Updated: Aug 10
[PREVIOUS SECTION: Myth #2 Accountants will be replaced by robots]
Speaker introduction slide
[Muted. Introduced by emcees.]
03:17:40-3:18:16 | Slide 0 - Myth #3 Our job is boring (just sitting at desks & crunching numbers)
Hopefully [Sir Bonnie’s] discussion already gave the merits that accountancy is not a boring industry to get into. But if you need more convincing, consider these: Financial reporting standards and tax laws are constantly-changing and so accountants are always kept on their feet! [Next slide, please.]
03:18:17-3:21:20 | Slide 1 - Debunking it right away!
The profession can also get glamourous depending on how much you’d like to be involved. You can be asked to help in a high-profile fraud investigation; be a financial planner for a multimillionaire celebrity; play a large part in company’s cybersecurity measures; travel the world to help multinational giants comply with international tax laws and dividend strategies; do a pivotal role in significant transactions such as mergers and acquisitions, restructurings and hedging activities. Climate change and sustainability issues now require accountants to quantify climate risk and assist in formulating a strategy on ‘green‘ projects.
The profession has gone a long way, and some suggest that a huge change is coming! Well, that huge change has already come albeit not yet into its full implementation! We are on the Transformative Age. The World Economic Forum calls it ‘Industry 4.0’ where business is anything but usual and navigating it demands that we ask better questions: what NOW, what’s NEXT and what’s BEYOND?
I resonate with Bill Gates when he said, ‘we always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10 - don’t let yourself be lulled into inaction’. How soon blockchain is going to disrupt the traditional bookkeeping is still anybody’s guess but that doesn’t give us the leeway to relax!
We can almost compare the Transformative Age to what now seems to be the jurassic Industrial Revolution where everybody talks about steel and oil, and Rockefeller and Carnegie. The expectation is there would be a fundamental shift in everything that we know – only now, the speed at which these changes are taking place is complemented by our increasing reliance on connectivity – whether to data, interfaces, people or experiences.
The COVID-19 pandemic clearly has only accelerated the buzz word ‘digitalization’ where virtual reality is the new normal, and ‘cloud’ is the limit.
[Sir Bonnie] also talked about bots and artificial intelligence. But then again, how will you be able to properly command bots – and to the extent appropriate, help in creating more bots to achieve the optimal level of productivity? Answer lies in future-proofing! We, accountants, are important pieces of the Future of Work equation. But there obviously needs to be some upskilling – and relentless upskilling! Which is why I was one of the few who didn’t join the resistance when the CPE credits for CPAs where increased to 120 units. I thought it was necessary given the current climate and the traction these technological developments were getting. That’s entirely a separate topic but I hope you do get the point! [Next slide, please.]
03:21:20-3:23:02 | Slide 2 - Data is the new oil
During registration, we asked you to pick among the choices on what currently interests you and what your upskilling plans are after graduating accountancy. The JPIA officers then put together the visuals based on the data they’ve gathered. They then used the data to recalibrate the themes of this webinar such that it tailor-fits you. And they actually plan on using these data to follow and track you, observe your activities and profit from you in the future! Of course that’s a joke – or is it?
As you can see, the basic visualization and statistical summaries turned a sea of bits to a nugget of knowledge. Data is knowledge, and we all know that knowledge is power.
You must have been noticing the theme of your YouTube ads, Google news and Amazon’s (or Zalora’s) top picks for you. You must have received one or two unsolicited text messages, calls and/or emails from certain providers. Your Facebook and LinkedIn also seems way too intelligent to suggest people you may know of. These are all manifestations of how powerful the use of data is! Today, every click translates data about us going into a data bank. These data, when analysed and applicable tools are incorporated, can be used to gain an advantage.
We, including our predecessors, have all been using data to make informed decisions but not in a scale they are being used today.
With Bitcoin being touted by a few as the digital gold, data on the other hand is widely accepted as the ‘new oil’. [Next slide, please.]
03:23:02-3:28:32 | Slide 3 - Data science
More than ever before, organizations are making data-driven decisions. Without data science, the information stays stuck – no story to tell, no insights to share, no solutions to try.
Dubbed as the sexiest job of the 21st century by Harvard, data scientists are high-demand, low-supply talents that will soon replace the monies generated by the Wall Street guys.
