The future of accountants, accountancy and data analytics

Posted by Philip Wong on

Digital disruption due to new and emerging 21st century technologies has resulted in growing volumes of data, transforming how we do business today, and accountants and finance professionals need to keep pace with these changes [1].

Historically, it was easier when accountants worked with structured legacy and ERP databases; to apply transactional, variance, ratio and trend analysis to determine financial performance and make business predictions [2].

The recent growth of digital data has added new types of semi-structured and unstructured data [2]; for example, Internet usage clicks IoT sensor data. Some of this data is so large they cannot be analysed by traditional tools such as spreadsheets, and need more sophisticated tools [3, 4]. 

What does this mean for accountants, and in turn, their clients?

Top analytical tools for businesses 2020

(Fig 1 Source: Smith 2020)

Figure 1. shows a list compiled by Smith [5] of companies that provide the latest analytic tools to capture, manage, analyse huge volumes of structured and unstructured data; to enable visualisations of complex data quickly and intuitively, and to create valuable business insights.

Opportunities

The increasing number of tools presents a unique opportunity for businesses to use it for strategic advantage [1].

Accountants and financial analysts play a significant role in this endeavour, by their training and close relationship with the businesses. However, to do this, they need to understand that their role has evolved from being "Bean counters" to "Bean Growers" [6].

Accountants should now learn to use new data types to extract value and business insights; to understand the demand from clients, not only for descriptive and historical information but also sophisticated predictive and prescriptive business data solutions [2].

Predictive and prescriptive analytics allow firms to use historical data to forecast the future and to optimise that data to prescribe for the best courses of action [4]. As an example, a company with an internet-connected system can use prescriptive analytics to make adjustments to pricing, and even run A/B tests in real-time, enabling it to turn the process of ticket pricing – as Disney did - into a science [7].

An accounting firm of the future needs people who understand how to capitalise on the opportunities that data, automation, AI and ML deliver to their clients and themselves [8].

These capabilities will lead to high-value, highly efficient professional accounting teams and expanded business advisory revenue streams [2, 8].

Limitations

Accountants with technical data analytics skills are difficult to find [9]; especially in areas of identifying key data trends (29%); data mining and extraction (28%); operational analysis (28%); technological awareness (27%) and statistical modelling and data analysis (27%) [10].

Accountants' skills also need to be expanded to understand limitations of analytics, of disparate data sets - especially with incomplete or missing values, or partial data could limit its usability [11]. Missing data can skew both the data and the model because if there is not a complete picture of the data, insights will be flawed [11]

Data collected from different sources could also be variable in quality with different formats, duplicate records or inconsistencies across data fields [11]. These will need extra effort to identify, pre-process and clean before meaningful results can be obtained [12].

The size of data storage is another concern, the root cause of which is the wide variety of the computational tasks required for accounting and financial analysis.

Complex analytical models may require an amount of computation that increases exponentially with the size of the dataset; and if not addressed, can lead to processing times that are both impractical or unfeasible [3].

Data visualisation tools, particularly, may face hardware or device limitations; such as with in-memory technology, poor scalability, functionality, and response times [12]. For example, it is impossible to plot a billion data points in a graph on a small mobile device. Data needs to be displayed in other ways, such as data clustering, tree-maps, sunbursts, parallel coordinates, circular network diagrams or cone trees [12].

All-important is the accuracy of the data for its intended use. In a Forbes survey, data scientists stressed that data preparation accounted for about 80% of the work of they performed; such as collecting data sets (19%) and cleaning data (60%) [13]. Cleaning of the data is critical because data analysis is only as good as its quality [12]; and the principles of 'garbage-in-garbage-out' (GIGO) warns that if data is garbage, so will be the insights [14].

Challenges

When establishing a business analytics function within an accounting firm, it must ensure that there is a multi-disciplinary team of data scientists as well as business analysts [15].

Data scientists, with mathematical and programming skills, will deal with building data applications and statistical models, while the analysts will focus on understanding and delivering the business requirements. Second, the team will also need tools to collect, explore, process, visualise and model data. Finally, a methodology for each client project is necessary, since what constitutes an appropriate analytics methodology for one client organisation will not necessarily be the same as that of another [15].

A methodology is critical for a project fraught with uncertainty [16]. Using an Agile project management methodology will being able to perform successive iterations to include new or changing requirements in a more controlled way [16]. A clear objective for the project will allow the team to define the scope of a project; to specify data collection from correct sources; to process, prepare and explore the data (clean); to create training and dataset tests (agile method); to build and improve the model, and to deploy the model [15, 16].

A 'proof of concept' test, with agile iterations, will help to uncover unknown challenges inherent with data volumes, data extraction, cleaning and integration; and will provide a clearer understanding of possible outcomes [16].

Ethics

Another issue with data analytics is ethical, especially when businesses monetise information for purposes other than those for which the data was initially collected [17]; behind the users' back [5].

