Building Data Science and Analytics Capabilities in Finance and Accounting
Stathis Gould | September 26, 2019
A strong finance and accounting background is no longer sufficient to become a value-add business partner over the long term.
That was the clear message from IFAC's Professional Accountants in Business Committee which is looking at the finance and accounting professional role in data science and analytics. At its March 2019 meeting, a workshop led by Jeff Thomson, President and CEO of the IMA, and Daniel Smith, Founder of advisory firm Theory Lane and member of the IMA Technology Solutions Group, considered how finance and accounting talent management needs to evolve quickly to emphasize competency in data science, analysis, and visualization.
Bhavesh Shah, Senior Vice President (and incoming IMA Board member), and Gurdeep Singh, Global Data and Analytics Lead, both from Johnson & Johnson’s Global Finance, based in its Singapore Financial Planning and Analysis (FP&A) Center of Excellence, provided insights into the knowledge, competency and skills required for accounting and finance professionals to develop their contribution to data analytics. They also outlined best practices in learning and development approaches.
Why are data and analytics so important to finance and accounting professionals?
As The Economist aptly put it in 2017, the world’s most valuable resource is no longer oil, but data. Technology advances have made it much easier to amass data in huge quantities. Capitalizing on this data helps to create value and growth, which is why organizations are investing in people and technological capabilities to extract greater value from data.
The digital and data revolution provides massive opportunity for finance functions and offers exciting career options for existing and prospective finance and accounting professionals. CFOs, finance functions and internal audit functions, as well as accountancy firms, are significantly increasing their capacity in data science, data analytics, and data governance.
IMA’s survey, The Data Analytics Implementation Journey in Business and Finance, highlights how organizations that have implemented leading-edge analytic techniques and technologies uniformly report improvement in their performance.
As more data and analytical roles are integrated into financial planning processes, new opportunities are emerging within finance and accounting functions, as well as new challenges.
What do finance and accounting professionals need to know?
For the accountancy profession, Jeff Thomson emphasized the critical aspects of data science that are needed for accountants to deliver insight and foresight.
The primary obstacle for businesses to leverage their data is a talent shortage. Businesses across industries require people with data analytics skills who are prepared to challenge norms. While management accountants probably don’t need to become expert data scientists, they do need greater data science and analytical skills in order to derive insights from data and to enable more effective decision making and control.
Future ready accountants will need enhanced competence and skills in:
- Statistics - to discover the patterns and insights provided by data;
- Data applications covering a range of areas, such as data governance, data architecture, data creation and storage, data transformation (extraction and transformation), data manipulation and modeling, and technology skills including machine learning and algorithms; and
- Business domain and applications combining an in-depth understanding of the business to identify problems and opportunities, and developing insights and intelligent solutions, including using tools for better visualization and workflow integration.
Business and leadership competence will continue to be paramount to enable effective communication (analytics translation), interpretation of results, and relevant recommendations for decision-making.
Daniel Smith highlighted the changing landscape of data science, which is a continually evolving discipline covering data and data tools, statistics, and machine learning in the context of business needs. Although demand for data science professionals is rapidly increasing today, the nature of data science will become more integrated into processes with the further evolution of artificial intelligence (AI) and cognitive business. Consequently, knowledge requirements will dramatically change as business moves from data science to AI in the coming years.
The areas in which data science and machine learning are forming the basis of new opportunities for finance and accounting professionals include enhanced roles in:
- Accountability and transparency in the areas of fraud, audit and attestation;
- Planning and analysis helping to improve forecasting, driver-based costing and profitability modelling, and supply chain;
- Data stewardship and governance to ensure reliability, comparability and consistency; and
- Decision making and ensuring relevance, understanding and cost/benefit.
In response to the rising importance of data and technology, the IMA has released an enhanced Management Accounting Competency Framework for Professionals in the Digital Age. Technology and Analytics is included as a distinct knowledge domain, and includes four competency areas covering information systems, data governance, data analytics and data visualization.
Learning and development approaches to support accounting and finance professionals
Johnson & Johnson Global Finance is investing in developing advanced analytics capability in its FP&A Center of Excellence. This investment aims to ensure the global finance function is more focused on planning and analysis, and that the operational and financial planning processes are integrated.
By building standardized processes and a single planning tool and data warehouse, they are enabling greater integration between business units. They have standardized reporting and automated repeatable tasks, providing greater descriptive capability through self-service dashboard reporting and diagnostics. In their current phase, they are investing in predictive analytics in P&L, cash-flow and balance sheet forecasting, prescriptive analytics (e.g., pricing) and cognitive services (e.g., chatbot).
The focus of FP&A is ultimately on bridging the gap between data science and business needs to enhance decision making. To support their evolution to advanced analytics, new roles are being developed. Although external data science expertise has been needed, the focus is on upskilling existing talent.
The mission for the FP&A Center of Excellence is to be a talent incubator for the finance function, equipping finance and accounting professionals with future-ready skills in advanced analytics and forecasting. This investment is largely self-funded through savings from automation.
Johnson & Johnson's training approach is to provide open source and customized in-house learning through training and online platforms. Training is developed and supported by external partners such as the National University of Singapore Business Analytics Centre and Singapore Management University. Training courses in 2019 cover techniques, methods and tools, including statistics, linear regression models, time series forecasting, exploratory data analysis, designing analytics solutions, data visualization and dashboarding, and workflow automation.
Key learning points from the Johnson & Johnson analytics journey include:
- The CFO needs to be a champion to ensure all key players, including the controller function, IT and business units, are aligned to deliver the mission. Business unit customers also need to be advocates.
- Operationalize analytics so that insights lead to direct impact on revenue and cost. Also integrate analytics within processes and systems.
- Adopt an agile and "fail fast" approach to learn quickly from failures and ensure end users are always in the loop and own the outcome.
- Prioritize analytics based on feasibility (data availability and ease of process integration) and impact by identifying the top use cases which can create greatest value quickly. Think about successes in terms of weeks and not months.
- Filter hype from reality. Data need not be "big" to be relevant and useful; technology is not usually a constraint if you focus on quality of data, process, people, and regulatory issues.
What does this all mean for the profession?
There is a sense of urgency around the change needed in accountancy education to incorporate data science and analytics competence and skills for both accountants in business and accountants working in firms.
The profession needs to further develop both the foundational areas of knowledge and competency in initial professional education and development, and the learning opportunities provided to mid-career accountants.
In terms of the profession’s future education agenda, key strategic questions to address include:
- How much of a focus on data science and analytics is appropriate for initial professional accountancy education and development?
- What are the key learning outcomes needed for professional accountants to remain relevant to the needs of their employers?
The finance and accounting team will likely be the source of competency and opportunity for an organization to leverage its data. It is therefore important to have strong relationships and mutual understanding between professional accountancy organizations with academia and other education and training providers to ensure:
- Alignment between what is taught in university education and what finance and accounting professionals do.
- Relevant continuing professional development for mid-career accountants, with training that is applicable to those working in both large and small organizations.
Professional accountancy organizations also need to do a better job at “telling the story” of the profession to millennials, Gen Z, and Gen Y, and articulating the opportunities provided by new career pathways.
For additional reading, see the PAIB Committee report, Future Ready Accountants in Business