Amanda Wilkie, consultant at Boomer Consulting and top technology expert, encourages accounting students to throw in a computer science minor or a math minor — something that's going to give them that logic and help them think in code. Or, for the already-CPAs, to look into online courses or something that is going to give them the computer science background to add to their accounting knowledge. She thinks if you marry those two abilities, then it's a home run in the profession. And, I totally agree!
Top accounting firms are bringing in data scientists and then getting them taught in accounting. Effectively, what this is saying is that, in some cases, they need the technical skills first, and the accounting skills second.
So what does a data scientist actually do? We can start by discussing the three pillars of this discipline:
· Data analysis, it’s to do with taking raw information and turning it into knowledge that can be acted on or that can drive a decision;
· Data modeling (a.k.a. machine learning), using the data that we have, to estimate the data that we wish we had; and
· Data engineering (akin to coding / developers), where taking the analyses and modeling activities and making everything work faster, more robust, and on larger quantities of data.
These three are all distinct and require different skills and different set of tools. But within these three is data mechanics, the dirty work that everyone needs to do but nobody likes to talk about which essentially is data formatting and cleansing. It can get gritty and in somedays, it will occupy majority of your time – but you never outgrow the need to do it.
The lower right portion of the slide illustrates the archetypes of data scientists. A beginner (not in the diagram) might have just a little bit of experience in each of these pillars. A generalist (the jack-of-all-trades) develops a solid mixture of proficiency in all the pillars but only to a broad view of problems with varying scopes. It means that a generalist may have to collaborate with a specialist for some projects. The specialists are further split into three: 1) Detective, the master of data analysis; 2) Oracle, the master of modeling and tools of machine learning; and the 3) Maker, the master of data engineering and the one who transforms good ideas into concrete machinery.
I believe the closest field for us is on the data analysis and hence, being the ‘supreme detective’ that we can be.
Interesting how the demand for data scientist in general soared in the last couple of years. The compensation is astonishing! Universities now offer data science as a learning course. Google is currently offering a 3 to 6-month data analytics course for free through Coursera!
University of North Carolina’s Kenan-Flagler Business School cited examples of how data analytics are currently being employed in the profession:
i. Auditors, both those working internally and externally, can shift from a sample-based model to employ continuous monitoring where much larger data sets are analyzed and verified. The result: less margin of error resulting in more precise recommendations.
ii. Tax accountants use data science to quickly analyze complex taxation questions related to investment scenarios. In turn, investment decisions can be expedited, which allows companies to respond faster to opportunities to beat their competition to the punch.
iii. Accountants who assist, or act as, investment advisors use big data to find behavioral patterns in consumers and the market. These patterns can help businesses build analytic models that, in turn, help them identify investment opportunities and generate higher profit margins.
It further shared why accountants make excellent data scientists. It said:
ü Accountants have outstanding technical skills. [Accountants are used to aggregating information to create a picture of an organization that summarizes the details contained in each transaction. Working with descriptive analytics, predictive analytics, and prescriptive analytics comes more easily to people who already possess excellent quantitative skills.]
ü Accountants are natural-born problem solvers. [The jump from descriptive and diagnostic analytics to predictive and prescriptive analytics requires that one shift from an organizational mindset to an inquisitive mindset; a shift from stacking and sorting information to figuring out how to use that information to make key business decisions. Accountants are experts at making this jump.]
ü Accountants see the larger context and business implications. [The true value of data analysis comes not at the point when the data is compiled, but rather when decisions are made using insights derived from the data. To uncover these insights, a data scientist must first understand the business context. Not only do accountants understand this context, they live it.]
So, indeed, we can thrive in this Transformative Age!
Wise men know that ‘the what’ will always change, and so the advice is to rather focus on ‘how to think and learn’. EY Global Learning Partner, Riaz Shah, the brains behind the EY Badges and Tech MBA reminds us that ‘in this age of machine learnings, we need to be learning machines!’
So again, if you think our job is boring – and your learnings are limited - as it only involves sitting in front of a desk to crunch numbers, you better think again! Some of us might be introverted but our professional lives are never dull! [Muriel could definitely shed more light on this. Over to you, ma’am!]
[NEXT SECTION: Myth #4 (All) Accountants are introverted and dull.]
The archived video is available through here: https://www.akawnthink.com/post/archived-acw20-negros-oriental-chapter-what-lies-ahead-webinar-july-18-2020