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Industries and businesses are growing at an exponential rate off the backs of the quickly evolving technologies available to them. These technologies are allowing companies to enter new markets and provide a quality of service never seen before. This paper will go over the business implications, limitations, and audit effects for the new emerging technologies of data analytics, block-chain and cryptocurrency; and data mining with big data.
Data analytics are processes or techniques of interpreting data to enhance productivity and profits. Data analytics is primarily conducted in business-to-consumer applications. Organizations are analyzing data associated with customers, processes, or economics. “Data is categorized, stored and analyzed to study purchasing trends and patterns.” (Techopedia, 2018)
There are four distinguishable types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Descriptive analytics sorts through data from multiple sources to help give valuable insight into the past; however, descriptive analytics is limited by it only being able to distinguish between what was right or wrong, not why. Diagnostic analytics uses trends found in historical data to compare against new data to answer the question of why something happened. Businesses use diagnostic analytics to gain insight into a problem. Predictive analytics predicts likely outcomes. “It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting”. (Bekker, 2017) Prescriptive analytics can enhance a company’s ability to more effectively purchase materials or advertise their business to strategically selected customers.
Businesses have always collected data to help make inferences about the future. With computers, the process of data collection is potentially endless. Companies have stored data in the past but have not been able to gain any significant insight until the application of data analytics. It takes computers and algorithms to interpret such large quantities of data. Data analytics bridges the gap between the data and decision makers. Data analytics helps decision makers predict the future, learn from the past, and decide on what to do next but like any process it comes with its own limitations.
Data analytics are limited by the quality of the data going into them and the power of the central processing unit (CPU) from which they operate. Data analytics predictions or analyses are only as good as the data going into them. Garbage going in means garbage coming out. Companies using data analytics need to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization.
Data analytics is not one size fits all. Companies can buy the programs to run data analytics, but they need to make sure those systems are compatible with their current operating systems. Companies need to assess whether their CPUs have the power to run more sophisticated algorithms to process larger amounts of data. Even with the challenges that come with data analytics, time has shown that the benefits far exceed the costs. Companies are continuously developing new ways to use data analytics. Even public accounting firms are using data analytics in their audits of public and private companies.
Companies are relying more on data analytics to base their estimates on. Estimates have always been a high-risk part of the audit, but when those estimates are based off sophisticated algorithms, it makes it even more difficult for auditors to test for accuracy. Instead auditors must focus on the testing of controls to obtain reasonable assurance. Audit firms must adapt to their clients growing use of data analytics by using data analytics of their own. Audit firms are no different than the companies they audit, they see the benefits of data analytics and will use those benefits, to gain a better understanding of their client.
With data analytics audit firms hope to test complete sets of data rather than just testing samples. Data analytics can minimize the detection risk of missing an error or misstatement that may be material to the financial statements. Analytical procedures are also greatly enhanced using data analytics. Now risk assessment can be performed through identification of anomalies and trends, comparison to industry data, and pointing auditors toward items they need to investigate further. (Murphey & Tysaic,2015) Audit firms are having to adapt to the rapidly evolving technologies their clients are using to maintain their ability to provide quality audits.
Block Chain and Cryptocurrency
Block chain technology is a transparent publicly accessible general ledger that allows users to securely transfer measures of value using public key encryption and proof of work methods. (Alyson, 2018) When a transaction occurs, each party agrees to the details which is then encoded into a block of digital data and uniquely marked. Each block is connected to the one before it and after it. The connection creates an irreversible, unalterable chain that prevents any block from being altered or the chain of blocks manipulated. (IBM, n.d)
Cryptocurrency is a digital currency encrypted to regulate the generation of units of currency and verify the transfer of funds. Cryptocurrency operates independently of a central bank and is virtually anonymous. Cryptocurrency uses block train to create a virtual market place where currency can be exchanged for products without paying tax or having records of purchase. Similar to stocks and bonds, cryptocurrency rises and falls in response to investor habits in its market and is somewhat easy to convert into legitimate currencies.
At its most basic, blockchain is a facilitator of exchange. Where not just information is exchanged but “anything of value – money, titles, deeds, music, art, scientific discoveries, intellectual property, and even votes”. (Tapscott, 2016) Blockchain is used when transparency and speed are a top priority. Blockchain allows customers, venders, and regulators complete access to a company’s transactional history. It ensures integrity and trust between strangers, which makes it difficult to cheat. Businesses are constantly looking for ways to exchange value and yield the highest gains for those exchanges. Block chain facilitates this need by providing a fast, precise, and transparent way to perform transactions. Block chain also reduces transactional costs for companies which goes further to increase profit margins. Block chain is what drove cryptocurrency to become the high yield, volatile, financial instrument that it is today.
Similar with blockchain, cryptocurrency promotes transparency, speed, and accuracy. Currently, cryptocurrency is mainly used on the black market and is typically involved in transactions where anonymity is required. Even though the transaction’s details are public, the users of cryptocurrency are anonymous. This benefits users who wish to have a buffer of sorts in between their credit card activity and their purchases. Cryptocurrency also reduces processing costs and has high transactional speed. Cryptocurrency is basically the equivalent of “cash dealings” but at a magnified level. The unique characteristics of block chain, places auditors in a new environment where the conventional audit practices may not be suitable.
