The Two Types Of Chief Data Analytics Officers
Over the past 10 to 15 years, businesses have witnessed the rise of the “self-made” chief data analytics officer (CDAO), whose singular qualification was a willingness to delve deeply into data to find economic opportunity. Although often lacking in a quantitative and technical background, these bootstrap CDAOs became analytics leaders within businesses when no one else wanted or was able to do so.
The CDAO role is not confined to the collection and maintenance of the data; rather, it sits squarely in collecting and analyzing the right data to improve decision-making, innovation, and find more economic opportunity. It’s the application of data that has opened the pathway for self-made CDAOs.
Today, increasingly, CDAOs are now coming from formal analytics backgrounds, having graduated from relatively new analytics programs ranging from undergraduate to PhD level. After working their way through the ranks, these formally trained professionals aspire to – or already have – become data leaders. As a result, analytics leadership talent is coming to an inflection point. Should organizations hire and promote more self-made CDAOs, or should formally trained analytics talent take on the role?
Perhaps the answer lies in a hybrid solution. Having been immersed in the CDAO community over the past few years, I’ve concluded that it’s reasonable to look for leadership in the data space from a variety of places, including from within business operations. These bootstrap CDAOs combine critical thinking, business acumen, and a solid understanding of problem-solving and decision-making using data. At the same time, they can work with formally trained data professionals. The result would be a best-of-both-worlds leadership approach to leveraging the power of data in business.
A Bellwether of Big Data
There is no shortage of data and analytics leaders today. In fact, the CDO/CDAO role has become a bellwether of the growth in Big Data among companies, with one recent survey showing that 65% of large, data-driven firms have appointed a CDO, up from only 12% in 2012. This evidence points to big data having been “absorbed into the mainstream during this decade,” the survey authors wrote. Similar, authors of a recent Harvard Business Review article observed, “In general, this trend reflects a recognition that data is an important business asset that is worthy of management by a senior executive.”
Many of the CDAOs I have worked with and who lack formal training in analytics describe their progression as having taken responsibility for an area that no one else wanted to take on, such as tracking customer behavior or figuring out how to keep data stored in something more reliable and accessible than a spreadsheet. Over time, as they were able to show the value of analytics, and the rest of the organization turned to them for help, including for good analytics and critical thinking.
Most self-made CDAOs seem to possess a natural talent for finding economic opportunity using data. One analytics leader I know attributes her career success primarily to frustration. A former mathematics major, she was leading a business development function when she asked her company’s IT team how she could access the data she wanted to make decisions around allocating resources, targeting the right prospects, and so forth. With a blank stare, she decided to collect the data herself, at first in an Excel spreadsheet. As she began to use and analyze these data, she refined her choices of how and where to collect and house the data, as well as the best tools to use for analysis.
Observing what she was doing, leaders of other business units asked her to help them to do the same. She agreed, but on one condition: they had to convince her that the problems they were trying to solve could, in fact, be addressed with data—and that outcomes would truly help accelerate the business in some way.
As this story illustrates, the passion for most bootstrap CDAOs lies in making the business better; the data are the vehicle for doing that. And that’s a distinguishing characteristic that cannot be underestimated.
Data Analysts and the Need for Business Acumen
This is not to say that data science professionals, who have worked their way up through the ranks of data science and analytics departments, shouldn’t aspire to become CDAOs. Those who do, however, often need to develop their ability to find analytics economic opportunity. Although they excel in data analysis and technology, it takes more than technical knowledge and methodological expertise to tackle today’s business problems:
† Bringing value to the business. When I teach data scientists, I tell them that the most important differentiator when it comes to attaining a leadership position will be their ability to identify how their work can add value to a business—beyond the technical execution of a project. And, when I teach business leaders, I explain that helping build a data analytics team and mentoring analytics leaders requires that they involve the data specialists in finding and scoping economic opportunities. As with any skill, finding meaningful analytics economic opportunity requires practice.
† Focusing on more than just the data. Sometimes, dates get in the way. In my experience, many formally trained professionals love to explore data and have no shortage of ideas of how data could be helpful. The problem, too often, is that they get enamored with the potential to build models that are interesting but may not necessarily be helpful. I know this all too well. Give me a rich data set and I can spend hours building models that do nothing more than answer questions that pique my curiosity. For the data-savvy, there’s nothing quite as exciting as looking at the screen and realizing that you are generating truly new knowledge. Interesting, but not helpful for improving business outcomes.
† Developing leadership skills. Finding economic opportunity is not the only important analytics leadership skill. CDAOs also must be excellent communicators, able to articulate analytical value. They must be great business leaders, more broadly, knowing how to manage people and teams, and set and achieve appropriate goals. CDAOs must also be able to partner with other C-level leaders in developing and executing business strategies.
It bears mentioning that not every formally trained data scientist wants to become a data science leader. Those who want to work on and get better at data science problem-solving will likely be in great demand for a long time. The bottom line for all CDAOs is that analytics success always starts with identifying an opportunity where data and analysis can meaningfully improve something of value to the business.