Big data presents both opportunities and challenges for businesses and professionals. It offers the ability to gain valuable insights from vast amounts of complex data from diverse sources. However, it also poses challenges such as integrating and analyzing unstructured and heterogeneous data types at large volumes, velocities, and varieties. For accounting professionals, big data can enhance audit, advisory, tax, and managerial services through more data-driven decision making, but professionals need skills in coding, analytics, and business expertise to maximize its potential and address privacy and ethical concerns.

1. Big Opportunity & Big Challenges
Supervised By:
Prof. Samir Abdel Fatah
Prepared by:
Amany Osama Mohamed
Group 6
Big Data

2. Outline
• What is Big Data?
• The Big Data Opportunity
• Big Data – Defining the Challenge
• Key Challenges Across Industries
• Other Aspects of Big Data
• What is the Big Data Impact on the Accounting Professional?
• What are employers looking for?…
• References
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3. What is Big Data?
Data sets so large, so
complex that
traditional software
tools can’t work with
them.
Why now?
• More data (volume)
• Velocity (faster rate)
• Types (variety)
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4. Source : http://www.nojitter.com/post/240170228/the-network-impact-of-big-data
Chart is from the United Nations Economic Commission for Europe (UNECE)
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5. Extracting insight from an immense volume, variety and velocity
of data, in context, beyond what was previously possible.
The Big Data Opportunity
Manage the complexity of
multiple relational and non-
relational data types and
schemas
Streaming data and large
volume data movement
Scale from terabytes to
zettabytes (1B TBs)
Variety:
Velocity:
Volume:
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6. Just a few facts…
• More data has been created in the past 2 years than in the entire history of the human race
• Every second we create new data. 40,000 searches on Google alone are made every second for
a total of 1.2 trillion searches per year.
• By 2020 1.7 megabytes of new information will be created every second for every human on the
planet.
• For a typical Fortune 1000 company just a 10% increase in data accessibility will result in more
than $65 million additional net income.
• Less than 0.5% of all data is ever analyzed and used.
Source: http://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-
read/#7d8cd4b6c1d3
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7. Big Data – Defining the Challenge
“Big Data has such a
vast size that it
exceeds the capacity
of traditional data
management
technologies; it
requires the use of
new or exotic
technologies simply
to manage the
volume alone.”
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8. Some Challenges in Big Data
 Big Data Integration is Multidisciplinary
Less than 10% of Big Data world are genuinely relational
Meaningful data integration in the real, messy, schema-less and complex Big Data world of
database and semantic web using multidisciplinary and multi-technology methode
 The Billion Triple Challenge
Web of data contain 31 billion RDf triples, that 446million of them are RDF
links, 13 Billion government data, 6 Billion geographic data, 4.6 Billion
Publication and Media data, 3 Billion life science data
BTC 2011, Sindice 2011
 The Linked Open Data Ripper
Mapping, Ranking, Visualization, Key Matching, Snappiness
 Demonstrate the Value of Semantics: let data integration drive DBMS
technology
Large volumes of heterogeneous data, like link data and RDF
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9. • Big Data analysis and data analytics promise new
opportunities to gain valuable insights and benefits –
new predictive modes of analysis;
• But, it will also enable expanded surveillance,
increasing the risk of unauthorized use and
disclosure, on a scale previously unimaginable.
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10. Big Data Technology is Not Foolproof
• “Despite rampant interest from enterprise
leaders and often sizeable investments in
Big Data technologies, many programs still
sputter or fail completely.”
— Evanta Leadership Network,
May 29, 2014.
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11. Big Data: More Than Just Input
• “In the afterglow of Big Data’s buzz, many
organizations are finding that successful
programs require much more than simply
plugging data into a program.”
— Evanta Leadership Network,
May 29, 2014.
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12. Big Data is moving from its
inflated expectations phase
to a trough of disillusionment.
— Gartner Hype Cycle,
April 2014
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13. Key Challenges Across Industries
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14. Key Challenges Across Industries
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15. Other Aspects of Big Data
1- Automating Research Changes the Definition of Knowledge
2- Claim to Objectively and Accuracy are Misleading
3- Bigger Data are not always Better data
4- Not all Data are equivalent
5- Just because it is accessible doesn’t make it ethical
6- Limited access to big data creators new digital divides
Six Provocations for Big Data
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16. Extensive and systematic use of data,
statistical and quantitative analysis,
exploratory and predictive analysis, and
fact-based management to drive business
decisions and actions.
(Davenport, 2006, HBR)
Big Data in Business
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See Video

17. What is the Big Data Impact on the
Accounting Professional?
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18. Big Data Implications for Accounting
Professionals
• Audit
• Advisory
• Tax
• Managerial
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19. Implications for Accounting Professionals
• Audit – Internal and External
• Data driven audits
• Better experience for the client
• Better experience for the auditor
• More valuable insights
• Improving corporate compliance
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20. Implications for Accounting Professionals
• Advisory Services
• Identify questions
• Use analytics to help business improve performance
• Build analytical models
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21. Implications for Accounting Professionals
• Tax
• Analyze tax efficiency of business units
• Identify tax-opportunities
• Aid in evaluating global opportunities
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22. Implications for Accounting Professionals
• Managerial
• Risk identification
• Risk management
• Operational improvements
• Forecasting
• Optimization models
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23. Data vs. Big Data
• Accounting professionals need to know how to conduct data
analytics regardless of whether it is “Big”.
• Transactional Data can tell us what has happened, Big Data
and data analytics can often help explain why.
• We need to embrace both.
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24. What are employers looking for?…
• An employee with the following skills:
• Ability to code and understand big data technology structures
• Ability to construct experiments, gather and analyze data, make
evidence-based decisions
• Strong communication skills
• Strong quantitative skills in statistical analysis, visual analytics,
machine learning, and ability to analyze unstructured data
• Business expertise – a good sense of where to apply analytics and
big data
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25. References
1. B. Brown, M. Chuiu and J. Manyika, “Are you ready for the era of Big Data?” McKinsey Quarterly, Oct 2011, McKinsey Global Institute
2. C. Bizer, P. Bonez, M. L. Bordie and O. Erling, “The Meaningful Use of Big Data: Four Perspective –Four Challenges” SIGMOD Vol. 40,
No. 4, December 2011
3. D. Agrawal, S. Das and A. E. Abbadi, “Big Data and Cloud Computing: New Wine or Just New Bottles?” VLDB 2010, Vol. 3, No. 2
4. O. Trelles, P Prins, M. Snir and R. C. Jansen, “Big Data, but are we ready?” Nature Reviews, Feb 2011
5. K. Bakhshi, “Considerations for Big data: Architecture and approach” Aerospace Conference, 2012 IEEE
6. S. Lohr, “The Age of Big Data” Thr New York times Publication, February 2012
7. M. Nielsen, “Aguide to the day of big data”, Nature, vol. 462, December 2009
8. Bernard Marr (2015) “Big Data: Using Smart Big Data Analytics and Metrics to Make Better Decisions and Improve Performance”
9. Cole Nussbaumer Knaflic (2015) “Storytelling with Data: A Data Visualization Guide for Business Professionals”
10. Thomas H. Davenport. “Competing on Analytics” &”Keeping up with the Quants: Your Guide to Understanding & Using Analytics”
11. Dona M. Wong (2013). “The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures”
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