Gartner’s well-known hype cycle applies to many new technologies, it seems. An initial period of inflated expectations gives way to a deep “trough of disillusionment.” After the trough bottoms out, a “slope of enlightenment” develops as people begin to find practical uses for the technology. That leads to an extended “plateau of productivity.”
In a recent webcast, Pawan Murthy, senior director of marketing for event host Prevedere, applied the concept to predictive analytics and machine learning, which are the company’s business. Without saying so explicitly, he suggested that the technology is now on a slope of enlightenment.
“We’re starting to see best practices involved, all the hype has sort of gone away, and now we’re starting to see actual value out of this,” Murthy said.
Of course, Murthy must be regarded as a biased observer. But panelist John Granato, a veteran CFO who runs finance for Tradesmen International, agreed.
A company can use the forward-looking data that is the output of predictive analytics to “develop a playbook to react to various scenarios of change and their associated triggers,” Granato said. That’s preferable, he added, to “the traditional plan that assumes a relatively static environment and at times encourages gaming the planning process and facilitates the status quo.”
Nonetheless, he added an important caveat: ultimately, the technology itself will not provide a competitive advantage.
“Think back to when the PC was introduced,” said Granato. “Not everybody had one. The people that did were able to drive productivity. But it didn’t take long before everyone had one. Then the advantage was gained by those that could use the tool better, not just have it.”
He said it likely won’t be far in the future when most companies will have access to machine learning, and using it for sophisticated analysis will be the norm. Then, “using it better” will come down to “judgment, knowledge, strategy, and approach,” he said.
Success will be about “utilizing the right data, not just having a lot of it,” Granato continued. “The key will be having the right management team that can decide what data is useful and act on the key outputs.”
Panelist Patrick Slattery, founder and managing director of Canopach, a CFO consultancy, concurred. As an example, he pointed to failures in the direct mail industry.
“They had all kinds of data on uplift and customer behavior,” Slattery said. “But that data didn’t help those businesses avoid the death spiral of status quo thinking. They chased a dying demographic as the landscape around them shifted from print to digital content. Many of them didn’t see the trend coming, or they discounted its potential impact on their business model.”
Granato further noted that having too much data can be just as crippling as not having any. The extreme volume of data that can be accessed today can lead to indecisiveness.
“Someone is always waiting for more data,” he said. “Or analytics teams have to jump from one thing to another without an end-game in mind, a connection between the analytics and how they’ll be operationalized to improve performance.”
Appropriate judgment isn’t only important for identifying useful data but also for selecting technologies, Granato noted: “A good finance leader needs to be tech-savvy, but it’s just as important to understand what technology adds value and make sure the company isn’t just chasing the next gadget. It’s got to be practical.”
Slattery — whose résumé includes stints at Deloitte and KPMG, and he also co-founded Answerthink, which later became part of The Hackett Group — again agreed.
“Ignoring technological advances puts a CFO at a strategic disadvantage, but being too aggressive in technology posture increases risk,” he said.
He added that being successful in finance today requires CFOs “to avoid chasing the bleeding edge of technology while at the same time avoiding the fear of making mistakes.”