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Keeping up with the Pace: Big Data, AI & Internet of Things Data Analysis

Explore the importance of big data, artificial intelligence, and internet of things in companies and auditors. Learn about the driving force behind big data and discover case studies and application areas of AI and IoT.

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Keeping up with the Pace: Big Data, AI & Internet of Things Data Analysis

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  1. KEEPING UP WITH THE PACE Big Data Artificial Intelligence & Internet of Things Data Analysis Paul M. Perry, FHFMA, CISM, CITP, CPA Member, Risk and Controls Practice Leader Yogesh N. Patel, CPA, CFE Member, Audit and Assurance

  2. Big Data Everything we do creates a digital trace (or data). We use that data for analysis, prediction, and testingCollection of data (traditional and digital) from sources (internal and external) • WHAT IS BIG DATA?

  3. Big Data DATASETS – large, complex and awkward to work with • WHAT IS BIG DATA? - Volume - Variety - velocity Mass amounts of data are getting larger. Different types of information and datasets are being created. The rate that data is being generated and captured is astronomical.

  4. Big Data Help a Company better staff for busier days/hours. • WHY IS BIG DATA IMPORTANT TO COMPANIES? Company could predict better event month based on Weather. Accounting department could predict when fraud was going to occur. Review monthly financial information for trends.

  5. Big Data Compare revenue to industry averages • WHY IS BIG DATA IMPORTANT TO COMPANIES / AUDITORS? Compare revenue by widget to cost centers – shift resources Compare revenue to check-in information to validate revenue Compare addresses for employees to addresses of vendors – any similarities.

  6. Big Data

  7. Big Data Facts • 0.5% • BIG DATA – Bernard Marr Amount of data stored that is ever used for analysis and projection. Imagine the possibilities...

  8. Big Data 1. Strategic Plan -Identify business priorities 2. Identify opportunities - Brainstorm 3. Determine data sources • Current data landscapeIs new technology needed? 4. Identify use cases - Helps prioritize resources to the biggest impact or cause 5. Pilot - Test drive tools and quality of data – refine process 6. Implementation - Capitalize on newly found data relationships and findings • Companies need a roadmap

  9. Big Data • Walmart • Express Scripts • Sports • MoneyBall (Oakland A’s) • Football, Hockey and Basketball • McDonalds • Healthcare System • Examples of Big Data Uses

  10. WHAT IS THE DRIVING FORCE BEHIND BIG DATA? Artificial Intelligence & Internet of Things

  11. Artificial Intelligence & Internet of Things The Future Started Yesterday • Nikola Tesla (1926 interview) “When wireless is perfectly applied the whole earth will be converted into a huge brain…and the instruments will be simple…” • John Romkey’s Toaster • Mark Weiser (1991 article) “The most profound technologies…disappear. They Weave themselves into the fabric of everyday life…”

  12. Artificial Intelligence & Internet of Things Business uses for AI Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. • Everyday examples: • Alexa • Uber/Lyft • Google Maps/Waze • Commercial Airlines • Spam filters

  13. Artificial Intelligence & Internet of Things What is the Internet of Things? The Internet of Things (IoT) is the network of physical objects—devices,, software, sensors, and netvehicles, buildings and other items—embedded with electronicswork connectivity that enables these objects to collect and exchange data. • Machine to Machine Communication • If all objects and people in daily life were equipped with identifiers, computers could manage and inventory them.

  14. Artificial Intelligence & Internet of Things Case Studies • Miami Airport – internet-connected sensors and beacons • CSX – adding sensors to railcars and railroad tracks • John Deere – installing internet connected sensors throughout tractors – • Predictive analytics to determine when moving parts might fail • Restaurants – sensors on appliances (ovens, warmers, refrigerators) • Facial Recognition Devices – detect smiling vs mad/frowning faces • Suppliers – internet-based counters / usage tags to order consumables when they reach a certain level (weight sensors) Creating Proactive Customer Service!!

  15. Artificial Intelligence & Internet of Things Application Areas Provided by Texas Instruments • Automation (home and building) • Access control, light and temperature control • Manufacturing • Real time inventory requests, asset tracking, employee safety • Wearables • Health, location and tracking and entertainment • FitBit and Apple Watch • Legal Cases • Cows • Automotive • Wire/bulb replacement, predictive maintenance, Car to Car

  16. Artificial Intelligence & Internet of Things Challenges of AI & IoT • Invasion of Privacy • Big Data • Cost • Cybersecurity • Early StagesGoogle, Amazon, Intel, TI, Nest, and Cisco (early innovators)

  17. WHAT DO YOU DO WITH THE DATA ONCE IT HAS BEEN COLLECTED? Data Analysis

  18. Big Data • Data without action is a waste of time and space. • …it is about the analysis, solution and change. It is not about the data…

  19. “The science of examining raw data with the purpose of drawing conclusions about that information.”Margaret Rouse • DATA ANALYSIS

  20. Big Data Data Analysis

  21. Data Analysis ExploratoryPredictive • Types of Data Analysis Locate unknown relationships. Reveal underlying secrets/facts of the data. Reveal outliers or anomalies of the dataset. Examples include graphs or plotting of information. Benford’s Proportion Law Using current and historical facts to predict the future. Data Mining, regression analysis and forecasting. Study the behavior of a population, find anomalies and predict future behavior. Credit Scoring by financial service entities. IRS uses for prediction of potential tax frauds.

