The FP&A Trends Webinar: Mastering Analytical Transformation with FP&A Trends Maturity Model
Click here to view details and register
The FP&A Trends Webinar: Mastering Analytical Transformation with FP&A Trends Maturity Model
Click here to view details and register
By Francesco Morini, Director - Innovation at CCH® Tagetik
At its core, the phrase "data-driven" means acting based on what the data tells you. Organisations are increasingly adopting data-driven approaches to decision-making. This is natural, given the amount of data we now have on hand. To match the demand, software providers are touting products that claim to facilitate this metric-centric decision-making. All-in-all, data-driven is now perceived as the right way to do business. If you're doing "data-driven decision-making", you're doing it right.
But are you really?
As we let the data determine more and more actions, we must keep in mind that "data-driven" doesn't necessarily mean "data accurate." Nor does it mean "data-efficient" or "data masterful."
Indeed, I've seen data-driven strategies that run the gamut. Some were good, some were bad, and others were just ugly.
This article will look into how to set up a good data-driven strategy and how to choose the best Predictive Analytics solution.
To help you determine where you land on the data-driven decision-making spectrum, I've made this handy chart to support you in your next steps.
I believe the best way to look at this is to understand the path towards an ideal data-driven strategy boosted by Predictive Analytics.
The crème de la crème of data-driven strategies is Predictive Analytics — specifically Predictive Analytics with explainable predictions. (I'll explain this concept in a bit.) Predictive Analytics produces precise projections to help shape decisions, guide course corrections, and redirect resources to productive activities.
In other words, a conscious and consistent journey towards Predictive Analytics will put you on the track towards — not just good — but exceptional data-driven decision-making.
When executed correctly, Predictive Analytics has the power to leverage all kinds of data and confer predictive power on every financial process.
Previously, companies used external consultants and data scientists to build and utilise predictive functionality. The burdensome, costly nature of this approach still lingers in finance's imagination. Yet, times have changed. Although predictive technology has matured beyond recognition, there are several things that a Predictive Analytics solution must do:
Predictions are only half the battle when it comes to making data-driven decisions. The other half? Understanding what is driving your performance and impacting most of the predicted outcomes.
For example, it’s helpful to know a product line’s predicted revenue. But it’s more beneficial to understand that your marketing campaigns and discount policy are the drivers of that revenue. This way, you could invest more in what's working and less in what's not and apply your insights to neighbouring initiatives.
In my eyes, leveraging a predictive solution without explainable predictions is like providing a cart without a horse. It lacks a driving force. That’s why it’s essential to recognise the main two types of Predictive Analytics software, as follows:
I've seen many organisations fall victim to shiny and new Predictive Analytics solutions that make data-driven decision-making more of an IT chore than a finance weapon. I suggest that, when you're vetting a Predictive Analytics solution or building your requirements for a data-driven strategy, be wary of this Artificial Intelligence and Machine Learning (AI/ML) technology red flags:
Crawl, walk, run! Don't let perfection be the enemy of progress. Even as an end goal, Predictive Analytics becomes a baseline for improved automation, data synthesis, and the drive to underlie more predictive technologies under more processes — so it's ok to start lean and slow with what you have.
The point is: if you have even minimal data requirements and implement Predictive Analytics software that includes explainable predictions, you'll still benefit from understanding performance drivers and automation, even if the predictions aren't 100% spot on.
We need to remember that our decisions are only as good as our data, and our data are only as good as the technology we use to understand and act on them.
When executed according to the framework and principles I've laid out for you here, the journey to predictive itself will result in data-driven decision-making based on data-accurate, data-efficient, and data-masterful financial processes.
To watch an FP&A Trends webinar on managing uncertainty with FP&A Predictive Analytics, please check out this link.
In the first FP&A Board Connect, Takeshi Murakami, Business Manager to CEO/President at Microsoft Japan, a speaker...
In this 7-minute presentation, Stefan Spiegel, CFO at Swiss Railway Freight Logistics (SBB Cargo AG), explains...
This article details the main benefits and drawbacks of predictive planning. It provides recommendations on where...
A Global FP&A Trends Webinar that was held on the 10th November 2020 focused on why...
‘If I had an hour to solve a problem and my life depended on the solution...
As I walk around various offices or even in social gatherings, I find many conversations about artificial...
We will regularly update you on the latest trends and developments in FP&A. Take the opportunity to have articles written by finance thought leaders delivered directly to your inbox; watch compelling webinars; connect with like-minded professionals; and become a part of our global community.