CPA Practice Advisor

JUL 2018

Today's Technology for Tomorrow's Firm.

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4 JULY 2018 ■ www.CPAPracticeAdvisor.com FROM THE TRENCHES RANDY JOHNSTON EVP & Partner K2 Enterprises & CEO of Network Management Group, Inc. randy.johnston@cpapracticeadvisor.com @RPJohnston Why and How: BIG DATA IF COGNITIVE COMPUTING drives much of the emerging technology computer science research, most of the data science research is focused on Big Data. Big Data can be analyzed to produce actionable business information. This data analysis need may explain the number of data scientists that are being hired by mid-sized and large firms. The amount of data produced by transaction processing is increasing notably because of the Internet of Things and the amount of detail in transaction systems. As consultant to the profession Brian Tankersley has observed: “These transactions produce “digital exhaust” which can be captured and analyzed.” Big Data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on large, unstructured data sets Big data very often means “dirty data” or “big bad data” and the fraction of data inaccuracies increases with data volume growth. Data scientists may need to check that the data is relevant, connected (meaning it is related and complete), accurate (but the data can be precise/imprecise), and that there is enough data to work with. If the data is ready for processing, according to Brandon Rohrer, senior data scientist at Microsoft, data science answers five questions: • Is this A or B? • Is this weird? • How much – or – how many? • How is this organized? • What should I do next? There are four types of data analytics that can be run on Big Data including: • Descriptive Analytics: What’s happening in my business? • Diagnostic Analytics: Why is it happening? • Predictive Analytics: What’s likely to happen? • Prescriptive Analytics: What do I need to do? Like all of the emerging technologies we have covered in these columns, Big Data has pros and cons. ON THE POSITIVE SIDE: • Extremely large data sets may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions • Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on” according to The Economist, June 2017 On the down side: • Challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy • Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks • The work may require “massively parallel software running on tens, hundreds, or even thousands of servers“ according to Adam Jacobs in an ACM (Association for Computing Machinery) article The term Big Data has been in use since the 1990s, with some giving credit to computer scientist John Mashey, formerly of Bell Labs, for coining or at least making it popular. A notable challenge is how to make the reporting simple enough for smaller businesses or firms to be able to process data effectively. Alternatively, with small businesses, the amount of data may remain small enough that there is insufficient data for the algorithms to produce meaningful data analytics.

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