  22. Data Analysis Benford’s Law – First Digit law Refers to the frequency distribution of digits in many (but not all) real-life sources (i.e. naturally-occurring) data. In this distribution, 1 occurs as the leading digit about 30% of the time, while larger digits occur in that position less frequently: 9 as the first digit less than 5% of the time. Benford's law also concerns the expected distribution for digits beyond the first: • First two digits – most commonly used – more granular • Second digit • Last digit

  23. Data Analysis Benford’s Law - Continued 1938 Frank Benford – Physicist • Surface areas of 335 rivers, • The sizes of 3259 US populations, • 1800 molecular weights, • 5000 entries from a mathematical handbook, • 308 numbers contained in an issue of Reader's Digest, • The street addresses of the first 342 persons listed in American Men of Science • The total number of observations used - 20,229.

  24. Data Analysis Benford’s Law - Continued

  25. Data Analysis Benford’s Law - Continued

  26. Data Analysis Benford’s Law - Continued

  27. Data Analysis Benford’s Law - Continued In 1993, in State of Arizona v. Wayne James Nelson (CV92-18841), the accused was found guilty of trying to defraud the state of nearly $2 million, by diverting funds to a bogus vendor. The perpetrator selected payments with the intention of making them appear random: • No duplicated check amounts • No round-numbers - all the values included dollars and cents amounts. He did not realize that his seemingly random looking selections were far from random. And the chart of amounts to the fictitious vendor looked like….

  28. Data Analysis

  29. Data Analysis Data Analysis Techniques Relative Size Factoris to identify anomalies where the largest amount for subsets (vendor, customer) in a given data set is outside the norm for those subsets. This test compares the top two amounts for each subset and calculates the RSF for each. Examples Vendor A – $5,000 and $4,000 = RSF is 1.25 Vendor B – $200,000 and $20,000 = RSF is 10.00 Question - Which is consistent with your company?

  30. Data Analysis Internal Control Application Adding Data Analysis to Internal Controls of a Company • Disbursement fraud • Perform detailed review of a sample of checks/invoices before signing or releasing electronic signature • Perform periodic review of vendors and inactivate unused vendors • Perform periodic review of bank statements, including digital check copies • Payroll fraud • Perform an independent review of the system-generated payroll file • Perform an annual review of W2 and 1099 reports showing total compensation paid to each employee • Compare W2 listings to company phone list, system access listing, or other independent employee listing

  31. Data Analysis Sample Projects or Tests • Vendor file review - compare employee listings to vendor listings • Inventory detail extraction/review – trends in inventory for last couple years • Company Sales Activity Prediction – trends in sales by division or services • Accounts receivable details – re-aging and sampling • General ledger detail – conversion for easier review of journal entries • Disbursement testing – fraud analysis projects or internal control issues • Payroll system – comparing employee addresses or trends in hire/termination dates. • Convert bank/investment statements to Excel

  32. Data Analysis Tools You Need Data Analyst – Data minded individual. Experience with large datasets and trending analysis experience. Data Extraction/Management Software –data reorganization or data manipulation Data Analysis Software (Excel Add-ins) –ability to slice and dice data, summarize it, split the information, duplicate the information, locate anomalies, etc. Open Mind or “Think out of the Box” Mentality –ability to look at your data with fresh eyes or be open and willing to hear what it has to say. Follow paths that are unexpected.

  33. Data Analysis Skills You Need - Analyst

  34. Data Analysis Tools You Need

  35. Data Analysis Tools You Need - Software • Summarize–similar to PivotTable (however, you cannot easily change summarization). • Duplicates–scans a listing for duplicate items based on criteria you set. Allows you to tag duplicates or permanently remove them from the listing. Good for removing duplicates from a listing to be sampled. Provides evidence that no duplicates exist. • Age–ages the listing based on criteria you set. Good for re-aging a clients AR. • Gaps–locates gaps in sequence of numbers. Good for journal entry completeness testing or check register testing –looking for missed/voided checks. • Statistics–provides new statistics about a listing –maximum, minimum, total number, number of positive/negatives. Good for an overview of AR or Inventory. • Sample –used for random sampling of a large listing. • Cells–adding indexing to, transposing contents or removing portions of information in a cell

  36. Data Analysis Data Reorganization Data Extraction of AR Detail by Location – i.e. multiple locations of AR detail converted into one spreadsheet

  37. DISCUSSION ORQUESTIONS

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