Session 10/Week 11 - Accounting, ‘big data’ and the virtual organisation

Question 1

Definition of big data

Answer 1

“Big data is data that contains greater VARIETY arriving in increasing VOLUMES and with ever-higher VELOCITY.”
- by Gartner (2001)
- known as the three Vs

Question 2

3 key differences of big data from regular management accounting information (McAfee and Brynjolfsson, 2012)

Answer 2

1. Data volume
   - Huge amounts of data are created each day, giving companies an OPPORTUNITY to work with a large amt of data in a single data set
   - & not just from the Internet but various other data sources
2. Data velocity
   - For many applications, speed of data creation is even more important than the volume
   - Real-time/near real-time info allows companies to be MORE AGILE than their competitors
   - can help businesses make decisions that provide STRATEGIC COMPETITIVE ADVANTAGES
   eg. the MIT Media Lab used location data from mobile phones to infer the no. of ppl in Macy’s parking lots on Black Friday - to estimate their SALES
3. Data variety
   - Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more
   - big data are not suited to be stored using the structured databases that store most corporate info (eg. can’t store videos in Excel)

Question 3

5 applications of big data (in modern organisations)

+ most Enterprise Resource Planning (ERP) system providers offer big data analytics in their systems
-> CENTRALISES all info in org; big data captures EXTERNAL info into the system

Answer 3

1. IBM {& John Hopkins Uni, UCBerkeley} & health sector
   - used data to develop models to FORECAST OUTBREAKS of epidemics
   - pharmaceutical companies use these forecasts to coordinate/PLAN and adjust their production
2. Testing & simulations
   eg. CERN uses big data to simulate the birth of the universe {particle accelerators are COSTLY to test with}
   eg. Porsche conducts computer simulations to test car frames’ stability using big data, rather than crash tests
3. UNDERSTANDING customer behaviour/preferences ~ creates a “surveillance system”
   - enables org.s to adjust their PRODUCT RANGE and tailor it towards specific customer requirements
   eg. Netflix records which shows a customer is watching and if he/she continues watching the entire movie
4. INFLUENCING customer behaviour
   - by making particular product suggestions based upon past consumption
   eg. Amazon uses ALGORITHMS to make suggestions for add. albums/products based on the albums/products users are currently looking at
   - algorithms could actively SHAPE particular music PREFERENCES
5. AlgoTrading
   - an automated trading system (ATS) is a computer program that creates orders and automatically submits them to a market exchange <- used for around 50% of all trades on the world’s largest financial markets
   - investment bank Goldman Sachs is currently seeking to execute FASTER TRADES, showing that they’re focused on establishing themselves as one of the top players in automated trading

Question 4

Impact of big data on the accounting profession in terms of 2 competing perspectives
1. Complementary perspective (3 points)

incl. the reference

Answer 4

1. In financial accounting & auditing, the big data movement allows new ways of ANALYSING FIN. STT.S and to PREVENT FRAUD {eliminates human error}
   ~ enhancing transparency and stakeholder decision making (Warren et al., 2015)
2. For running complex acct. models
   ~ developing effective management control systems and budgeting processes (Warren et al., 2015)
   eg. for ABC implementation, it is important to quickly process and analyse large data sets to establish cause-and-effect relationships between cost drivers and cost pools
3. For linking geographically dispersed org.s
   - by enabling the collection of real-time info regarding PERFORMANCE of SUBSIDIARIES
   - big data is considered to be a driver for globalisation

Question 5

Impact of big data on the accounting profession in terms of 2 competing perspectives
2. Accounting as a threatened perspective (3 points)

Answer 5

1. Big data & information systems experts are increasingly taking over the conceptual development of acct. tools, which have always been considered as a core acct. activity (LESS threatened for accountants)
2. Calculating information is VERY threatened.
   - big data requires COMPLEX software systems to filter relevant info
   - accountants can only calculate simple Excel files
3. Presenting information is a bit threatened.
   - DATA MINING SOFTWARE usually provides ready-made platforms for generating and presenting info,
   - only require little adjustments by managers

Question 6

What is the new professional agenda / shift in role for management accountants? (ACCA, 2013)

Answer 6

From calculating information -> learning to INTERPRET & understand information + COMMUNICATE the info

1. Develop new METRICS & combine diff. datasets for data valuation
2. Learn new ANALYTICAL SKILLS to interpret big data
3. Communicating the data
4. Overlapping of acct. with other disciplines. New professional hybrids => finance + information + technology

Question 7

6 challenges that big data poses for its use as an SMA tool in organisations + incl. 2 risks from the Podium Data case

Answer 7

1. Quality of info is dependent on the QUALITY of ALGORITHMS
   - the use & maintenance of big data analytics requires EXPERTS and substantial KNOW-HOW
2. Need to have adequate quality MEASURES in place that ensure the RELIABILITY of the data
   - managers need to understand the underlying cause-and-effect relationships of information for DECISION-MAKING, to prevent misleading interpretations
3. Big data poses significant COORDINATION RISKS
   - if diff. software systems need to be used across the diff. subunits of an organisation
   - such systems are extremely COMPLEX & requires COLLABORATION with experts; normally no expert can fully understand the whole system
4. Could result in significant legal costs for organisations
   - org.s must make sure the info is used FAIRLY & LAWFULLY, in accordance with strict rules (‘data protection principles’)
   eg. Legal risks for Podium Data & its customers in compiling data from VARIOUS SUBSYSTEMS into ONE LARGE DATA LAKE in case the organisation is very international.
   - Then they run the risk of accidentally letting information protected by NATIONAL LAWS to cross the border into other countries
5. IT security
   - org.s need to focus on defending the security of their systems and data, eg. avoid hacking
   eg. Podium Data faces potential IT security risk by pooling all systems into ONE SINGLE DATA LAKE -> external + internal risk of staff leaking competitive/sensitive info
6. ETHICS
   - Individuals and org.s are RESPONSIBLE for protecting and respecting the PRIVACY of people & the purpose of which the data is put to use
   eg. the Cambridge Analytica algorithm was highly unethical

Question 8

What additional points does the “Big Data: The Management Revolution” paper by McAfee and Brynjolfsson (2012) make?

Answer 8

1. Big data can make BETTER predictions and smarter decisions.
2. Companies need to RELY MORE on DATA to make important decisions {change company culture}
   - Oftentimes, companies depend on HiPPO - the highest-paid person’s opinion - and experience and intuition.

Question 9

Podium Data example

Answer 9

Applegate et al., 2017
1. Podium Data’s product offering of Big Data can provide better mgmt acct. information to its customers
» variety: allows customers to detect patterns within their diff. data sets
» volume: allows org.s to collect more data by creating a DATA LAKE that can be coupled w/ many diff. servers
» velocity: can generate info quicker than by normal analytics based on MA info in silos (often time saving of months)

Question 10

Reference: 3 limitations of the usefulness of Big data

Answer 10

Warren et al., 2015 “How will Big Data Change Accounting”
Usefulness of big data to companies is limited to…
1. quantity (lack of data),
2. quality (irrelevant data / not credible source)
3. accessibility (lack of expertise in extracting information).