data assets that are fit for use, 2) from those assets, glean insights that inform changes to work practices, products, or offerings, 3) from those insights, make changes to work practices, products, or offerings, 4) based on those changes, monitor and manage a range of resulting benefits, and 5) realize some portion of the created value on an income statement or whatever instrument reflects the financial health of the firm.
Moving through the data value creation process involves myriad activities – such as capability building, data asset use and exploitation, innovation, development, change management, and performance management – that collectively require pervasive organizational involvement. Leaders need to establish vision and get support from their peers and board members to keep activities aligned and on track; they need to engage people from IT, data management and data science, strategy, business process, product, customer experience, finance and beyond for execution.
Our book describes Satya Nadella’s transformation of Microsoft and the way in which he made data monetization the responsibility of every employee at the company, which numbered more than 100,000 people at the time. Nadella did not view data monetization narrowly as selling data, but he encouraged data use mainly for the improvement of work tasks and for the transformation of operations. Within 3 years, 61 percent of the workforce was using business intelligence monthly, working in new data-driven ways.
Your book provides practical frameworks and rich case studies. What was your process for selecting these cases, and which one stands out to you as particularly compelling?
My favorite and most frequent research methodology is the longitudinal case study. Basically, that means that I follow organizations over time – to learn what and why they are doing things and understand what happens as a result. I usually find case sites through my data research advisory board (a group of 100 global data and analytics leaders from MIT CISR sponsor organizations who regularly participate in and review my research) and by judging best practice competitions. When I observe a company doing something that I have never seen before (which by now does not happen very often!) or when I see a company successfully achieving a goal or overcoming a challenge in a novel way – then I will ask the leader if they would be amenable to academic research. When they are, I pull an academic team together and we spend time (from a few months to many years) interviewing people and gathering publicly available and internal information and then analyzing our research data for insights.
One of my favorite cases was the global financial services company BBVA. Back around 2015, one of their data leaders explained to me the company’s goal of setting up a separate legal entity to sell solutions based on deidentified bank card data. Their goal and approach intrigued me, and they agreed to let me travel to Madrid to meet with the team – and then conduct interviews for several years. After the initiative proved out and had clear evidence of success, we submitted BBVA’s story to an international CIO case competition, and the case won first place. The story is featured in our book to illustrate the concept of building data capabilities.
Many organizations struggle with integrating data into their business strategy. What are some common roadblocks, and how can leaders foster a culture where data truly drives decision-making?
I would argue that the top roadblock is leadership’s lack of data-savvy. It’s a non-starter when organizations have leaders who are unable or unwilling to engage in establishing and managing the firm’s data strategy. Too often executives overly rely on IT and data leaders and the data office to drive data monetization success. This is a big mistake. It takes those at the apex of the organization to set the organization’s vision for data, allocate investments and resources to deliver on the vision, and make it clear how (and why) people across the enterprise need to be getting involved.
In the 2024 research briefing I mentioned earlier, we found that CXO-level data leadership amplifies the financial impact of data monetization. When their CXOs can communicate data goals and outcomes in clear and compelling ways, organizations are more likely to succeed in data monetization. CXOs are well served by taking executive education courses about data, analytics and/or AI, getting reversed mentored by data-savvy employees, and reading books (like ours!).
With advancements in AI, automation, and data analytics, how do you see the landscape of data monetization evolving over the next decade?
The landscape of data monetization is evolving to incorporate AI technology across the data value creation process. Organizations will more often and more extensively use AI to convert data into data assets, to glean insights, and to automate processes and decisioning. This will drive data monetization – improvements to work, enhancements to product, and information solutions – that is on demand, mission critical, and competitively distinct. This is the time to build capabilities to support such a scenario.
As a researcher and author, how did you balance academic rigor with making the book accessible to a broad audience? Any lessons from this experience that surprised you?
I have been interested in making my research consumable from day 1 of my academic career. In fact, academic-practice bridge-building is a passion of mine, a passion that brought me to MIT CISR back in 2013. The research center just turned 50 years old and pioneered the concept of balancing academic rigor with relevance and accessibility. My experience at the center has taught me the importance of continuously and actively work with leaders in the trenches. I do this primarily by collaborating with my data research advisory board, but I also learn quite a bit about what frameworks work, what stories resonate, and how to define terms by doing talks and workshops and teaching exec ed around the globe.
Your career has been deeply rooted in data and its impact. Looking back, what is one piece of advice you would give to professionals who want to make data more meaningful in their work and careers?
Start using it! It’s hard to appreciate what data can do without a bit of test and learn. As we mention in the book, when you actively use data, you learn and help your organization learn, which begins to power a positive reinforcing cycle that drives data monetization forward.