WELCOME TO ANALYTICS AND BIG DATA FOR ACCOUNTANTS!

Analytics is a new force driving business. Tools have been created to measure program effects and return on investment, visualize data and business processes, and uncover the relationship between key performance indicators—many using the unprecedented amount of data now moving into organizations. In this course, you will discuss leading-edge topics in analytics and finance in a session that is packed with useful tips and practical guidance that you can apply immediately.

INTRODUCTORY COMMENTS

One of the first times the author was exposed to the concept of Big Data was when he served as the CFO for a large non-profit early in his career. The organization was going through a major organization restructure, and it was also restructuring its information technology infrastructure and the software tools used to provide information technology services. As a result, the agency brought in an expert on a fourth-generation language database program. The expert had a wife who was suffering from a severe illness. The expert was adamant that if he could obtain a large number of patient records, he would find a cure for his wife’s illness. His contention was that a relationship between the illness and the cure was hidden within other patient records. He certainly had the skills to find a connection. The author saw the concept of Big Data first-hand without realizing how it would evolve. The author is more convinced than ever that the accountant position is morphing daily. Transaction analysis, variance analysis, and ratio analysis are all being performed by software packages. The accountant role will continue its migration from data creator, manipulator, and archivist to one of data scientist and storyteller. It is imperative that the accountant of today keep pace with technological change and recognize the need to move from historical analysis to predictive change and ultimately, prescriptive change.

It’s against this backdrop that the advent of Big Data and analytics has already affected many organizations and will play a much more significant role in the future.

TOPICS DISCUSSED

•     Evidence-based techniques for finding or generating data, selecting key performance indicators, isolating program effects

•     Relating data to return on investment, financial values, and executive decision-making

•     Data sources including surveys, interviews, customer satisfaction, engagement, and operational data

•     Visualizing and presenting complex results

THE CHAPTERS IN THIS COURSE

•     Chapter 1: What Are Big Data and Analytics?

•     Chapter 2: Big Data History—Big Data Sources and Characteristics

•     Chapter 3: What Are the Trends in Big Data?

•     Chapter 4: What Are the Strategy and Business Applications of Big Data?

•     Chapter 5: Big Data Platforms and Operating Tools

•     Chapter 6: Big Data End User and Accounting Tools

•     Chapter 7: Examples of Big Data

•     Chapter 8: Big Data in the Accounting Department

•     Chapter 9: Ethics and Privacy With Big Data

OPENING DISCUSSION

As you progress through the course, consider the following challenges:

•     What information does your organization currently have access to that is already in your systems and available for use?

•     What information could you have access to that exists in your organization but is not captured?

•     What information exists on the Internet in regards to industry databases or government databases that you could access?

•     What information is available through sensors and machines that could provide insights into the operational and strategic dynamics of your business?

EXERCISE

It would be helpful to spend a couple of minutes to jot down the type of information your organization currently has available. This analysis will be helpful as you consider different applications of analytics and Big Data. Use table Table 0-1 to gather your information.

Before becoming immersed in the overall topic, it may be useful to discuss a broad picture of Big Data so that all participants have an initial understanding. Consider illustration 0-1:

TEUs—Twenty-Foot Equivalent Units

IPS—Industrial Production Statistics

S&P—Standard & Poor’s

NYMEX—New York Mercantile Exchange

MPP—Massively Parallel Processing

Data originates in a variety of places and in a variety of forms. Data from individual companies, organizations, streaming data, and so on accumulate in the cloud, as shown in illustration 0-1. Organizations access the information via in-house servers, laptops, tablets and other mobile devices. Organizations may desire a unique combination of external databases and internal databases, including calculations, projections, and so on. That information is sent to multiple computers with individual processors to analyze the vast amounts of data. These multiple computers with their processors are known as massively parallel processing (MPP).

EXAMPLE

Next, we will consider a simple example of how large amounts of data can be processed to help you create a narrative to supplement your accounting processes. Let us assume that our company is in the lumber industry or is affected by lumber prices. The industrial production statistics are available at www.federalreserve.gov/RELEASES/g17/ipdisk/alltables.txt. The Federal Reserve’s monthly index of industrial production rates covers manufacturing, mining, and electric and gas utilities. The production index measures real output and is expressed as a percentage of real output.

The initial data found on that website looks like that in table 0-2. This raw data is overwhelming and not easy to interpret in its current form.

I selected the IPS data for the NAICS codes B500001 (Total Index Statistics for all industrial production) and G321(Wood Products Industry Statistics) to begin the analysis, as in table 0-3:

Next, the monthly data was obtained for Louisiana-Pacific Corporation (LPX) from Finance.Yahoo.com, and then all three data series were combined utilizing Excel. Once the data series had been established, the graph in illustration 0-2 was generated.

It’s easy to see that in graph form, this data comes to life in a new way. As we supplement our accounting skills with storytelling and data science, is it possible to make some summary conclusions from the data in this example? My interpretation is that:

1. The wood product production statistics seem to follow along a similar trend line as the LPX stock price.
2. The major IPS also appears to follow a similar trend.
3. A supposition could be that a decline in wood production precedes an overall economic decline (note the steep decline which corresponds to the Great Recession.)
4. A supposition could be that wood production statistics during the housing bubble were supported by an increase in LPX stock prices.

By manipulating the data available in just this small example, we were able to uncover trends that could be used to make business predictions. However, most real-life examples of Big Data are much more complex than this. Most use much larger databases and more sophisticated technological tools than Excel. This example was meant to demonstrate the fundamental concept of Big Data so that you can see how powerful it is and help you relate to it.

As the course develops, we will be exploring significantly larger and more varied types of data. The software programs used to interpret Big Data are more complex than Excel, although Microsoft has created a product—Excel BI, which will most likely be the tool that accountants will prefer because of most accountants’ current levels of familiarity with Excel.

By the end of this course, you should be familiar with the sources, types, and trends of Big Data as well as the various tools available for processing and interpreting this information. We’ll take a look at some more examples and learn how you can apply these techniques in your own practice. Are you ready to get started?