CFO Talk: Data-Driven Decisions: Unlocking the Power of Analytics in FP&A Part 2 of 2
If you’ve ever tried to wrangle an Excel spreadsheet that resembles a wild rodeo more than a financial report, welcome home. In the second part of the “CFO Talks” double-feature—“Data-Driven Decisions: Unlocking the Power of Analytics in FP&A”—a trio of heavyweights ride into the analytics arena to tackle the real questions: Why does our road to data utopia keep winding like a Boston cow path, and why are we all hiding our spreadsheets like contraband snacks?
Let’s set the stage: Steve Rosvold (Chief Learning Officer at CFO.University) helms the discussion, joined by analytics sage Prashanth Southekal and FP&A evangelist Bryan Lapidus. Their goal? To chart a straight line (or at least a well-signposted detour) through the sometimes foggy landscape of advanced analytics, data quality, and the age-old battle: humans versus new tech.
Here is the video from Part II, followed by a brief recap of the conversation. (Revisit Part I Here)
Analytics Pitfalls: Paving the Cow Path, Faster
Bryan Lapidus kicks things off with a sage warning: “Not doing it is a risk.” In other words, standing on the shoreline while the data-driven ship sails is a sure way to become yesterday’s news. But the real trap isn’t moving too slowly—it’s using your shiny new analytics toolkit to pave over the same old cow path.
Picture this: your organization upgrades from cobbled spreadsheets to a slick, high-speed analytics solution. But instead of rethinking your processes, you pour that same outdated logic into your new tools. Now, you have messier answers, just faster. As Bryan put it, Boston’s curvy streets are a monument to “paving the cow path”—let’s not make that the legacy of FP&A automation.
Prashanth Southekal’s advice? Flip the funnel. Don’t dump all your data into the machine and hope for “insights” to tumble out. Instead, anchor everything to business KPIs. What decisions drive performance? What data do you actually need to inform those decisions? Without this clarity, you’ll get “80 percent of revenue comes from 20 percent of customers”—a newsflash from Captain Obvious!
The Myth of 100% Data Quality (and Other Unattainable Unicorns)
Let’s address the elephant (or unicorn) in the analytics room: perfect data. Prashanth sets it straight—aiming for 100% data quality is like chasing a rainbow. “It’s a journey,” he says. The business never stands still: mergers, new products, and regulations mean that your data will always be catching up.
Should you try to sanitize every last byte before launching analytics? Not if you value your time (or budget). Start with “good enough.” Focus on completeness (what data truly matters for your KPIs?), consistency (does “customer” mean the same thing across teams?), and uniqueness (how many different ways can you spell Israel?). In other words, let’s not build pristine temples to data when most decisions only need a sturdy hut.
People, Process, and the Spreadsheet Addiction
So, you’ve got a top-notch analytics tool. Why are your people still sneaking off to their old Excel haunts? Bryan spills the beans: in AFP’s member surveys, over half of those with fancy EPM systems are secretly (or not so secretly) bypassing them for spreadsheets. Old habits die hard.
What’s at play? It’s not job security fears, Bryan suggests, but the life-and-deadline dance between “fast” and “good.” Your team can be fast and good with what they know. Throw in a new tool, and the learning curve spikes panic levels—and deadlines don’t budge.
The solution? Time, patience, and peer support. Leaders must offer political cover and psychological safety: “It’s okay if the first AI-powered budget explodes—just learn from it!” providing that safety net and building buddy systems or analytics evangelist communities can turn tech adoption from a lonely hike into a group trek.
The Great AI Leap: Black Boxes, Glass Boxes, and Trusted Companions
No modern finance discussion escapes without peering into the shimmery (and occasionally terrifying) realm of AI. Steve throws down the gauntlet: with AI-generated insights everywhere, how do you trust the black box—and, more importantly, convince your CFO or board to trust it too?
Prashanth offers a reality check: don’t use AI simply because it’s trendy with the Silicon Valley set. First, determine if AI fits—look for standardized, automatable, or optimizable processes. Then, focus on explainability: tools to test fairness and transparency exist, but never ditch the human in the loop. If you can’t explain why the model spat out a $375 million forecast, don’t blame ChatGPT when the board turns pale—use AI as a savvy sidekick, not your CFO body double.
Bryan agrees: treat your AI models like an employee. Audit them, review their “performance,” and keep asking, “Show your work.” Safe hands make for safe numbers.
And don’t believe the myth that AI is reserved for “big tech” or “big pockets.” As Prashanth observed at an Olympic conference: most elite gains are about discipline—getting enough sleep, hydrating, and, in analytics, honing curiosity about your KPIs. The entry price is lower than we think, if we’re willing to exercise our questions.
AI Spaghetti: Stickiest Applications in Finance
Where is AI making the biggest splash? Bryan admits: we’re still “throwing spaghetti at the wall”—with built-in AI tools, chat-based models (ahem, ChatGPT), and custom AI agents all taking their turn as the next big thing. The key is experimentation—use what’s accessible, what integrates with your workflow, and keep an eye on how agents that sit atop your data (as opposed to within business processes) might shift the analytic tectonics.
Prashanth adds a note of caution: don’t get so enamored with building AI agents atop your data that you lose sight of business processes. Analytics must serve a roadmap, not replace it. “Data is an asset only if you know how to use it. If not, it’s a liability—or even a destroyer.”
Championing Data Culture: The Evangelists Lead the Parade
How can CFOs champion a data-first culture? Both Bryan and Prashanth are unequivocal: leadership, not lip service, is your ticket. Walk the talk—celebrate data-driven wins, reward curiosity, and lift up your analytics evangelists. Let them connect, run projects, and bring their gospel back to the masses.
Prashanth goes further: CFOs, stop seeing analytics as an IT sideshow. You already have the knack—working with data, collaborating across the business, and linking actions to KPIs. All you need now is a bit of discipline (“think big, start small, go fast”) and the humility to keep learning.
Final Words: Buzzwords We Wish Would Retire
What finance jargon would our dynamic duo throw in the bin? For Bryan, it’s “AI”—too mystical, not specific enough. Let’s talk about machine learning or language models, not AI as if it’s magic.
Prashanth can’t stand “actionable insights” (unless you can define the action, the resources, and the impact) and “real-time analytics” (newsflash: there is no true real-time, only near-real-time).
The Path Forward: Stop Paving, Start Reimagining
If there’s one guiding star from this FP&A analytics rodeo, it’s this: don’t just pave over the old cow paths with shinier tech. Rethink, reimagine, and bring your people (and their trusty spreadsheets) along for the ride. The analytics future is less about the tools you wield and more about the curiosity, discipline, and leadership you bring to the herd.
Now saddle up—and may your KPIs always point to greener pastures.
Episode Quotes:
Bryan:
“If you’re digging a hole, the first thing you should do is stop digging.”
“When it comes to AI, when it comes to advanced analytics, the technology has been solved. We can do this. It’s the people in the process that are the hang up.”
Prashanth:
“There is nothing called 100% quality data. That situation doesn’t exist. It’s a kind of a unicorn.”
“Data is an asset only if you know how to use it. If you don’t know how to use it, it’s a liability. The same with AI as well. If you know how to use it, it’s an asset. If you don’t know how to use it, it might even destroy your company.”
Steve:
“In part I, we established the critical importance of data and analytics for finance and touched on practical initial steps. In Part II continue the conversation by focusing on implementation challenges for championing a data-first culture.”
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