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OVERVIEW

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.

Table 0-1 What Information Does Your Organization Have Available?

What Information Do You Have?
Customer Vendor Employee Strategic Operational Other
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
4 4 4 4 4 4
5 5 5 5 5 5
6 6 6 6 6 6
7 7 7 7 7 7
8 8 8 8 8 8
9 9 9 9 9 9
10 10 10 10 10 10

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:

Illustration 0-1

image

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.

Table 0-2

B50001: Total index
“IPS.B50001” 1919 5.0354 4.8128 4.6737 4.7572 4.7850 5.0910 5.3970 5.4805 5.3692 5.3136 5.2301 5.3136
“IPS.B50001” 1920 5.5113 5.8143 5.7030 5.3970 5.5361 5.5918 5.4527 5,4805 5.2657 5.0632 1.6459 4.3677
“IPS.B50001” 1921 4.1173 4.0339 3.9226 3.9226 4.0339 4.0060 3.9782 4.1173 4.1451 4.3955 4.3399 4.3121
“IPS.650001” 1922 4.4790 4.6737 4.9241 4,7572 5.0075 5.2579 5.2579 5.1466 5.4246 5.7309 5.9812 6.14e2
“IP5.B50001” 1923 6.0091 6.0925 6.2873 6.4263 6.5098 6,4542 6.3985 6.2873 6.1481 6.1203 6.1203 5.9812
“IPS.B50001” 1924 6.1203 6.2316 6.1203 5.9256 5.6752 5.4248 5.3414 5.5361 5.7309 5.8700 5.9812 6.1482
“IP5.BS00O1” 1925 6.3429 6.3429 6.3429 6.3985 6.3707 6.3151 6.4320 6.3707 6.2873 6.5376 6.6767 6.7602
“IP5.B50001” 1926 6.6489 6.6489 6.7324 6.7324 6.6767 6.7602 6.7880 6.8715 6.9827 6.9827 6.9549 6.9271
“IPS.B50001” 1927 6.8993 6.9549 7.0384 6.3715 6.9271 6.8993 6.8158 6.8153 6.7045 6.5654 6.5654 6.5933
“IPS.B50001” 1928 6.7324 6.7ES0 5.B436 6.8158 6.8993 6.9549 7.0384 7.1775 7.2331 7.3722 7.5113 7.6S04
“IPS.BS0001” 1929 7.7617 7.7339 7.7617 7.9008 S.0399 8.0955 8.2068 8.1234 8.0677 7.9286 7.5391 7.2053
"IPS-BSOOOl™ 1930 7.2053 7.1775 7.0662 7.0106 6.3993 6.7045 6.3935 6.2594 6.1432 5.9812 5.3421 5.7030
“IP5.B50001” 1931 5.6752 5.7030 5.8143 5.8421 5.7587 5.6196 5.5361 5.3414 5.0910 4.8963 4.8406 4.8128
“IPS.B50001” 1932 4.6737 4.5624 4.5068 4.2008 4.0617 3.9226 3.8113 3.9226 4.1730 4.3121 4.3121 4.2266
"IP5.B50001™ 1933 4.1451 4.1730 3.9226 4.2008 4.3963 5.6474 6.1760 5.9256 5.5916 5.3136 5.0075 5.0354
“IP5.B50001” 1934 5.2023 5.4527 5.7030 5.7030 5.8143 5.7030 5.3136 5.2579 4.9519 5.1745 5.2301 5.5639
“IPS.B50001” 1935 6.0091 6.1203 6.0925 5.9812 5.9812 6.0647 6.0647 6.2872 6.4542 6.6489 6.7880 6.8715
“IP5.B50001” 1936 6.7602 6.5933 6.6767 7.0940 7.2331 7.3722 7.5113 7.6226 7.7617 7.8730 8.0955 6.3459
“IPS.B50001” 1937 8.3181 8.4294 8.6241 8.6241 8.6519 8.5406 8.5963 8.5406 8.2624 7.6504 6.8993 6.2872
“XPS.B50001” 1938 6.14B1 6.0925 6.0925 5.9812 5.8421 5.8978 6.2316 6.5654 6.7602 6.9271 7.2053 7-2868
“IPS.B50001” 1939 7.2686 7.3444 7.3722 7.3444 7.3166 7,4835 7.7061 7,8173 8.2903 8.7076 8.9301 8.9301
“IPS.B50001” 1940 8.8188 8.5406 8.3459 8.5128 8.7632 9.0414 9.1527 9.2083 9.4031 9.5421 9.7647 10.0985

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:

