Victor Tan, Co-Founder, Infinity Cube, and an Institute member
In recent years, data analytics as a discipline has taken on a sophistication that suggests a complexity and a need for advanced skill sets that are largely inaccessible to ordinary people. Terminology and jargon such as big data, data mining, data cleansing, data visualization, algorithms, etc., add to the inaccessibility. Taken together, data analytics appear esoteric and limited to the initiated few.
Stripping away all the jargon and the hype, data analytics is a basic and fundamental skill that everyone is capable of doing and exercising on a daily basis. It is a uniquely human ability to observe things around us and analyse them to come to a decision. For instance, window shoppers check the range of available products, prices, locations, etc. (data sets), compare and analyse the data sets, taking into account perhaps additional data sets such as brands, distance of the shops, transportation convenience and costs, etc. (data analysis), to arrive at a decision whether or not to buy and, if to buy, where to buy.
The difference between data analytics at the daily level and the level at which it feels like rocket science is the wide and varied data sets that were not available previously. With cheap computing power, the level of data analytics possible has grown from the routine to the amazing. With the advances in computing technology, it has become easy to organize, structure and contextualize (data cleansing) these data sets, leading to raw data being turned into INFORMATION.
Every accountant has a data analytics toolkit – one example of a tool in that toolkit is the “analytical review” procedure, i.e. the process of comparing changes in account balances from one period to another, or comparing related accounts. By training, accountants have developed a keen ability to derive insights simply by analysing financial accounts. To aid such analysis, accounts are presented in a columnar format showing one period’s results to the next. If an accountant has more than two periods of data, better insights can be derived. Even better insights can be derived if the accountant has data from other sources, e.g. from a competitor company (ideally after data cleansing to make comparison meaningful), and so on. As the data sets become wider and more varied, the insights from the analysis can become better, not just as a comparison between periods or related accounts, but also in terms of new patterns of insight that were not previously observable. The more tools in the accountants’ data analytics toolkit, the better equipped the accountant is to observe patterns in data that will prove valuable – and valuable here means the ability to support good decision making in business.
Professional accountants in business (PAIBs) should be, at the very least, highly proficient in data analytics. PAIBs who are experts in data analytics will be worth their weight in gold to their employers and clients.
“Professional accountants in business should be, at the very least, highly proficient in data analytics.”
Terry Smagh, Senior Vice President, Asia Pacific, Blackline
Finance and accounting teams are still dependent on spreadsheets. A survey conducted last year by KPMG and ACCA in Hong Kong finds that financial professionals spend about two-thirds of their time on descriptive and diagnostic analytics, and only a third predicting future trends.
Many of today’s accountants are therefore still knee-deep in performing transactional duties, such as entering journals and tying out reconciliations. This manual crunching of information not only makes hunting for financial variances and anomalies challenging, but also potentially creates risk in spreadsheet-centric activity.
Automating tasks and data analytics need to go hand in hand. With automation handling repetitive tasks, accountants’ roles have evolved to focus on conducting strategic research and analysis.
Increasingly, businesses expect to obtain up-to-date reports to help inform critical decisions. This can only be done when they have greater visibility, control and transparency of their financial data. Additionally, investors today demand a more granular view of their portfolio companies’ financial data and how they are performing in near real-time, in order to take necessary steps, especially in light of the current uncertainties and new challenges.
Audit analytics – Audit data analytics enables auditors to focus on what matters with immediate visuals on key indicators, period-on-period variances, benchmarks against typical thresholds, comparisons and variances around business units, accounts, products, and processes. Less time is wasted on confirming the obvious and more time on assessing relevant transaction risks in real time.
Benchmark analytics – Typically, companies compare finance and operating metrics, such as profit margin and return on assets, against same-industry and same-sized peers. Accounting benchmarking on the other hand, compares internal data with curated external data for comparative statements and provides insight into where the biggest margins are for improvement or rectification. Where necessary, businesses can adjust and manage performance targets – be it to accelerate change or improve processes.
Financial analytics – As accounting teams increasingly see more business exposure, they are expected to play a greater role in supporting decision-making and take on an active role in financial analytics – interpreting the key performance indicators like revenue, expenses, and cash flow, which are critical for business decisions. Financial analytics will enable them to have insight into the company’s risk exposure with specific customers, whether capital and headcount investments are aligned with the right opportunities, or if the company has a good handle on revenue from new initiatives.
“Automating tasks and data analytics need to go hand in hand.”
Kane Wu, Co-Founder and Chief Executive Officer, ThinkCol Transform Limited, and an Institute member
In this day and age, it’s essential to equip oneself with more than one skill set. With skills in data analytics, accountants can better conduct their work with higher efficiency, predict and anticipate needs, and explore potential opportunities.
Traditionally, accounting is deemed as a provider of historical information. The analysis of previous year data is undertaken to perform the audit or prepare the tax return. Given the amount of raw, historical data available to them, accountants have the necessary information to understand the uniqueness of a business and anticipate its key needs. Through identifying a pattern using historical data, accountants can use past indicators to develop foresight and advise businesses on the best course of action. Their hypothesis can be supported by the data and, therefore, better visualized.
Throughout the years, there have been rapid advances in artificial intelligence (AI) and continuous advancements in computational power. No organization wants to be left behind. To reap the maximum benefits of converging the disciplines of accounting and data analytics, it’s essential to not only have both data scientists and accountants in an organization, but also accountants who have a basic understanding of data analytics.
Many people who have never used data analytics techniques before fear data science. But in fact, most accountants already incorporate basic data analytics skills into their daily work, for example with the use of advanced Excel formulas. While others also utilize Excel macros as well.
Incorporating data analytics tools is not as challenging as most people think. There is software available in the market that incorporates drag-and-drop tools to make it simple for professionals to quickly learn and use. For instance, instead of using Excel to manually plot numbers, data visualization tools such as Tableau can automate the task. Other data analytics software, such as KNIME, can transform maths and numbers. These tools help to visualize, summarize and reformat data so that counterintuitive insights are easily detected.
Data analytics is essential to any industry as well as different levels of organization. My data science consultancy, ThinkCol, has witnessed the growth of many companies through promoting data analytics in the form of basic data science training for staff and the implementation of AI systems. Discovering the diamond in the rough may not be as difficult as it seems. It’s time for the accounting profession to further take charge of their data and encourage the appropriate use of data analytics tools.
“Data analytics is essential to any industry as well as different levels of organization.”
