The Evolution of the Chief Financial Officer

Financial Leadership in the Age of Analytics

The Evolution of the Chief Financial Officer

The historical view of the Chief Financial Officer (CFO) as a fastidious bean counter detached from the nuances of operational divisions is as far removed from the reality of modern corporate finance as paper ledgers and mechanical calculators.

With the continued evolution of financial and accounting software, the nature of finance has shifted from transactional and historical to real time and analytical. CFOs are still responsible for traditional finance activities like financial planning and analysis (FP&A), audit, compliance, and treasury management, but in the era of big data, effective CFOs must also become masters of business intelligence.

The key to meaningful business intelligence is the effective use of data, which has moved out of departmental information silos and into the operational realm. Once considered little more than a by-product of a company’s business processes, data has become fuel for innovation and improvement.

In order to make effective use of company data, the modern CFO must understand the fundamentals of her company’s business. She must be more than a financial specialist, becoming an expert at using predictive insights to harness company data to drive corporate decisions.

Finance departments have a distinct view into all aspects of the company, which puts the well-informed CFO in a unique position to combine business operations knowledge with financial insights. Further, the adoption of data analytics is a natural fit for the finance department, where analysts are accustomed to finding trends, patterns, and meaning in numbers. Just as in financial analysis, business analytics reveals trends, risks, and opportunities. With more information, analysts can use data to further refine not only financial models, but also to identify risk and regulatory issues, increase productivity and efficiency, and evaluate new business opportunities.


Businesses across all sectors are awash in an ever- increasing wave of data that continues to increase in volume, variety, velocity, and veracity. The exponential growth comes from sources ranging from traditional databases and cloud-based systems to the public internet, Internet of Things (IoT), and social media.

Businesses without a plan to use this wealth of data will likely drown in it.

Adoption and use of new technologies, including cloud- based accounting platforms, Enterprise Resource Planning (ERP), machine learning, and other integrated process automation platforms will be integral to any successful use of an ever-increasing volume of customer and business data.

Chris Argent also highlights how the CFO skill set is shifting to the masters of business intelligence in The Digital CFO Skill Shift – Evolution of the CFO Suite


The proliferation and availability of business data has changed the way companies of all sizes evaluate business processes, opportunities, and threats.

While solid financials will always be the ultimate yardstick on which a business is measured, there is a growing expectation that CFOs take on the role of data scientist, providing real time valuable and predictive information between monthly close reports.

The agile CFO must shift focus to where the company is going—not where it has been. Too much time looking backward at lagging indicators opens the door for more nimble, forward-looking organizations to gain a competitive advantage.

With access to and understanding of consolidated data, the CFO is able to collaborate with other divisions to establish and guide Key Performance Indicators (KPIs) and metrics to connect data to core business issues. By establishing a data-driven corporate culture, the CFO can effectively drive results by leading an organization that is intelligent and responsive to data, which is used not only as a predictive tool, but as a key measurement of business success.

Effective use of big data to develop meaningful business analytics programs requires stakeholders to embrace the technologies of data science. The most valuable CFOs are those who adopt and cultivate their new supplemental role as “Chief Data Officer,” taking responsibility for using data and analytics to define key metrics and provide thoughtful interpretations of data from across departments and industry

To implement a successful business analytics program, finance departments must understand the extent of data available to the business across disparate systems, work with IT to make data available across all departments, establish data analytics and visualization techniques, and ultimately deliver actionable information to key decision makers.


The Evolution of the Chief Financial Officer

The good news is CFOs are not being asked to do more with less. They are being asked to do more with more. Smart CFOs embrace technology to shift the focus of their team from transactions and reports to strategic thinking.

In order to achieve this new level of business intelligence, CFOs must embrace digitization to increase efficiency and data visibility.

The first step in establishing a data-driven culture is to increase automation to let software handle back-office transactional jobs, which will allow greater human focus on strategic initiatives and advanced analytics. This may require an upgrade to the company’s accounting and finance software. For those who haven’t already, it is time to move away from legacy systems and reliance on spreadsheets and adopt new technology to improve efficiency by automating the accounting process as much as possible.

Among the key benefits of finance automation are the reduction or elimination of manual business processes, reduction of data entry errors, the removal of human bias from analysis, and a move toward continuous accounting, which allows organizations to run most monthly close tasks in real time.

Through the use of modern ERP systems and big data platforms designed to deal with the vast amounts of data available, CFOs can drive greater business intelligence at a lower cost than legacy approaches that relied on data warehouses and siloed departmental information platforms

Through careful selection and implementation of an effective ERP system, companies can integrate business applications to automate back-office functions related to technology, services, and human resources. Areas ripe for automation include product lifecycle management, supply chain management, customer relationship management, sales order processing, and decision support systems. This integration will allow organizations to leverage data across the company— eliminating data silos and turning data into actionable information.


