In the day-to-day crush of collecting and reporting data it’s easy to forget the ultimate reason why an office of finance exists — to help make smarter use of financial resources.
A recent study of finance teams by Ventana Research, a business technology research and advisory services firm, reported that 68% of surveyed employees spend most of their time on non-analytics tasks such as collecting, organizing, and reporting — and only 28% on analytics. This squares with the frequently cited statistic that data scientists also spend only about 20 percent of their time on analyzing data and the rest of their time on finding, cleaning, and reorganizing it.
You might think that this 80/20 ratio should be reversed, given how much value analysis adds to data. In the case of finance, that value comes from the better decisions that result in better business outcomes. If stakeholders don’t understand in a timely way what data means then they won’t know where to steer the organization, and may steer it in the wrong direction.
Among other benefits, analytics lets you:
- Respond opportunistically to positive business events
- Identify which assets are most appropriate to deploy against those events
- Identify and mitigate business risks
- Choreograph the timing of key actions for maximum opportunity or risk mitigation
- Identify best where to cut underperforming or redundant assets
- Improve planning, budgeting, and forecasting based on real-world conditions
Few would disagree that these are all activities on which finance teams and other stakeholders should focus more, and focus more effectively. The key question is, how?
Two Key Barriers
In our work with clients of all types we see two key barriers they have in common that prevent organizations from capturing more of analytics’ enormous economic potential. The first is the amount of time and energy devoted to the 80 percent part of that 80/20 ratio — the financial close and other periodic reporting. This is the mandatory financial accounting work companies need to do just to be companies. The data gathering and organizing involved can be difficult and time consuming with little bandwidth left over for analytics, no matter its potential value.
The other barrier is that the analytics work itself can also be extremely difficult and time consuming — often for the same reasons as for financial reporting. Data from disparate sources — particularly different business entities, different types of business entities, and sometimes different regions with different currencies and accounting rules — is difficult to access and integrate. Another issue (for both reporting and analytics) is the huge and rapidly growing amount of data companies need to deal with, whether they’re analyzing it or just trying to collect it.
Then specifically on the analytics side there is also the challenge of how to extract meaning from all this data, and communicate that meaning to stakeholders. Take Excel, the tool of choice for ad hoc financial analysis the world over. Excel’s usefulness as a productivity tool diminishes rapidly as the amount and variety of data becomes more and more massive. It’s hard to see patterns in the data when you’re looking at it as rows and columns, even if only on a single page.
What EPM Was Built For
EPM software overcomes both these barriers. First, it automates many of the previously manual and repetitive tasks — such as tracking down data, formatting spreadsheets, and constructing (or fixing) functions — that typically comprise the bulk of the financial close and reporting process. So teams have more time left over for more value-adding in-depth analysis. And, second, EPM offers tools that accelerate and empower the analytic work itself — tools like built-in financial models, dashboards, scorecards, and storyboards that can be easily tweaked based on an organizations particular needs. Users can even employ an Excel-like interface (e.g., for ad-hoc analysis) if that makes them more comfortable and productive.
EPM’s built-in analytical tools means that users — whether in finance or other departments — do not necessarily have to be experts themselves in how to build robust financial models or sophisticated visualizations in order for them to benefit from these features. It also means that vital information and analyses quickly get into the hands of business analysts, i.e., those often best equipped to leverage insight based on their direct knowledge of the business. A high degree of interactivity also enables stakeholders to add or modify reports and dashboards rapidly as business conditions change. Stakeholders are empowered to serve themselves rather than wait for IT or finance to help them. Hence, critical actions are more timely and therefore more likely to be successful, with even more time freed up for value-adding analysis.
And that ultimately is what analytics is all about — pushing yesterday’s barriers aside so you start to map out your future today. It’s also worth remembering that the same goes for your technology as well.