One constant in the accounting profession is change. New companies and new technologies present new challenges for the profession. One could argue the biggest challenge for the accounting profession of tomorrow is dealing with companies and technologies that do not exist today.
We live in an interconnected world in which new technology increasingly disrupts status quo at breakneck speed. The disruptive power of companies like Amazon, Google, Uber and AirBnB have not only changed the business environment, but these innovative technologies have also changed our consumer habits and personal expectations.
"One could argue the biggest challenge for the accounting profession of tomorrow is dealing with companies and technologies that do not exist today."
Subsequently, the accounting profession needs to embrace the technological changes from disruptive technology and provide the analytical expertise to continue to serve and protect the public interest. It is no longer debit and credits, but rather debits, credits and data! Data analytics, the Internet of things, robotics process automation (RPA) and artificial intelligence are the future of business. The profession needs to adapt to these emerging technologies.
A joint “CPA Evolution” project on the future of the CPA Exam by NASBA and the AICPA will incorporate skills including business intelligence, data management, predictive analytics, cybersecurity, risk management and information security governance. Building skillsets that are adaptable to the changing technology is the future of the profession.
BIG DATA AND DATA ANALYTICS
Gartner Research suggests data is the new oil, and analytics is the combustion engine. Yet most organizations find big data initiatives an enigma as the United States is estimated to operate at only 18 percent of our digital capability.1
Considering 90 percent of the data in the world today has been created in the last two years, finding the promised results from big data initiatives is an elusive pursuit for many organizations. The challenge is to determine what data is relevant, now that we have massive amounts of unstructured (e.g. text, voice, image) data that can be paired to our traditional structured data to provide new insights that can create competitive advantages.
Big data analytics may mean different things to different people in different context. Big data analytics is the process of evaluating massive data sets with the purpose of drawing conclusions to address business questions. Therefore, data analytics creates big data models in order to discover and communicate useful information and patterns for decision making. Big data refers to information with such extremely high volume, variety and velocity that organizations invest heavily in system architecture to handle it. The promise of big data is transforming the way we do business and is impacting most other parts of our lives.
Analytics can take many different forms including descriptive, diagnostic, predictive and prescriptive analysis.
"The promise of big data is transforming the way we do business and is impacting most other parts of our lives."
analytics deals with data integrity and discovering trends, key performance indicators and dashboards to help understand “what” has happened. Descriptive statistics can be used to answer questions like: How much profit did we make? What is our profit trend? What does our A/R aging look like?
analytics seek to explain “why” it happened. What does our variance analysis look like? Why did general and administrative expense increase?
analytics is useful to show “what is likely to happen,” such as what are future earnings or how likely is a company to go bankrupt? Can we predict when the financial statements might be misstated?
analytics tackles “what we should do” about it. How can we maximize revenues in the wake of a trade war with China? Should we make our products or outsource?
Data analytics takes different forms, yet the big abstract question for data analytics is: How can we get more efficiency out of the operation?
A 2019 survey by the Institute of Management Accountants suggests enhancing analytics capabilities is the key to the future. But the big question is how? Analytics can be used to discover patterns or trends in data and ask “what-if” questions to help drive the decision making process in business. It has been argued the biggest obstacle is not the data, but the lack of understanding of how to use analytics to improve business and the lack of management support due to competing priorities.2
We need more critical thinking skills to help determine what data are relevant to inform our analysis.
Artificial Intelligence (AI) is the science of engineering and making intelligent machines that try to simulate and reproduce human behaviors such as thinking, speaking, feeling and reasoning. There are many different types of artificial intelligence, including machine learning, natural language processing, expert systems, voice and image recognition. AI can help automate and inform time-consuming and redundant tasks that should augment human decision making rather than be used to replace it entirely.
AI uses algorithms as a mechanism for creating a set of rules for the machine to follow, which enables the machine to process vast amounts of data no human can reasonably process or even comprehend. Machine learning is one form of AI where the computer will learn from the experiences discovered through the application of statistical models and algorithms in order to predict an outcome. Machine learning includes a process model that will train with data and then the trained model will be tested and continually retrained. After training on over eight million different web pages, one AI program became so good at writing, its creators wouldn’t release the technology because it could write better than most humans!4
"Big data analytics and AI may do the number crunching and analysis to assist accountants and allow for CPAs to focus on higher level tasks — which is great because arguably the breadth of scope of what we do as accountants continues to widen."
