Today’s CFO is responsible not only for shareholder performance but also acts as a strategic business adviser to the CEO, helping drive the executive agenda. While financial rigor is necessary, it is not sufficient to deliver the performance results required to meet shareholder expectations or simply deliver on strategic objectives.
Leading CFOs are not waiting to be tapped for insight. Rather, they have become trusted strategists, growth enablers or agents of business transformation. To do this, they are using data analytics to gain insight and make better decisions to drive the business forward. However, in spite of this opportunity and the prevalence of game-changing insight from data, many CFOs are finding it can be very hard to get started.
CFOs have at their disposal many systems that can handle the proliferation of data, including ERP systems, enterprise business intelligence and visualization tools, GRC, and shared-data service functions with analytics. Yet, harnessing so-called “big data” remains a challenge. Two factors undermine success today: 1) access to a diverse dataset and 2) lack of a single platform that aggregates analytic output so CFOs can extract actionable strategic insight.
While no one can tell the future, a smart CFO with the right tools can use predictive analytics to better understand the company’s market, and use that insight for better decision-making in generating growth, protecting profitability, and stewarding capital.
Predictive analytics is basically the application of statistical models to existing datasets that prove or disprove assumptions about the future. The most common form of predictive analysis is regression analysis, in which organizations look at a number of variables from past data to project what might happen in the future.
In retail or manufacturing industries, for example, predictive analytics are deployed to detect emerging trends and build new consumer products. Transportation companies will capture data around engine performance for fuel optimization and safety-related improvements.
Big data is so ubiquitous; it can be overwhelming for CFOs to know where to start with predictive analytics. Let’s take a look at three concrete areas where CFOs can access data and apply predictive models to gain strategic insight and better assess business performance: strategic objectives on operational data, market insight, and the “internet of things.”
1. Strategic Objectives on Operational Data
It may be obvious, but most organizations don’t harvest all the key insights that exist from the data they have compiled right under their noses. When you look at 10-K filings, organizations are driving towards the same strategic objectives irrespective of industry: sustainable growth, supply chain excellence, and a productive workforce. So, pick one, and harvest for that insight.
a. Growth: Gather existing CRM, ERP, marketing, customer loyalty (NPS), support, and usage data around your book of business, and use or define the metrics that will indicate expansion, success, and churn. From there, you can forecast your pipeline and your churn risk, and create a profile for customers that are likely to expand.
b. Supply Chain: Aggregate operational data around procurement and supply chain and profile top- and bottom-performing vendors. Create a fraud profile and score all vendors against it to see which share attributes that make them vulnerable to fraud.
c. Talent: Look across your global entity structure, and analyze your HR data, NPS data, and social media to create a talent profile that predicts where your talent pool is at risk or where it’s performing at a level of excellence. Then, benchmark and trend it over time. These trends will indicate which geography or cohort of tenure might be at risk or which business unit is performing exceptionally. Trends can also identify where you can apply attention or resources to mitigate.
2. Market Insight: Emerging Trends and Risks
CFOs in the retail or manufacturing space know that tastes and trends can vary greatly across geographies. Increasingly, predictive analytics can be applied against social media data to detect trending views on fashion, color preference, and related areas to help determine what inventory to stock where. Conversely, monitoring social media data can be used for the flip-side — to apply predictive analytics to detect and mitigate reputational risk.
CFOs are also driving initiatives like environment and corporate social responsibility by monitoring market and consumer trends and applying predictive analytics to influence everything from packaging to raw material procurement.
3. IoT – Internet of Things: Performance & Safety Insight
Organizations are using metadata from equipment to extract performance insight or indicators to improve safety. Boeing, for example, is capturing large volumes of data — up to a terabyte per flight on the 787 Dreamliner — that will give them insight into how airplane engines are performing.
Logistics and transportation companies are applying similar technology to capture metadata around driving and drivers, and using predictive analytics to identify factors that lead to accidents so they can improve driver safety.
Lastly, businesses like Under Armour are using smart devices and wearables to capture activity and predict enhancements or develop new products and services.
It sounds easy, so why aren’t companies driving more value and change? The CFO has never had more technology available to analyze data. It turns out, however, that there is a big difference between having a lot of technology and having the right technology. I still remember the first time I ran a data-driven google ranking prediction I had no idea what I was doing, but I was excited about getting the information. I was working at a mid-sized company that had a lot of data but was doing very little with it.
CFOs will often have multiple ERPs, CRMs, finance systems, support systems, and various analytics and visualization tools at their disposal. Each of these technologies does a pretty good job within its own silo, but does a horrible job of accessing or aggregating data across the broader ecosystem.
Big data resides across many sources, and few technologies today make it easy or economical to access and aggregate the insight from predictive analytics that rely on that data. While analytics themselves have become a commodity, access and aggregation of data is the harder nut to crack.
There is so much value hidden in big data that CFOs can feel like they’re trying to boil the ocean. The answer is to start with one practical step. Pick an operational area that sits in the top five of your executive agenda, where you have plenty of data sources, and identify the key indicators — performance, risk, control. Then monitor that area. Once you find success, add more of the top five until you have not just more but better insight. Then, you can truly count yourself among the top CFOs who are sought after as drivers of strategic growth and agents of business transformation.
Kris Hutton is director of project management for ACL, a software provider helping governments and companies use data analytics to quantify risk, stamp out fraud, and optimize performance.