Financial planning and analysis (FP&A) provides essential insights into decisions that impact every organization in every industry. No matter where you fit into the world, you have to manage money, and that requires a good plan if you want to succeed.
Today, FP&A is more powerful than ever, riding on the shoulders of technological advancements.
Technological Transformations in FP&A
Technology moves fast, and in terms of FP&A, the biggest advancements right now all relate to big data and analytics. You can collect more information than ever before, and the tools are actually powerful enough to make use of that information.
In particular, this technological revolution reflects in three specific aspects of FP&A: extending horizons, increasing frequency, and improving planning.
When you have more data, you can find stronger trends. The idea is that larger numbers create inertia, and your predictions become more valuable.
It’s easy to understand in practice. Imagine a political poll that only asks two people how they plan to vote. That’s not a good data set, and it offers no predictive power.
Offer that same poll to 10,000 people, and you might have a better view of the contest.
This applies to all forms of statistical analysis. Big data gives the larger poll, and that allows it to collect more information and more points of comparison. As a result, you can aim your planning and forecasts further into the future.
You can pre-plan for a wider range of possibilities. You can anticipate black swan events and global shake-ups. That makes it easier to constantly adapt, and it empowers your financial planning.
One of the most transformative aspects of modern data and analytics is the prevalence of real-time monitoring. Once upon a time, you might do a quarterly review to keep track of emerging trends.
Today, all of your data gets processed extremely quickly, and you can have a real-time view of anything you want to analyze. On top of that, your tools chart backward from the present so you can see exactly how things have changed. You can even put your projections on the same graph.
The point is that you no longer have uncertainty tied to analytical frequency. It’s constant, and that means you always work with up-to-date information.
Big data can observe more metrics simultaneously. The tools can even scrutinize a sea of metrics to create a hierarchy that shows you which metrics matter the most — depending on what you care about informing.
Let’s clarify that idea.
Say you want to model the stock market to make sound investments (or investment recommendations). You can measure obscene amounts of data related to companies on the exchange in order to make picks. How do you know which metrics actually matter?
You plug your metrics into a neural network and allow the model to play with each metric as an independent variable. Then, you can see how much each variable impacts the final output, and you can rank your countless variables according to what impacts the value of a company the most.
This is a specific example, but it shows you how big data can review itself to make sure you’re paying attention to the right insights.
The result is that you can better anticipate trends. On top of that, with your wider net, you can also use better forecasts. The next time a global crisis changes economies around the world, you can have a plan in place that has already analyzed the specific metrics related to that crisis.
Enhancing Forecast Accuracy
We can look more specifically at forecast accuracy to understand how it changes the game for any industry, including FP&A.
In most cases, expanding the scope of forecasting and models increases the likelihood of making useful and accurate predictions.
As an analogous example, we can look at hurricane forecasting. As weather observation and analytics have improved, the range of where a hurricane might make landfall (and with what intensity) has actually widened.
Compare a forecast chart from the 90s to the most recent season, and you’ll see that the forecasts start sooner and account for a much wider range of possibilities. The “cone” of landfall is usually much wider in modern forecasts.
As a result, modern forecasts are more accurate. It’s quite rare for a hurricane to make landfall outside of the prediction windows these days.
Now, you might say that’s just because the forecast window is wider, so of course it’s right more, but you can drill down into this to see how forecasting benefits from this approach. In modern predictions, you can see probability gradients. A hurricane might have a 40 percent chance of landing in a certain time window at a certain location. Then it might have a 10 percent chance in a different forecast window.
With the probabilities, you can easily see how the forecast is accounting for different variables, and that allows you to make more informed decisions.
Wrapping up this example, hurricane forecasts are more useful. Evacuation plans work better. Fewer people get hurt (relative to the size of the population), and areas tend to recover from hurricanes faster. The wider scope nets positive outcomes.
Bringing this back to financial planning and analysis, big data allows you to cast a wider net. Whether you’re observing the stock market, customer trends in your industry, shipping impacts, or anything else, your wider net improves accuracy and even creates probability gradients that account for so much more than lesser forecasts.
Facilitating More Strategic Decision Making
At the end of the day, big data is there to inform decisions. Otherwise, what’s the point?
You’ve seen that larger data pools, greater numbers of metrics, and broader analytics create better models and give you more information. Managing all of that can prove difficult, but the last step in facilitating strategic decision-making brings it all together.
By simply binning your insights into useful categories, you can inform decision-making with reduced cognitive load, despite the increase in information that you process.
Here’s an example. Consider splitting all of your big data insights into four categories: descriptive, diagnostic, predictive, and prescriptive.
Prescriptive insights certainly inform your decisions, but you can look back into the other bins to map the prescription. You can see exactly what was measured, how it impacted processes, how that shapes forecasts, and why it led to the conclusion you are considering.
At a glance, you understand what the decision will impact and how, and you can even steer your decisions into the regions likely to yield the most power for your desired change.
That’s the final impact of big data in FP&A: meaningful changes that stem from informed decisions.