Deep learning is a type of machine learning driven by data representations as opposed to algorithms. Alexa or Siri are examples of our commonly used deep learning virtual assistants. Other types of deep learning include translations, driverless cars, chat bots and service bots, and facial recognition.
Domino’s Pizza will be piloting delivery with autonomous cars in Houston later this year. Amazon is expanding its infrastructure so the company is poised to take its drone delivery service to scale. Automotive companies are using facial recognition to control access of cars. Banks are using facial recognition for authentication. Beauty companies are using facial recognition to let customers try makeup virtually. Apple’s facial recognition can unlock the iPhone and authenticate purchases and payments at a quick glance.
Facial, voice and image recognition include measurable physical and behavioral characteristics for verifying a person’s identity, which is referred to as biometrics. Biometrics is becoming increasingly important in cybersecurity, and the regulatory and legal environment is taking notice.
The General Data Protection Act (GDRP) in Europe, new state laws and/or pending legislation take aim at biometrics, placing increased restriction for use such as replacing “opt-out” with “opt-in” permission models and empowering users to validate their personal information is correct. The changing regulatory and legal landscape are placing a premium on IT governance and IT policies accountants need to understand as we assess risk.
ROBOTIC PROCESS AUTOMATION (RPAS) AND BOTS
Nowadays, we check in for flights with our smart phones or via self-service kiosks at the airport using robotic processing automation. Gone are the days where we would check in with a human being.
Self-pay kiosks are commonplace at Walmart, and soon all 14,000 McDonald’s will have self-ordering kiosks. Currently, there are 45 robot baristas at coffee shops in South Korea. Automation will undoubtedly hit the service and manufacturing sectors.
Beyond that, any task that is redundant and has a well-defined process can potentially be a candidate for RPA. For example, if accountants are re-keying data, there is a broken process which can likely be automated. Routine tasks in accounting could include accounts payable, travel expenses, fixed assets and payroll, which all could be automated. This would empower accountants to focus on transactions that may require more analysis. RPA extends beyond numeric values, as data from PDF files or text can be incorporated into the automation.
WHAT LIES AHEAD
Big data analytics and AI may do the number crunching and analysis to assist accountants and allow for CPAs to focus on higher level tasks—which is great because arguably the breadth of scope of what we do as accountants continues to widen.
CPAs of the future will need to focus on strategic value creation for their organizations and for their clients. Take the Internet of things (IOT) for example. The Internet is evolving from connecting people to connecting “things,” so these various “smart things” (e.g. wearable devices, appliances, HVAC systems, automobiles, ERP systems) gain the capacity to learn from each other. Accountants should care because as these “things” learn from each other, there are efficiencies gained that could reduce redundancy and minimize costs.
In order to take advantages of efficiencies gained from the IOT, accountants must understand what kind of data is useful and how to collect it. Accountants must also understand there are increased risk factors as a result of this interconnectedness and big data explosion. IT security and audit must be in harmony to mitigate the risks presented from the sophistication of new cybersecurity threats.
As a framework for the future, a white paper from PwC suggests core competencies for the future should include basic programming skills in addition to working with both structured databases and unstructured data.5
A study from Forbes Insight
in collaboration with KPMG suggests new technologies and the big data explosion have redefined the skills more highly valued in the audit of the future to include: technology skills, communication skills and critical thinking skills.6
Critical thinking skills as introduced from real-world concepts to demonstrate how analytics add value to business and how the data flows throughout the organization are skills required for the next generation of accountants. This includes the technical ability to understand what is wrong, how it would be tested or validated and how it would be communicated in a meaningful way.5
It’s no longer a question of whether big data analytics will reshape the accounting profession, but rather a question of how quickly.
1. Manyika, J., G. Pinkus, and S. Ramaswamy, The Most Digital Companies Are Leaving All the Rest Behind.
Harvard Business Review, 2016.
2. Norris, D.M. and L.L. Baer, Building organizational capacity for analytics.
Educause Learning Initiative, 2013: p. 7–56.
3. LaValle, S., et al., Analytics: The new path to value.
MIT Sloan Management Review, 2010. 52(1): p. 1–25.
4. Metz, R., This AI is so good at writing that its creators won’t let you use it
, in CNN Business. 2019.
5. PwC, Data driven: What students need to succeed in a rapidly changing business world.
2015, PwC firm publication New York, NY.
6. Forbes, Audit 2025: The future is now.
, in Forbes Insights.
2017, Forbes Insights: Jersey City, NJ.