The 2017 CFO IT Survey
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway,” Geoffrey Moore, author of Crossing the Chasm, wrote.
Certainly, many CFOs, and not just those with web-based businesses, would wholeheartedly agree: data is the sensory information produced by a business that has its eyes and ears on operations and customers. Analytics is the brain that processes the information and provides insight, which ideally leads a company to take meaningful action.
That’s the way data analytics is supposed to work. But using data analytics, as when using most information technology tools and systems, is full of trouble spots, stumbling blocks, and blind alleys. In other words, it requires effort and energy on the part of an organization.
The challenges organizations confront in evangelizing, constructing, and deploying data analytics programs stood out in CFO’s annual IT survey. Called “Data and Analytics: The CFO’s Evolving Role,” the survey was conducted in January 2017 and garnered 202 respondents, of which about one-third were CFOs, one-third a different finance title, and one-third CEOs.
As a promising way to drive decisions and corporate actions, data analytics has a bright future. CFOs are clearly building a solid, quantitative foundation for decision-making, instead of relying on gut instinct and whimsy.
Among the survey participants, 83.6% “agreed” or “strongly agreed” that the strategic decision-making in their organization is already highly data-driven. (Less than 10% disagreed with that statement.) A similar number said the same thing about operational decision-making. What’s more, 66.7% strongly agreed or agreed with the statement that their strategic and operational decisions are not only based on data, but also “informed by sophisticated data analytics” (see Figure 1).
The organizational entity primarily responsible for setting the data and analytics agenda at the responding businesses varied, but the finance function was cited most often, at 46.8%. (That result echoes a survey conducted by Adaptive Insights in November 2016.) Business units set the analytics agenda at 19.9% of companies, and at 16.9% of responding firms the information technology function handles the responsibility.
But at most companies finance is clearly the function responsible for driving analytics into disparate parts of the organization, such as sales and marketing, operations, and customer service. No wonder 70.7% of the CFOs and other finance executives responding to the survey personally plan to devote substantial time to data analytics in the next two years. (Fifty-one percent have already undergone training related to data analytics.) Separately, 68% said they plan to improve their own data analytics skill set in the coming year.
To adapt, CFOs are also reshaping their staffs’ capabilities accordingly: 60% plan to improve the analytics skill set of their existing finance team in the coming year, and 59.6% said they will require strong data analytics skills from new finance team members. (See “Hire Expectations.”)
The need to develop employees’ and management’s skills is a recognition that the age of analytics is still in its infancy. When asked if their finance function should “substantially increase its use of data analytics to support decision-making,” 72.7% of respondents said “yes.” A similar number agreed or strongly agreed that finance should do so “to be better partners across the business.”
As CFO examined in “Master of All Metrics” (January/February 2017), data analytics capabilities naturally reside within the finance department, and they are also a means of spreading finance’s influence. How have things gone thus far? When asked if other parts of the organization see the finance function as having a highly effective data analytics program, 43.7% of respondents said “no” (see Figure 2).
There are many factors that could breed discontent among the ranks. Certainly, many organizations still have execution problems. For example, 28.5% of executives answering the survey said data from across their organizations is not rolled up into a “single version of the truth” (see Figure 3, below). And 21.9% of respondents said their finance function does not systematically communicate data to business leaders, a failure that could easily weaken the clout of a data analytics program.
Finance also can’t ignore that its stewardship of the data analytics function may cause resentment in other departments, especially if finance is viewed as authoritarian. Lamented one respondent: “We are becoming too ‘accountant’ controlled and are declining in our growth and ability to make quick changes because the accountants are driving a total risk-averse culture where no change is safer than a slightly risky change.”
Cultural issues are a key ingredient in the successful application of data analytics. Almost a quarter of respondents cited their “corporate culture” as an obstacle. “Resistance by the operations side of the business” and “resistance from the sales organization” were two written-in responses to the question, “What is the biggest challenge you see in making better use of data analytics in your organization?”
Getting departments on the same page and prioritizing what should be measured are other common challenges. One executive noted his organization’s greatest stumbling block is “clearly defining the data analytics that are germane and critical to the overall operation of the business.” Another cited “the lack of a common definition of data across multiple, vertical business units.” Getting managers to trust the results of an analysis can be a difficult task also. “When the insight from data analytics suggests different findings to the commonly held views, there is a tendency to reject the analytics and use them selectively when it supports the views,” one respondent said about her company.
Winning the backing of senior management was seen as crucial. “Buy-in from the board and department to embrace time and costs associated with data capture” was the primary hindrance for more than one respondent. For others, as one executive put it, the problem was simply collaboration, “having everyone on board on everything, and working together with the least amount of stress.”
Focus and Efficiency
There’s little doubt that technology and systems also are prominent barriers to a highly effective data analytics program. They were mentioned by the most respondents, 34.6% (see Figure 4, below). The challenges include the need for:
- Interoperability between financial and operating data
- Structure, organization, and delivery of information in an agile, digestible format
- The capability to roll up multiple ERP systems into a consolidated view
Clearly, the technology and systems supporting data analytics are still falling short of the (possibly unattainable) ideal, which one executive described as “a low-cost system to report out the meaningful metrics and conclusions automatically.”
But that doesn’t seem to be souring executives on putting the concepts and technologies into practice. One executive said, “[I] cannot think of anything [that is] not valuable with data analytics.” Commented another: “Data helps all decision-making.”
At the same time, executives say the applications of data analytics have to be sharply focused. Many executives have observed their companies deploying data analytics in less-than-optimal situations. Some organizations are unsuccessful with data analytics because they’re applying new technology to old paradigms, for example. When asked what was the least valuable use of data analytics seen in their organization, one executive cited as useless the “‘old school’ operations reports that analyze and report historical performance and trends.” Other examples included annual planning and recreating legacy reports.
Clearly, being efficient about using data analytics is a challenge, with almost 27.7% of respondents selecting “time constraints” as an obstacle to effectiveness, the second-most-cited hurdle after systems and technology.
The least-valuable uses of data analytics can be “time wasted gathering data that wasn’t ultimately necessary or relevant,” as one respondent indicated, or “getting bogged down in the details of the data and taking too long to make a decision based on data,” according to another. One executive pointed to the rollout of systems “without a good understanding of the overall business needs and lack of integration,” which leads to analysts “spending too much time aligning and linking data, instead of working on the insights.” In another organization, the challenge is “culling through the volume of data ‘cuts’ to get at the real actionable information,” a respondent said.
The good news is that some organizations have mastered the technology, cultural, and efficiency challenges and are applying data analytics in forward-thinking ways. Among the most valuable uses of data analytics that survey respondents listed were the following:
- Setting the pricing policy in advertising and marketing functions
- Understanding key customer and profitability data by factory and location
- Targeting the customers most likely to generate the highest profits
- Making investment and resource allocation decisions
- Exposing gaps in performance compared to benchmarked competitors or related industries, which focuses the organization on where it strategically needs to improve
- Pivoting a product suite after months of testing, learning, and working with clients to determine the highest-potential success metrics
As Carly Fiorina, former CEO of HP, said when discussing data analytics, turning “data into information, and information into insight,” can be a long journey for an organization. Finance executives are running into bumps and barriers on the path to effectiveness, but they are also making hard-fought progress.
Vincent Ryan is editor-in-chief of CFO.