While data offers numerous benefits, it can also be a "weapon of dehumanisation" – not by data itself, but by the way people might use it [18]. Information from analytics can also be misused even when there is no intent to harm [2];
Existing ethical and legal frameworks cannot prescribe what we should and should not do with it [17].

While legislation, for example, regarding data privacy, is reasonably straightforward - either a company is doing what is legally right or wrong; ethics, on the other hand, is more nuanced and complex.  Each business needs to decide its ethical framework, about how data should or should not be used [19].

An ethical framework to address the benefits and risks to the individual, such as the one proposed by the IAF, is something that will need to be considered [20]. While this framework is likely to vary between countries, the IAF framework emphasises that – for data and data analytics - "the core interest in fairness remains critical to any discussion of basic rights" [20].

Conclusion

Accountants and business analysts in the era of digital and big data must start to understand the opportunities, limitations, challenges and the ethical use of data and analytics, to move from 'bean-counting' to 'bean-growing' [6].

This will enable them to help their clients grow the businesses with the use of the hidden insights in their data. In the 21st century, data, and more ideas like it ned to be discussed to create more digitally-capable and profitable businesses; and small-medium firms cannot and should not be left behind.

References

1. Tokc-Wilde, I. The big data effect. 2016 [cited 2020 May 14]; Available from: https://www.accaglobal.com/an/en/student/sa/features/big-data.html.
2. Lindell, J., Analytics and big data for accountants. 2018: John Wiley & Sons Incorporated.
3. Vasarhelyi, M.A., A. Kogan, and B.M. Tuttle, Big Data in Accounting: An Overview. Accounting Horizons, 2015. 29(2): p. 381-396.
4. Hannan, A. Big Data Meets Accounting. 2016 [cited 2020 May 14]; Available from: https://www.becker.com/blog/accounting/big-data-meets-accounting.
5. Smith, S. Top 20 Big Data Analytics Companies For Small Business. 2020 [cited 2020 May]; Available from: https://cloudsmallbusinessservice.com/small-business/best-big-data-analytics-companies-for-small-business.html.
6. Pickard, M.D. and G. Cokins, From Bean Counters to Bean Growers: Accountants as Data Analysts—A Customer Profitability Example. Journal of Information Systems, 2015. 29: p. 151-164.
7. Berenson, M., et al., Basic Business Statistics: Concepts and Applications (14th ed). 2020, Harlow, UK: Pearson.
8. Wolters Kluwer. The Intelligent Practice: Building an accounting firm for the future. 2020 [cited 2020 May 12]; Available from: https://www.cchifirm.com.au/the-intelligent-practice/.
9. Liu-Lindberg, A. Financial Analysts Should Embrace Big Data. 2017 [cited 2020 May]; Available from: https://www.linkedin.com/pulse/financial-analysts-should-embrace-big-data-anders-liu-lindberg/?published=t.
10. Bramwell, J. Accountants with Data Analytics Skills Are Difficult to Find. 2016 [cited 2020 May]; Available from: https://www.accountingweb.com/practice/team/accountants-with-data-analytics-skills-are-difficult-to-find.
11. Bari, A., M. Chaouchi, and T. Jung, The Limitations of the Data in Predictive Analytics, in Predictive Analytics for Dummies (2nd edition). 2016, Dummies - John Wiley & Sons: E-Book.
12. Firican, G. The 10 Vs of Big Data. 2017 [cited 2020 May 13]; Available from: https://tdwi.org/articles/2017/02/08/10-vs-of-big-data.aspx.
13. Press, G. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. 2016 [cited 2020 May]; Available from: https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#3953bf6c6f63.
14. Kumar, V. Untold Story of Big Data's Garbage In Garbage Out. 2013 [cited 2020 May]; Available from: https://analyticsweek.com/content/untold-story-of-bigdatas-garbage-in-garbage-out/.
15. Vidgen, R., S. Kirschner, and F. Tan, Business Analytics: A Management Approach. 2019: Macmillan Education UK. Kindle Edition.
16. Lewis, M. Project Management Methodologies for Big Data Analytics. 2017 [cited 2020 May]; Available from: http://www.scienteconsulting.com/blogs/803/project-management-methodologies-big-data-analytics/.
17. Uria-Recio, P. 5 Principles for Big Data Ethics. 2018 [cited 2020 May 12]; Available from: https://towardsdatascience.com/5-principles-for-big-data-ethics-b5df1d105cd3.
18. Mayer-Schönberger, V. and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think. 2013, NewYork: Houghton Mifflin Harcourt.
19. Walle, T. How To Move From Data Privacy To Data Ethics. 2020 [cited 2020 May]; Available from: https://www.forbes.com/sites/forbestechcouncil/2020/03/11/how-to-move-from-data-privacy-to-data-ethics/#34c72d788ff6.
20. IAF. Unified Ethical Frame for Big Data Analysis: IAF Big Data Ethics Initiative, Part A (Draft) [Download pdf]. 2015 [cited 2020 May 13]; Available from: https://b8e.99c.myftpupload.com/wp-content/uploads/IAF-Unified-Ethical-Frame.pdf.

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