Blockchain has many benefits, but ultimately companies do not want that level of transparency in business operations. Businesses want their transactions to remain private. This is not to suggest that those transactions are illegal, but that companies do not wish for competitors or customers, to know their true costs or dealings. Companies lose leverage when they adopt high levels of transparency. Furthermore, the amount of power a company will consume when it adopts block chain will increase significantly. Businesses must run a cost benefit analysis to determine if the amount saved in transactional fees will be more than the amount spent in energy.
Cryptocurrency is run off blockchain and comes with all of the limitations found in blockchain. An added limitation of cryptocurrency is that it is highly volatile. By the time a transaction is processed and converted into conventional currency, it may have significantly changed in value. In that situation there will always be a winner and a loser. This is not attractive for business owners because it creates uncertainty in transactions where uncertainty typically does not exist. If cryptocurrency was a stable form of currency, then there could be real business applications; unfortunately, that is currently not the case.
Change is coming in the auditing world due to blockchain technology. Blockchain creates a permanent record of transactions which can be audited, using code. The role of auditors will evolve as blockchain starts to be used in accounting practices. Auditors will have to define standards and strategies of block chain accounting. These standards will then be coded into a blockchain, so transactions are auditable in real-time rather than after the fact. Blockchain is a form of internal controls, so the integrated audit may be a thing of the past. It seems like the most important audit, in a world where businesses run off blockchains, would be an audit of the block chain itself. That kind of audit would be done by individuals with computer science and accounting backgrounds.
There are ongoing arguments about whether there are any real business applications in cryptocurrencies. Despite the wide array of opinions, top four accounting firms are gearing up to audit companies using cryptocurrency. Some firms are creating software internally to track and audit cryptocurrency transactions while others are partnering with the private and public sectors to branch into the cryptocurrency market. (NEWS BTC, 2018) The use of cryptocurrency and blockchain ultimately limits the role of an auditor in transactional audits. With blockchain audits can be done in real time for some applications, rather than year-end sampling.
Data Mining with Big Data
Data Mining is the process of finding patterns, anomalies and correlations from large data sets to predict outcomes. It is mainly used in statistics, machine learning and artificial intelligence. (“Difference Between”, n.d) Due to recent advancements in processing power, data mining no longer requires manual analysis but can be analyzed through automation. The more data, a data mining model can learn from, the more relevant insights it can uncover. Big data is large volumes of data collected from businesses daily dealings with customers, venders, basically any interaction they can gain insight from. Big data is perfect for data mining as it provides complex and large data sets that help provide valuable insights into a company.
Similar with data analytics, datamining uses historical data to make inferences about hypothetical or future events. Data mining uses statistical and machine learning techniques to predict customer behavior. Datamining can perform exploratory analysis and help predict, classify, or cluster information that allows business users to make educated decisions. The results from the data mining models can be made into visualizations or dashboards to help business users better understand the information in front of them. (Augusteen,2017) For example, a pattern might indicate which customers are more likely to default on loans. This information could help the lenders determine a more efficient underwriting process that will decrease the amount of high risk loans underwritten.
Big data aids the process of data mining by providing large quantities of data for models to learn from. Data mining works best when it can train from large quantities of complex data sets. Together, big data and data mining can find patterns in populations, weather, stock markets, and diseases. Typically, financial services and marketing companies are the most well-known users of data mining but now we see companies using datamining in energy, diagnosis, and research. There is an endless source of implications the data mining can be used. With the right kind of data and power to run the data mining models, companies can create value from the information they have collected from daily operations.
There are three main limitations to datamining: privacy, security, and inaccurate information. Companies like Facebook and amazon are continuously collecting data on their customers. In some ways, this may benefit consumers as it allows businesses to predict trends that result in better customer service, but some people are starting to get concerned about the use of that data. Currently, privacy is not so much a limitation because it is pretty much the wild-wild west now for data. There are no real regulations and companies can do as they will with the data they collect. Although companies should prepare for limitations in the future as the environment of data collection may not always remain unregulated.
Security is a big limitation for companies when they use datamining. Companies who collect big data may have very sensitive information stored. “Businesses own information about their employees and customers including social security number, birthday, and payroll” (Zentut, 2018). If these companies are hacked then all this information is now at risk of being used with malicious intent. As companies increase their collection and storing of data they need to simultaneously increase security to prevent possible hacks. This increase in storage space as well as the security, are additional costs companies will incur to capitalize on the benefits of big data and data mining.
Inaccurate information whether from error or irrelevance can skew the data mining results giving unhelpful information to decision makers. Similar with data analytics, if garbage data goes in, then garbage data goes out. Companies not only need to provide securities for the data but they also need to provide controls for that data to ensure it is not tampered with or incorrectly inputted. When data mining learns from the data sets containing a few incorrect rows of data, it can train the model conclude false reasonings which lead to incorrect predictions.
Datamining is making auditors jobs a bit easier by allowing software to process large quantities of data in a significantly short amount of time. Datamining software does not alter the structure or components of the data but allows auditors to explore that data and convert the data to common platforms. After the conversion, auditors can then analyze the data to gain insight into their clients and aid in the audit process. Analyses that used to take several weeks are now done in hours. The top audit firms are gearing up to use any new technology available to them in their audit processes, especially datamining. Data mining helps audits create expectations for their testing which would then be compared to a company’s actual account balance or business behaviors.
We are currently seeing an explosion in growth in many different industries and many different companies. As technologies continue to advance we will see a corresponding growth in businesses. Most of these advancements have been seen in data analytics, block-chain and cryptocurrency; and data mining with big data. This paper went over the business implications, limitations, and audit effects for these new emerging technologies in order to gain insight into their business uses.
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