3 min read

Finance grads: This is not your parents’ workplace

Tony Levy

Tony Levy is Anaplan’s Global Head of Finance Solutions. He has a passion for helping clients drive business results through the strategic application of corporate performance management software. He has more than 20 years of experience in the finance and supply chain software industry and has played multiple leadership roles in product strategy, product marketing, product management, business development, and industry marketing.

The emergence and application of machine learning, artificial intelligence, and advanced analytics in finance represents a tremendous opportunity to impact business outcomes. For recent college graduates in the field of corporate finance, these new tools help equip them to join a workforce focused on data-driven, actionable insights, and enterprise-wide collaboration.

Financial planning and analysis (FP&A) careers are evolving with a stronger focus on analytics, especially strategic analysis. FP&A professionals can leverage dimensionally rich data through machine-learning processes and advanced analytics, causing both professionals entering the field and established finance leaders to seek out and master new analytical capabilities.

Six valuable proficiencies for today’s FP&A professionals

Advancing technology helps organizations anticipate and react to heightened market volatility, uncertainty, and risk. Regulatory changes, economic uncertainty, and business model innovation require finance leaders to shift their mindset from hindsight to foresight.

Looking ahead, finance transformation will be shaped by investments in both operational efficiency and business partnering. For finance professionals, functioning as a record keeper using manual, spreadsheet, and legacy IT systems to try to deliver timely and reliable business insights is no longer feasible. To stay current and ready for the future, the following skills are paramount for new graduates and FP&A professionals looking to sharpen and advance proficiencies during their careers:

  • Synthesize financial, operational, internal, and external data
  • Infer trends from dimensionally rich data to distinguish the “signal” from the “noise”
  • Generate insight that is timely, relevant, reliable, and actionable
  • Identify weak assumptions and the likely source of bias
  • Create and communicate a story based on insights that influence management decisions
  • Use insights and storytelling to drive collaboration from the head office to the business units, and across finance, marketing, sales, supply chain, and operations.

Developing and improving upon this new skills set can enable savvy finance teams to help drive profitable growth, address risk, and adapt to uncertain market conditions.

The shift from hindsight to foresight

The development of this advanced toolset enables finance teams to dynamically incorporate their core FP&A processes with timely, reliable analytic insights. Such skills allow the business to adopt best practices such as incorporating leading indicators into scorecards, integrating external, and non-financial data into dashboards, focusing on relative targets rather than only on fixed targets, incorporating business drivers easily into plans and analyses, and forecasting beyond the fiscal year boundary.

In the future, finance teams can expect to use their data in more meaningful, high-value ways. Here are some additional examples of how finance teams can leverage strategic decision-making through the capabilities of analytics:

  • Multi-dimensional analysis of data can help finance teams recognize trends from dimensionally rich data. As an example, red golf shirts have been selling better in Atlanta for the past 12 months, whereas blue t-shirts have been selling better in Phoenix for the same period. Dimensions of color, type, and city can be analyzed quickly to spot trends that inform actions and get reflected in updated forecasts.
  • Statistical models applied to historical data can help create reliable baseline forecasts. For instance, a time-series regression model is applied to 12 months of sales data in order to project a baseline forecast for the next six months. Human judgment around the impact of initiatives that have yet to take hold are added to this baseline to create the final updated forecast.
  • Charts and graphs can be created to measure monthly forecast error and identify bias (i.e., cognitive, social, or motivational). Forecasting processes can then be redesigned to reduce the likelihood of bias. For example, you can actively encourage views that differ from the norm during forecast review meetings to minimize the impact of social bias, such as the tendency to conform to the group.
  • Machine learning processes can be used to help uncover demand drivers from external and internal data. For instance, weather, social media, and historical sales data are quickly analyzed to discern their impact on sales. This information is ultimately used to help finance create more reliable forecasts.

Eventually, FP&A teams can begin to reach beyond finance, connecting finance’s processes to supply chain, sales operations, and marketing, and out across the enterprise to align corporate objectives with financial plans that are linked to operating tactics and market events.

What does the impact of technology look like for the future of finance? Watch the on-demand webinar, “Future of finance: Seven predictions for CFOs,” to learn how to prepare your organization for the impact of digital transformation.

Future of finance: Seven predictions for CFOsWatch webinar