Five Keys for Financial Planning & Analysis When Applying Predictive Analytics

Today, businesses of varying sizes, products and industries are actively applying predictive business analytics to improve customer engagement, thus resulting in increased revenues and profits. Often these businesses look to their financial planning and analysis (FP&A) group to guide, if not lead, these efforts to successful outcomes. 

According to the 2017 CFO IT Survey, over 70% of finance executives said they plan to substantially increase the use of data analytics in the next two years, to support decision-making and improve business partnering. Fully 68% of respondents plan to improve their data analytics skills in the coming year.

Consequently, it is essential that FP&A professionals understand what it is going to take for them to succeed. 

The ultimate value of all that data will only become evident if the people who know how to think about data can access it easily, explore different views, test various hypotheses, and share their findings effectively

Here are the five keys to applying predictive business analytics.

One: Framing the question or outcome

Framing the question properly will better illuminate the resources, data requirements, tools and methods that need to be selected to solve the question and provide insight to decision-making.

Two: Structuring the team

It is important to bear in mind that assembling the right team is critical to success. The team structure should include key skillsets as illustrated below:

   Data management

  • Understand data
  • Integrate and manipulate
  • Know structured content and hybrid data
  • Understand data quality and standards

   Data scientists/modelers

  • Know analytics
  • Understand techniques and model development
  • Diagnose/validate models (“Trust, but verify”)
  • Perform data discovery and visualize results

   Business analysis

  • Focus on the business
  • Information requirements
  • Create business value from insights
  • Enable management review and decision management

Three: Discovering insight

Drawing insight from a piece of data involves understanding how it fits into the larger picture of an organization (i.e. context), explains Jeff Jonas, IBM’s Entity Analytics chief scientist.

Business environments aren’t the only ones that require context; context is a necessity for any attempt to know more by examining data. According to the renowned statistician W. Edward Deming: “Information is a means to an end. The ultimate purpose of collecting data is to provide a basis for action or a recommendation.”

During data discovery, there often is a need to refine the model results. Several techniques are particularly helpful, such as back-testing, or using historical data to test/validate known outcomes.

Black Swans, events that happen by surprise, have a major effect, and are often inappropriately rationalized after the fact with the benefit of hindsight. Gray Swans, events that can be anticipated to a certain degree but are considered unlikely to occur, may impact the overall market.

Also helpful are latency adjustments, which consist of adjusting for time shifts or unanticipated delays.

Four: Applying tools and methods

FP&A project leaders should consult with their data-scientist team members as to which method should be used to model the question and its possible outcomes or implied actions.

For example, Netflix and Amazon use collaborative filtering by utilizing the alternating least squares technique. Healthcare providers are applying predictive medicine using machine learning (i.e., how computers learn without being explicitly programmed).

These techniques are used to support predictive analytics and, more recently, prescriptive analytics.

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