As the need to make impactful operational and strategic decisions in real time increases, CFOs are playing a greater role in the adoption and integration of data analytics in their organizations to support data-driven decision making.
Executives and business unit leaders are increasingly relying on insights produced by Finance to better understand enterprise performance. That is, what has happened, why it has happened, what is most likely to happen in the future, and the appropriate course of action to take.
In an era where data is proliferating in volume and variety, decision makers have realized it’s no longer enough to base key enterprise performance and risk decisions on experience and intuition alone.
Rather, this must be combined with a facts-based approach. Which means CFOs must set up modernized reporting and analytics capabilities with one of the main goals being the use of data as a tool for business decision making.
Appropriately analyzed and interpreted, data always has a story, and there’s always something to discover from it. However, many finance functions are failing to deliver value from their existing data analytics capabilities.
There is a misconception that to deliver actionable insights, the function needs more data for analysis. As a result, the supply of data keeps rising, while the ability to use it to generate informed insights lags badly.
Yet it’s not about the size of the data. It’s about translating available data and making it understandable and useful.
In other words, it’s about context and understanding that numbers alone do not tell the whole story. Finance leaders should connect the dots in ways that produce valuable insights or discoveries, and determine for example:
- What is being measured, why, and how is it measured?
- How extensive the exploration for such discoveries was?
- How many additional factors were also reviewed for a correlation?
Further, to use data intelligently and influence better decision making, CFOs and their teams should recognize that most enterprise data is accumulated not to serve analytics, but as the by-product of routine tasks and activities.
Consider customer online and offline purchases data. Social media posts. Logs of customer communications for billing and other transactional purposes.
Such data is not produced for the purpose of prediction yet when analyzed, this data can reveal valuable insights that can be translated into action which delivers measurable benefits.
Often the company already has the data that it needs to answer its critical business performance questions, but little of it is being aggregated, cleaned, analyzed, and linked to decision making activities in a coherent way.
Exacerbating the issue is the mere fact that the company has a mishmash of incompatible computer systems and data formats added over the years ultimately making it difficult to perform granular analysis at a product, supplier, geographic, customer, and channel level, and many other variables.
There is nothing grand about data itself. What matters most is how you are handling the flood of data your systems are collecting daily. Yes, data can always be accumulated but as a finance leader:
- Are you taking time to dig down into the data and observing patterns?
- Are the observed patterns significant to altering the strategic direction of the organization?
- Are you measuring what you really want to know, what matters for the success of the business?
- Or you are just measuring what is easy to measure rather than what is most relevant?
CFOs do not need more data. What they need right now is the ability to aggregate, clean and analyze the existing data sitting in the company’s computer systems and understand what story it is telling them.
Before they can focus on prediction, they first need to observe what is happening and why. Bear in mind correlation does not imply causation.
Yes, you might have discovered a predictive relationship between X and Y but this does not mean one causes the other, not even indirectly.
For instance, employee training hours and sales revenue. Just because there is a high correlation between the two does not mean increase in training hours is causing a corresponding increase in sales revenue. A third variable might be driving the revenue the increase.
Jumping to conclusions too soon about causality for a correlation observed in data can lead to bad decisions and far-reaching consequences, hence finance leaders should validate whether an observed trend is real rather than misleading noise before providing any causal explanation.
Certainly, big data can be a powerful tool, but it has its limits. Not all data is created equal, or evenly valuable. There are situations where big data sets play a pivotal role, and others where small, rich data sets trump big data sets.
Before they decide to collect more data, CFOs should always remember data is comparable to an unexploited resource.
Even though data is now considered an important strategic asset for the organization, raw data is like oil that has been drilled and pulled out of the ground but not yet refined to its finer version of kerosene and gasoline.
The data oil has not yet been converted into insights that can be translated into action to cut costs, boost revenues, streamline operations, and guide the company’s strategic direction.