Half of enterprises have adopted machine learning (ML) technologies as part of their enterprise business. The rest are exploring it. Clearly, the age of machine learning is upon us.
Nowhere is this more intriguing than in the office of finance, which is where every organization’s financial and operational data comes together. More than merely reporting what has happened, modern finance organizations wield the latest technologies to help their businesses anticipate what will happen.
One of those technologies is ML, which leverages the advantages of automation, scalable cloud computing and data analytics to generate predictions based on historical and real-time data. Over time, you can train your ML engine to improve the accuracy of its predictions by feeding it more data (known as training data). Your ML engine grows even more intelligent through a built-in feedback loop that further teaches the platform by choosing to act (or not) on its predictions.
Predictions Versus Judgment — And Why It Matters
Machines are very good at automating and accelerating the act of predicting. ML makes them even better at it. But judgment is very much a human strength, and it’s likely to remain so for some time. We can program machines to make limited judgments based on a preprogrammed set of variables and tolerances. If you have assisted driving features on your car, then you’re already seeing this in action. These systems are trained to detect potential problems and then take specific actions based on that data.
But it’s important to recognize that these systems are designed to operate in relatively contained, discrete scenarios: keeping your automobile in its lane or braking when your car spots an object in your blind spot. For now, at least, they lack the contextual awareness required to make the countless decisions necessary to safely navigate your way.
For that, you need people.
In a larger business context, situational awareness helps us weigh factors that may not have been ingested by the ML engine. We know to question a prediction or proposed action that doesn’t fit with our company’s values or culture. The numbers might add up, but the action doesn’t. We need people to make that call. A well-designed finance platform will leave room for you to make those calls, because in a world awash in data, even the best ML engines can be fooled by spurious data and false correlations. That’s why ML complements, rather than replaces, humans.
Is ML A DIY Project?
I’ve overseen the development and implementation of ML at two companies — the first to spot potentially fraudulent health insurance claims, and the other to model accurate forecasts and develop insightful what-if scenarios. I’ve learned a lot from the experience.
But my experience may not look like yours, at least not in the details. As a technologist, I was responsible for bringing ML-powered solutions to life working with development teams to incorporate ML into products for our customers. And, there’s every chance that ML will enter your environment via a SaaS (software-as-a-service) financial management or planning platform.
If you’re a SaaS platform customer, the actual implementation of ML in your finance environment may be relatively transparent — a built-in algorithm that drives powerful next-level features, intuits business drivers and helps support decision making (at least, that’s how it should work). But since every ML engine is so dependent on data, and on the decisions you make around that data, you still must address some considerations.
Here are three important ones:
1. Understand where your data is coming from. Your ML predictions will only be as relevant as the data you use to train them. So one of the first steps is to decide what data you’ll want to input into the system. There’s general ledger (GL) and operational data, of course. But how much historical data is enough? What other sources do you want to tap? HCM? CRM? Do those platforms integrate with your ML-driven finance management or planning platform? Sit down with your IT team to craft a data ingestion strategy that will set you up for success.
2. Appreciate the cost of anomalies. No system is perfect, and occasionally yours will output outlier data that can skew your predictions. Understanding and acknowledging what these anomalies can cost your business is critical. In fact, one of the first uses we defined for ML in business planning purposes was to detect anomalies that could unwittingly put decision-makers on the wrong track. We designed this feature to flag outliers so managers can determine for themselves if they want to accept or disregard them.
3. Acknowledge and avoid bias. This is a big one. Whether we like to admit it or not, bias of all kinds affects much of our decision-making process, and it can threaten the success of your use of ML. Say you want your workforce planning system to model the ideal FP&A hires over the next eight quarters. One reasonable approach is to pick your highest-performing talent, define their key characteristics, and model your future hires after them. But if the previous managers tended to hire men—whether they were high performers or not — you’ll be left with a skewed ingest data sampling that is unwittingly tainted by historical bias.
Harnessing the promise and power of machine learning is an exciting prospect for finance executives. Before long, planning systems will operate much like a navigation system for finance teams — a kind of Waze for business. The business specifies its goals, where it would like to go, and the planning system will analyze all available data about past and current business performance, intuit the most important drivers, and offer different potential scenarios along with their relative pros and cons.
Think of ML as a way to make better, smarter use of data at a time when the way forward is increasingly uncertain. For businesses seeking agility, ML offers a way for them to find their true north in the office of finance.