As we continue to battle our way through the pandemic, uncertainty about the future has made scenario planning increasingly essential to the survival of most organizations. CFOs (chief financial officers) can play a key role in stabilizing the business and positioning it to thrive when conditions improve, directly contributing to the organization’s financial health and resilience.

To fulfil this role, the CFO and finance function need to deliver actionable information that leads to creating value and preserving decisions. This requires:

  • Well governed, good quality, internal and external data to ensure plans, budgets and forecasts are realistic, and reporting is accurate and meaningful
  • Events driven planning and forecasting, allowing the organization to respond quickly via astute scenario planning, creating competitive advantage 
  • The ability to make insightful business decisions in real-time

A recent survey by Forbes suggests that CFOs’ optimism is on the rise and they are preparing for explosive growth in 2022[i]. Taking advantage of their growing influence, CFOs need to leverage EPM. KPMG’s 2021 EPM survey shows that 72% of mature EPM clients have already embarked on their EPM journey[ii] through supporting centers of excellence, predictive forecasting and upskilling their employees. They have witnessed a 20% increase in revenue over the past three years[iii].

EPM – The basis of successful business partnership

Enabled by technology, EPM is an enterprise-wide capability that provides a 360° business view to translate strategy into action for improved performance. It allows businesses to holistically align their strategies with plans and actions.

EPM has been around for years in various forms. Despite its rewards, it is a challenge for finance and accounting professionals as understanding and communicating value creation is an iterative undertaking, not an exact science. However, EPM has become a necessity for most organizations since the pandemic started, and has been pushed up on their agendas.

The evolution of EPM

Sixteen years ago, FP&A (financial planning and analysis) was a team within the finance function that was notorious for working late nights and using PowerPoint, whilst the rest of finance were managing their own versions of “death by spreadsheet”. Leaders across organizations struggled to interpret insights from finance.

Today, EPM has evolved. According to Gartner, 54% of finance organizations still struggle to provide data that supports stakeholders’ decisions[iv] despite advancements in modern analytics and business intelligence (A&BI). Insights often lack context and are not easily understood by most users who spend their time in predefined dashboards, which FP&A teams spend hours populating manually. In some organizations systems are outdated, and budgeting platforms still require up to four months to complete a budget.

Globally, organizations who embarked on their EPM journey have shifted their EPM offering from ‘reactively descriptive’ to ‘forward looking prescriptive’. Spending more time providing the forward-looking guidance that management needs to capitalize on the next opportunity provides more meaningful insights and better decision making. This can be achieved through predictive and prescriptive analytics.

In the UAE, this shift is less prevalent. The evolution of EPM can be described through four key phases:

  1. descriptive 
  2. diagnostic 
  3. predictive 
  4. prescriptive

Descriptive analytics

This is the simplest and most basic form of analytics, using data mining and data aggregation. Descriptive analytics is used by 90% of organizations today[v]. It answers the question “what has happened?” analyzing real-time and historical data for insights on how to approach the future by learning through previous success or failure.

For example, companies may analyze consumer behavior and engagement with their businesses by mining historical data to identify and address areas of strengths and weaknesses. This type of analytics uses tools like MS Excel, MATLAB (MaTrix LABoratory), STATA, etc.

Diagnostic analytics

This is performed on internal data to understand the “why” behind what happened and obtain an in-depth insight into a given problem, provided they have enough data at their disposal to identify anomalies and determine relationships within data.

For example, to understand why an organization missed their profit margin goals, they can drill the sales and gross profit down to various product categories. Healthcare organizations can understand the influence of medications on a specific patient segment.

Predictive analytics

This is performed to answer the question “what could happen in the future based on previous trends and patterns?” A particularly relevant example where predictive analytics finds application within financial institutions is in producing credit scores.

In a recent KPMG EPM survey, 54% of respondents are planning to implement predictive modeling in the next 12 months[vi]. Gartner says that 1 of the 3 key strategic actions for success for the CFO is to unlock the value of AI (artificial intelligence) and predictive analytics[vii].

Predictive analytics requires the collection of contextual data. It relates it with other user behavior datasets and web server data to get real insights. This provides better recommendations and more future-looking answers to questions that cannot be answered by BI. It also helps predict the likelihood of a future outcome by using various statistical and machine learning algorithms.

However, the accuracy of predictions is not 100%, as it is based on probabilities. To make predictions, algorithms fill in the missing data with the best possible guesses. This data is pooled with historical data present in various enterprise wide systems to look for data patterns and identify relationships among various variables in the dataset. Data scientists are required to develop statistical and machine learning algorithms to leverage predictive analytics and design an effective business strategy.

Predictive analytics can be further categorized as: 

  1. Predictive modelling – what will happen next, if…
  2. Root cause analysis – why this happened
  3. Data mining – identifying correlated data 
  4. Forecasting – what if existing trends continue?
  5. Monte Carlo simulation – what could happen?
  6. Pattern identification and alerts – when action should be invoked to correct a process

Moreover, organizations may leverage predictive analytics to:

  • Identify trends in sales based on customer purchase patterns
  • Forecast customer behavior
  • Forecast inventory levels
  • Predict products that customers are likely to purchase together
  • Offer personalized recommendations
  • Predict the number of sales at the end of each quarter or year

Prescriptive analytics

Prescriptive analytics is the next step of predictive analytics: it incorporates an additional dimension of manipulating the future. It leverages both internal (within the organization) and external (e.g. social media data) data advising on possible outcomes, and results in actions that are likely to maximize key business metrics. It uses simulation and optimization to ask: “what should a business do?”

It is an advanced analytics concept based on simulating the future, under various sets of assumptions, allows scenario analysis—which, when combined with different optimization techniques, allows prescriptive analysis to be performed. The prescriptive analysis explores several possible actions and suggests actions depending on the results of descriptive and predictive analytics of a given dataset.

Prescriptive analytics is comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage. Prescriptive analytics, if implemented properly, can have a major impact on business growth. Organizations could use prescriptive analytics to schedule inventory in the supply chain, and optimize production and the customer experience.

Aurora Health Care system saved USD 6 million annually by using prescriptive analytics to reduce re-admission rates by 10%[viii]. Prescriptive analytics can be used in healthcare to enhance drug development, finding the right patients for clinical trials, etc.

When adopting EPM across the layers of the operating model, organizations must decide on whether they should:

  • Embark on this in-house or outsource 
  • Recruit or upskill their current teams
  • Purchase the right tools and invest in fixing their data or start revisiting their strategy

What’s next?

The aspiration is for EPM to enable finance leaders to build a future-ready finance organization. Entities should consider whether their EPM capabilities enable them to be RAPID:

  • Recommend: make future recommendations to channel business growth 
  • Ask: insightful questions to look based the data as even data is subject to confirmation bias which is the tendency of the brain to latch onto information that is in alignment with its expectations
  • Predict: make sensible predictions based on trends, what if scenarios and driver-based planning 
  • Inform: the business and drive business strategy 
  • Decide: co-pilot with the business to make future decisions

Ask yourself these questions to evaluate if your organization needs improved EPM capabilities

  1. Does your executive team have real insight into the group’s true profitability by product, service/channel, country/region and customer?
  2. Is your organization combining financial, operational and customer data to make better decisions and create a competitive advantage?
  3. Are you able to anticipate future regulatory changes and use insights to gain entry to new markets using innovative channels faster than your competitors?
  4. Do you know which channels currently provide the best growth and profitability?
  5. Do you have a plan for optimizing these challenges?
  6. Are you able to conduct collaborative planning across all of your business functions? 
  7. Are you able to optimize investment decisions and improve shareholder return while maximizing efficiency?