How Data Scientists Can Get the Ear of CFOs (And Why You Want It)
Hey, data scientists! Here’s how to bend your CFO’s ear, equip your company with high-quality analysis, and boost your value and career in the process.
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Data scientists have a world of possibilities at their fingertips. Many of those reside in the realm of business intelligence and data analysis. Disciplines like these can be some of the most valuable in a business setting, where finding growth opportunities and inefficiencies and outmaneuvering competitors are paramount.
Data specialists can get the attention of their company’s chief financial officer (CFO) by learning about the information and reporting CFOs care about. Data science can deliver value-adding and actionable business intelligence and predictions. Here’s how to bend your CFO’s ear, equip your company with high-quality analysis, and boost your value and career in the process.
Know the Value of Different Types of Business Analytics
Business analytics is set to become a $684 billion industry by 2030. Data scientists who can help their companies and CFOs get ahead in this arms race tend to secure their positions and demonstrate their value well.
To do this, data scientists must understand the primary types of business analytics and how they apply to the numbers-driven game of enterprise planning.
1. Descriptive Analytics
This branch of business analytics provides insights into past events, like company performance and broader industry trends. Studying what happened in the past helps companies come to terms with their weaknesses and strengths.
For CFOs, this could include anything from market fluctuations and cash flow problems to employee turnover and spending patterns. Other factors also impact a company’s nimbleness and preparedness for the future.
2. Diagnostic Analytics
Diagnostic business analytics build on the findings unearthed by descriptive analytics. It provides a more granular investigation into company data to find hidden risks and casualties and ultimately spells out why things happened the way they did.
This is a vital part of strategizing for the future. Seeing clearly where inefficiencies occur or where waste happens can make a CFO’s job considerably easier.
3. Predictive Analytics
Predictive business analytics fulfills the promise of amassing organizational data in the first place. Historical information helps data scientists and decision-makers understand the odds of an event or trend reoccurring. In a business setting, this includes predicting workforce growth or decline, considering future changes in demand and buying behavior, and detecting financial fraud or cybersecurity events.
Experts in the financial planning and analysis industry say the methods used by companies haven’t evolved as quickly as many would like. Strong subject matter knowledge in this area translates to desirability in a job market saturated with unmet requests for data analysis professionals. Open job listings by some accounts number around 140,000 in the United States in 2021.
4. Prescriptive Analytics
This type of business analytics is the culmination of the previous ones. Prescriptive analytics takes present-day insights, combines them with reasoned, data-led inferences about the future, and translates them into the language CFOs and other decision-makers care about.
Applying Prescriptive Analytics and Business Intelligence to the CFO Role
Making the translation from raw data to analysis to actionable recommendations for the C-suite is missing a few steps. The most critical of these involves reporting tools.
Data scientists can find objective reviews of business intelligence tools and dashboards easily. They need to know how to surface the information decision-makers care about to speak effectively to CFOs and the C-suite,
Some of the dashboards most likely to be of interest to CEOs, CFOs and other decision-makers include the following:
- Revenue and expense dashboards
- Trend dashboards
- Balance sheet dashboards
- KPI dashboards
- Benchmarking dashboards
- Variance and exception dashboards
Many of the tools on the market today provide templates predesigned for specific business niches. Various products may also include data staging and warehousing functions to organize the available data – which is step one in wringing any added value out of it in the first place.
There are many business areas and critical workflows where this data-led exchange of ideas is becoming a hotbed of innovation and business streamlining. These are some of the places where data science is defining the quest for a leaner, cleaner, profitable and transparent corporate structure:
- Financial planning and business models: Data scientists who understand this area can help companies build responsive pricing structures, know how feedback is used, and track revenue proactively and in greater detail.
- Engineering, research and development: Knowing whether getting a new product or service off the ground is worth the investment of money and materials goes much more smoothly if it’s informed by descriptive and predictive analytics.
- Expansion, scalability, tax and treasury: Launching into new territories or making acquisitions can’t rely even on high-level assumptions. Knowing the likely tax and treasury impacts over time, and studying target demographics to steer the course of expansion, relies on data science and scientists.
- Procurement: Data science is building new tools regularly to help companies track their raw materials and personnel in greater detail. Running a lean operation is part of becoming sustainable, and data science can surface opportunities to make supply chains more efficient.
Can a Data Scientist Become a CFO?
Is there a pathway available by which a business intelligence-oriented data scientist can become a chief financial officer themselves? The answer is yes – and there are success stories describing that progression.
One example saw a specialist in data surveillance systems – designed to spot signs of fraud or target investment opportunities – parlay his knowledge of big data-led financial strategy into the role of CFO. The risk insights and business opportunities leveraged here, and the leap upward in company roles, were possible because the scientist knew how to put “data exhaust” to good use.
Data surveillance and machine learning in the financial tech sector is just one place where the role of data scientist complements that of CFO. Between business intelligence reports and deep-dive analyses into the exabytes of information produced daily by industry, data scientists have a lot to offer decision-makers if they know how to seize the opportunity.
Bio: Devin Partida is a big data and technology writer, as well as the Editor-in-Chief of ReHack.com
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