Just as cloud-based accounting revolutionized corporate accounting by offering previously unimaginable levels of collaboration, accessibility, efficiency, timeliness, and security, new technologies like machine learning will lead the next revolution in accounting and finance

Through machine learning, time spent by humans scouring data for trends and correlations will be offloaded to software that automates complex processes and monitors anomalies in real time. Machine learning programs that use Natural Language Processing (NLP), Bayesian analysis, and other supervised and unsupervised methodologies to classify transactions and to define or refine data models used for forecasting will be the norm—sooner than many might realize.

Technology already exists that would allow learning machines to make a first pass of financial analysis to identify and highlight patterns, check for errors, and identify anomalies. Automating this manually intensive process would allow finance professionals to spend less time on data entry and review, freeing them to take a deeper dive into business analytics and make use of real-time information to drive business decisions.

Successful application of business analytics allows companies to bridge the gap between finance and operations to give leadership the tools they need to accurately measure their performance in the moment rather than scouring over what happened in the past.

In this environment, the CFO then becomes the messenger of findings up and down the chain of command: from the board of directors to front-line employees. Everyone in the company has to understand how the metrics impact them and their role in achievingthem. The CFO must be able to communicate this information to each individual.


Data is not information, information is not knowledge, knowledge is notunderstanding, understanding is not wisdom.

Clifford Stoll

Building a proper data science team means assembling a group that includes domain experts (team members who understand company goals, industry fundamentals, and how to access and leverage existing data), analytics specialists, coders (R, Python, Java), database administrators for both SQL and NoSQL databases for unstructured data, machine learning specialists who understand how to use ML frameworks, algorithms, and models, and big data experts who are proficient with tools like Hadoop and Spark.

Many of these specialties may already exist within the company, in which case the team could be set up as a functional team or a decentralized group. For smaller companies or startups, the data science team could consist of only two or three key individuals who perform multiple roles.

Regardless of business size and resource availability, all CFOs must find ways to leverage available resources to increase focus on business and customer data. Any business of a size and scale that warrants a dedicated CFO has the capacity to leverage automation and data analytics to improve business intelligence and performance.

Through thoughtful capital investment in automation technologies alone, CFOs can repurpose existing resources to focus on analytics to gain important operational and financial insights that can improve the bottom line. While Return on Investment (ROI) may vary from business to business, few can afford to be caught flat footed in the wave of big data that will continue to shape business operations for years to come.


The first step to transitioning to a data-driven organization is to standardize, automate, and log all relevant data.


Automation is a move to a record-keeping platform that is connected, computerized, and continuous. Eliminate the most repetitive, time-consuming finance and accounting tasks. This includes your company’s accounts payable, accounts receivable, and journal entries.


  • Transactional accounting–automate repetitive entries
  • Batch processing–define bulk activities (invoicing, revenue recognition, etc.)
  • Recurring jobs–daily, weekly, monthly scheduled activities
  • Dashboards/reporting–user level access to information in real time


  • Time and resources–automation enables finance departments to do more with less
  • Elimination of errors–manual entry creates potential for errors (lack of visibility, broken Excel sheets, printed reports, emails, etc.)
  • Cost–staffing expenses and the expense of potential mistakes
  • Eliminate/reduce human labor and reduce errors– consistent accounting entries
  • Automation is faster/easier/more accurate
  • Faster accounting–bills, payables, and reconciliations are all examples of tasks that can be automated
  • Seamless data sharing–better financial modeling with more data, which can lead to better KPIs– tools to manage individuals, departments, and the company at large


Systems based in Excel and email with key figures with isolated and specific knowledge. Old school systems are not scalable. Monthly close is an exceptionally time- consuming process.

When your finance shop is automated:

  • You will be able to collect, store, and process accounting data in real time
  • You and your team will have access to critical information instantly
  • Employees will be adding value and not just keying data into software systems
  • Your department and your company will be more flexible and efficient

Further, once you’ve automated your processes, you can begin to harness the power of analytics on top of automation. When your team isn’t spending the day making manual accounting entries, they can focus on other more important value-added tasks like analytics and research. Number crunchers become data investigators who spend their time tracking customer behavior and trends and performing both descriptive and prescriptive statistics.


1. Document and standardize your processes–identify repetitive tasks and move their automation to the top of the list.

2. Locate and identify your available data:

  • Line of business spreadsheets, other systems, databases;
  • Data wrangling into new spreadsheets, databases, shared drives, exported into various Excel reports and slide presentations.

3. Create a map–this provides accountability and visibility for data.

4. Analyze your statistics–find correlations and relationships. Use these to show the value of your company’s data to encourage support from other stakeholders.

5. Get buy-in from the rest of the leadership team (show them the power of data).

6. Get your systems talking to each other. Different tools used in various sections of your business should be able to communicate and share data. Systems like your CRM, project management, accounting software should all be playing from the same sheet of music.

7. Automate manual processes and optimize time.

  • What do you want to automate?
  • Where does the data come from?

i. APIs

ii. Paper invoices

iii. Digital documents

  • Consider rules-based automation (RBA). RBA is optimized to process-specific tasks and formats; when transaction data differs from this criterion, it can break.
  • Use third-party tools (like Floqast and Emburse).
  • Evaluate potential ERPs for seamless integration of disparate systems for end-to-end data flow.

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