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The Mystery is in the Data

The Mystery is in the Data

Photos by Emma Highfill, Rose Wheat Photography

Using data to inform decisions is something businesses have always done. Companies have long been using structured data to analyze issues and predict the future. However, today with over 2.5 quintillion bytes
of data collected daily (Forbes, 2018), businesses have much more structured and unstructured data to inform practices and solve problems. In addition, the advancements in technology make it possible to collect, store, process, and convert vast amounts and varieties of data into useful information for evidence-based decision-making.

The massive amount of data, commonly called "Big Data," was initially identified by four main attributes, referred to as the 4 V’s. Today, there are 7 V's adding three to the original four.

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The types of data available for management decision-making come from a variety of sources and are often free. For instance, government websites have census, crime and spending data. Consider the Internet of Things from which information is generated by such things as appliances, cars, mobile devices, fitness trackers and GPS devices. Uses of such data are numerous. For example, a company could monitor employees’ activities through fitness trackers to reduce health insurance costs.

With technology pervasive in business today, companies now both generate and collect massive amounts of information from internal and external sources. Bernard Marr’s 2016 book, "Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results," describes how companies used much more than internal financial data, and based decisions and invented whole new lines of business by utilizing insights gathered from the diverse types of data.

We all leave ‘digital footprints’ generated by our devices in business and in life. Modern business strategy is now informed by analysis of data from geographic locators (GPS), sensors, social media, text, voice, video and weather data, to name a few. Data analytics is the “new norm.” The future of organizations will be determined by their ability to analyze data and derive insights to improve business decision-making. For example, the leading firms in the accounting profession now seek to hire employees with data science and data analytic skills as well as accounting skills.

All areas of accounting are influenced by the digital transformation which includes information-based decision-making. Auditing, for example, is being transformed by using new forms of data, including full live data streams monitored for a continuous audit. Technology automation of routine business functions, including the integration of big data, impacts the type of “work” being done by humans.

To see the future effect on accounting, view KPMG’s, “Audit 2023: Audit technology fit for the future,”

The concept of automation builds upon the use of data analysis when monitoring real-time data flows that can then be used in automated processing and computer decision-making. As tasks are automated, individuals in all areas of business have more time to devote to value-added activities. Exhibit 1 shows all areas of business are being disrupted by automation.

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Although businesses and business problems differ, the data analysis processes used to find insight is similar. After identifying a problem or an issue, it is critical to determine what data is relevant. Data discovery involves identifying the data needed and sources of that data. Data is plentiful and can be internally generated, purchased externally, or many times downloaded free from the web using web scraping tools.

Once the sources are determined, the data must be extracted, transformed and loaded (ETL). The extracted data needs to be cleaned (transformed) to ensure it is complete, error and corruption free, and formatted and structured consistently so that it can be integrated (loaded) and accessible for analysis. Through data modeling, the connections among the data are identified, which leads to more enriched data analysis.

At this point, using data analytics tools, the mysteries in the data can be identified and provide the information for evidence-based decision- making. Analyzing data does not always require statistics. Sometimes simply converting the data into a visual is enough to show patterns, relationships and issues. Analyzing data can help companies determine what has happened, provide insights into what could happen, or offer an understanding of possible future outcomes. For instance, hotels analyze the comments on Trip Advisor to monitor customer satisfaction. Retailers analyze customer purchases to offer individualized discounts and coupons. Airlines know when and where major athletic events will be held and analyze past travel patterns to determine flight schedules.

Analyzing data can lead to business insights for problem-solving and decision-making only if the analysis is effectively communicated. Just as companies use dashboards (visual representations) to monitor production, track key performance indicators and communicate with employees and stakeholders, effectively communicating the data analysis results requires data visualization. There are many data visualization software choices available that offer

a multitude of visualization options from simple line graphs to heat maps to interactive dashboards. What is essential is that the visuals convey the information simply and accurately and with an accompanying narrative that provides context and insight that leads to evidence-based problem-solving and decision- making.

Multiple data analytic software products are available for organizing, analyzing and visualizing data. Any business that has a license for Microsoft Excel can download Power BI Desktop free. Exhibit 2 shows a screenshot example.

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As the advances of artificial intelligence and technology continue to outpace human’s ability to adapt more tasks will be automated as illustrated in 
Exhibit 3.

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As companies automate routine tasks and implement data analytics, employees’ work changes, and new and different skills sets are needed.

However, the supply of individuals with the necessary skills sets to perform data analytics has not met the growing demand as illustrated in Exhibit 4. Companies are addressing the changes by developing employee teams that together have the skills needed for this new approach to business. In response, universities are beginning to offer certifications, courses, and degrees in data science and data analytics. The School of Business at Washburn University is no exception. Starting fall 2019, Washburn will offer a Foundations of Data Analysis course. They are also planning to offer seminars and develop a major area of concentration in Data Analytics that will allow Washburn to partner with businesses and provide students with the skills necessary for success in business.

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