The document discusses the future of accounting and the rise of "big data accounting" and the "predictive accountant" by 2020-2022. It notes that algorithms will positively alter the behavior of over 1 billion people and blockchain business will be worth $10 billion. It highlights that analytics accounting professionals using tools like predictive analytics, data mining, and data science will become more common. Overall, the accounting profession will need to adapt to incorporating big data and new data-driven technologies to provide insights to clients.

1. The Future of Accounting:
Towards 2020  The Predictive Accountant
https://www.slideshare.net/ssood/future2020
Suresh Sood, PhD
suresh.sood@charteredaccountantsanz.com
@soody

2. Vignettes in the two-step arrival of the internet of
things and its reshaping of marketing management’s
service-dominant logic
Woodside & Sood
Journal of Marketing Management Volume 33,
2017 - Issue 1-2: The Internet of Things (IoT) and
Marketing: The State of Play, Future Trends and
the Implications for Marketing

4. Acuity 2017
Big data accounting-the predictive accountant
Tools and techniques of the predictive practice

5. Source: Tips and tools of the predictive practice, Sood (2017) ,
June/July Acuity Magazine & 19 June Accounting Daily
The Predictive Practice

6. Areas for Conversation
• What will the future look like?
• What is driving major change in our accounting?
• Why do we need to bother with big data ?
• What is big data?
• How do we ingest truly massive data sets?
• What are the use cases for practices ?

7. © Chartered Accountants Australia and New Zealand 2016
By 2020-22 :
 100 million consumers shop in augmented reality
 30% of web browsing sessions without a screen
 Algorithms positively alter behavior of over 1B
 Blockchain-based business worth $10B
 IoT will save consumers/businesses $1T a year
 40% of employees cut healthcare costs via fitness tracker
Strategic Predictions for 2017 and Beyond, research note
14 October, http://www.gartner.com/document/3471568
2017 Hype Cycle for 3D Printing ,
July, http://www.gartner.com/document/3388326
Gartner (2016/17)

8. 8© 2017 FORRESTER. REPRODUCTION PROHIBITED.
Forrester Research, 2016

9. “As business is transformed by
the impact of big data and big
data analytics, so the role of
finance professionals will
change as well”
Ng Boon Yew Executive Chairman of Accountancy Futures
Academy of the Association of Chartered Certified
Accountants Executive chairman
More Diverse Associates

10. The ANZ Heavy Traffic Index comprises
flows of vehicles weighing more than 3.5
tonnes (primarily trucks) on 11 selected
roads around NZ. It is contemporaneous
with GDP growth.
The ANZ Light Traffic Index is made up of
light or total traffic flows (primarily cars and
vans) on 10 selected roads around the
country. It gives a six month lead on GDP
growth in normal circumstances (but cannot
predict sudden adverse events such as the
Global Financial Crisis).
http://www.a http://www.anz.co.nz/about-us/economic-markets-research/truckometer/
ANZ TRUCKOMETER

11. Statistics, Data Mining or Data Science ?
• Statistics
–precise deterministic causal analysis over precisely collected data
• Data Mining
–deterministic causal analysis over re-purposed data carefully sampled
• Data Science
–trending/correlation analysis over existing data using bulk of population i.e. big data
–Extraction of actionable knowledge directly from data through a process of discovery,
hypothesis, and hypothesis testing.
Adapted from: NIST Big Data taxonomy draft report :
(see http://bigdatawg.nist.gov /show_InputDoc.php)

12. Data Science Innovation
Data science innovation is something
an organization has not done before
or even something nobody anywhere
has done before. A data science
innovation focuses on discovering
and using new or untraditional data
sources to solve new problems.
Adapted from:
Franks, B. (2012) Taming the Big Data
Tidal Wave, p. 255, John Wiley & Son
Data Science Algorithms
Companies are reimagining Business
Processes with Algorithms and there
is “evidence of significant, even
exponential, business gains in
customer’s customer engagement,
cost & revenue performance”
Wilson, H., Alter A. and Shukla, P. (2016),
Companies Are Reimagining Business Processes
with Algorithms, Harvard Business Review,
February

13. Variety of Data Types & Big Data Challenge
1.Astronomical
2.Documents
3.Earthquake
4.Email
5.Environmental sensors
6.Fingerprints
7.Health (personal) Images
8.Graph data (social network)
9.Location
10.Marine
11.Particle accelerator
12.Satellite
13.Scanned survey data
14.Sound
15.Text
16.Transactions
17.Video Big Data consists of extensive datasets primarily in the characteristics of
volume, variety, velocity, and/or variability that require a scalable
architecture for efficient storage, manipulation, and analysis.
. Computational portability is the movement of the computation to the location of the data.

14. Categories of Data Sources
1. Transactions
2. External Data (CA Kairos curated data and content packs)
3. Customer data (includes web/e-commerce site Google analytics)
4. Social media and online search data

16. • The data collected in a single day take nearly two million years to playback on an MP3 player
• Generates enough raw data to fill 15 million 64GB iPods every day
• The central computer has processing power of about one hundred million PCs
• Uses enough optical fiber linking up all the radio telescopes to wrap twice around the Earth
• The dishes when fully operational will produce 10 times the global internet traffic as of 2013
• The supercomputer will perform 1018 operations per second - equivalent to the number of stars in three
million Milky Way galaxies - in order to process all the data produced.
• Sensitivity to detect an airport radar on a planet 50 light years away.
• Thousands of antennas with a combined collecting area of 1,000,000 square meters - 1 sqkm)
• Previous mapping of Centaurus A galaxy took a team 12,000 hours of observations and several years - SKA
ETA 5 minutes !
To the scientists involved, however, the SKA is no testbed, it’s a transformative instrument which, according to
Luijten, will lead to “fundamental discoveries of how life and planets and matter all came into existence. As a
scientist, this is a once in a lifetime opportunity.”
Sources: http://bit.ly/amazin-facts & http://bit.ly/astro-ska
Galileo
Square Kilometer Array Construction
(SKA1 - 2018-23; SKA2 - 2023-30)
Centaurus A

17. The following BigQuery query (note that the wildcard on "TAX_WEAPONS_SUICIDE_" catches suicide vests, suicide bombers, suicide bombings, suicide
jackets, and so on):
SELECT DATE, DocumentIdentifier, SourceCommonName, V2Themes, V2Locations, V2Tone, SharingImage, TranslationInfo FROM [gdeltv2.gkg] where
(V2Themes like '%TAX_TERROR_GROUP_ISLAMIC_STATE%' or V2Themes like '%TAX_TERROR_GROUP_ISIL%' or V2Themes like
'%TAX_TERROR_GROUP_ISIS%' or V2Themes like '%TAX_TERROR_GROUP_DAASH%') and (V2Themes like '%TERROR%TERROR%' or V2Themes like
'%SUICIDE_ATTACK%' or V2Themes like '%TAX_WEAPONS_SUICIDE_%')
The GDELT Project pushes the boundaries of “big data,” weighing in at over a quarter-billion rows with 59 fields for each record, spanning
the geography of the entire planet, and covering a time horizon of more than 35 years. The GDELT Project is the largest open-access
database on human society in existence. Its archives contain nearly 400M latitude/longitude geographic coordinates spanning over 12,900
days, making it one of the largest open-access spatio-temporal datasets as well.
GDELT + BigQuery = Query The Planet

18. Oil reserves shipment monitoring
Ras Tanura Najmah compound, Saudi Arabia
Source: http://www.skyboximaging.com/blog/monitoring-oil-reserves-from-space

19. https://nodexl.codeplex.com/

20. Airbnb Power BI App

21. Connecting Power BI with Massive Big Data

22. Big Data Use Cases for Advisory Practice
Forecasting (Financial) or predictive analytics using external big data sources e.g. Airbnb and Web/e-comm site
Investor deck for startups and early stage including financial reporting and potential e-commerce revenues
Cross border/interstate business expansion
Cost of Capital Estimates
Risk Management including reputation (use social media channels)
M & A
Fraud
Spend Analytics
Continuous Auditing and/or missing inventory e.g. via Drone

23. 23© 2017 FORRESTER. REPRODUCTION PROHIBITED.
trillion
$862 billion
$19 billion
$397 billion
$1.5 the amount of USD spent online globally in 2016
North
America
Latin
America
Western
Europe
Asia
Pacific
$248 billion
Source: Forrester Research ForecastView Online Retail Forecasts
*Global figure comprises 27 countries: Argentina, Australia, Austria, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, Greece, India, Ireland, Italy, Japan,
Luxembourg, Mexico, Netherlands, Norway, Portugal, South Korea, Spain, Sweden, Switzerland, United Kingdom, and United States
Rate based on CAGR 2015 to 2021
10.6%
12.3%10.3%
8.3%
11.3%
CAGR
How fast eCommerce is growing globally

24. 24© 2017 FORRESTER. REPRODUCTION PROHIBITED.
Data aggregated from IBIS World reports, 22 August 2017

25. Online tenure leads to more spending per customer
High engagement leads to more orders, more
categories purchased, and more spend
https://www.quillengage.com

26. http://recount.com/recount-expert-financial-analysis/

28. SimilarWeb - visiondirect.com.au

29. GoogleTrends

30. Alexa - visiondirect.com.au

31. Language on Twitter Tracks Rates of Coronary Heart
Disease, Psychological Science, January 2015
31
The findings show that expressions of negative emotions such as anger, stress, and fatigue in the tweets from
people in a given county were associated with higher heart disease risk in that county.
On the other hand, expressions of positive emotions like excitement and optimism were associated with lower
risk.
The results suggest that using Twitter as a window into a community’s collective mental state may provide a
useful tool in epidemiology…So predictions from Twitter can actually be more accurate than using a set of
traditional variables.

32. 32
Sherman and Young (2016), When Financial Reporting
Still Falls Short, Harvard Business Review, July-August
Sood (2015), Truth, Lies and Brand
Trust The Deceit Algorithm, http://datafication.com.au/
New Analytical ToolsCan Help

33. 33
Deception Algorithm
(1) Self words e.g. “I” and “me” – decrease when someone
distances themselves from content
(2) Exclusive words e.g. “but” and “or” decrease with fabricated
content owing to complexity of maintaining deception
(3) Negative emotion words e.g. “hate” increase in word usage
owing to shame or guilty feeling
(4) Motion verbs e.g. “go” or “move” increase as exclusive words
go down to keep the story on track

34. http://www.analyzewords.com
34

35. “The honest answer is this:
The accountant of the future might not be an
accountant at all. Career paths and internal
structures will change dramatically”
…a new type of "analytics" accounting professional is evolving and joining
existing practices. Initially, these individuals may well not have CA credentials
but are productive from day one if they possess an analytical mindset with an
ability to utilise relevant data tools and tech to gain insights from accounting or
more broadly business information.
At the same time, the individuals with existing data science or analytic skills
have the opportunity to compliment such skills with accounting skills through
bridging style conversion courses or accounting boot camps for data analysts or
data scientists.

36. The future is impossible to predict. However
one thing is certain :
The company that can excite it’s customers
dreams is out ahead in the race to business
success
Selling Dreams, Gian Luigi Longinotti
36