Table 0-3  

IPS.B50001
2000
94.7615
95.0664
95.4649
96.1873
96.3963
96.488
96.386
96.0455
96.4354
96.1096
96.1276
95.8441
IPS.B50001
2001
95.1747
94.5581
94.2954
94.0294
93.3526
92.7435
92.249
92.05
91.7355
91.3042
90.8141
90.8243
IPS.B50001
2002
91.403
91.402
92.1252
92.5335
92.9145
93.8071
93.5978
93.6123
93.7211
93.4327
93.9229
93.4505
IPS.B50001
2003
94.0868
94.3655
94.1543
93.444
93.4875
93.5856
94.0002
93.8015
94.3862
94.4645
95.2175
95.1269
IPS.B50001
2004
95.3012
95.8359
95.3155
95.6825
96.4212
95.6445
96.3852
96.445
96.5044
97.4167
97.6225
98.3132
IPS.B50001
2005
98.7886
99.4359
99.3247
99.4595
99.6415
100.0262
99.7447
99.9372
98.0478
99.3189
100.2981
100.8948
IPS.B50001
2006
101.0256
100.9981
101.2658
101.6644
101.5254
101.9173
101.8981
102.252
102.1093
102.0843
101.9683
102.9751
IPS.B50001
2007
102.4954
103.6101
103.7575
104.5126
104.564
104.5525
104.5442
104.7213
105.0936
104.537
105.1581
105.1322
IPS.B50001
2008
104.8595
104.5926
104.2989
103.53
103.0519
102.8476
102.3093
100.7832
96.4822
97.3647
96.1487
93.3954
IPS.B50001
2009
91.2227
90.6371
89.2352
88.4857
87.5811
87.214
88.1641
89.119
89.7943
90.1731
90.4311
90.7512
IPS.B50001
2010
91.8162
92.0991
92.7191
93.0609
94.4844
94.6828
95.1542
95.4866
95.715
95.4803
95.518
96.3941
IPS.B50001
2011
96.3665
95.8776
96.6955
96.2901
96.5438
96.7612
97.1711
97.7699
97.7762
98.4296
98.2829
98.7841
IPS.B50001
2012
99.5096
99.7389
99.0887
99.928
100.0508
99.9691
100.2736
99.833
99.9189
100.1878
100.6435
100.858
IPS.B50001
2013
100.933
101.3425
101.561
101.5385
101.4689
101.6621
101.2685
102.0442
102.6361
102.6534
102.9163
103.1889
IPS.B50001
2014
103.0047
103.8079
104.6615
104.8595
105.2461
105.716
106.0803
106.1138
106.6776
106.8463
107.7996
107.9108
IPS.B50001
2015
107.6003
107.4368
107.2374
107.0599
106.6799
106.6628
107.4746
107.4973
107.5331
107.4001
106.5811
105.8689
IPS.B50001
2016
106.8477
IPS.G321
2000
143.972
143.4722
143.9109
143.2324
140.9417
138.436
137.5802
135.2912
136.5014
133.6298
133.283
129.321
IPS.G321
2001
126.5031
125.8307
128.3142
128.6762
129.9331
130.9957
129.5036
131.7801
132.8763
129.2955
130.1209
131.9857
IPS.G321
2002
133.3057
132.8523
135.8989
135.6197
135.6325
137.4395
135.6227
136.501
135.7389
135.9186
134.1315
133.5371
IPS.G321
2003
134.3941
134.3887
132.4979
132.858
132.6237
133.6178
134.9538
134.4597
135.2376
136.3176
140.5582
137.6886
IPS.G321
2004
137.9976
138.0842
136.9454
138.3816
138.9288
136.5499
138.691
138.9603
136.4546
140.4624
140.2499
139.8354
IPS.G321
2005
146.1386
142.86
142.2799
142.6249
143.0869
143.4326
144.3807
144.0517
149.3065
156.8229
158.6932
158.6398
IPS.G321
2006
158.764
155.9862
155.0613
152.576
151.3601
149.1037
149.5498
147.233
145.8068
139.3438
138.9545
142.9898
IPS.G321
2007
140.0055
139.8647
141.9422
139.9414
141.1664
142.9497
142.5319
139.8908
138.6741
135.9987
133.9249
134.3439
IPS.G321
2008
131.3716
128.5247
127.1709
124.6709
123.2955
122.3217
121.8669
119.398
115.8094
109.9483
106.6985
96.6497
IPS.G321
2009
93.6668
93.3626
89.9492
88.3143
87.4935
90.724
90.5585
92.0024
92.1858
90.6401
91.2439
90.7279
IPS.G321
2010
92.6534
91.2404
92.4849
96.417
97.872
95.2164
94.6381
93.8519
92.8365
93.9712
94.0948
94.4015
IPS.G321
2011
94.1239
93.5283
95.8471
93.7046
94.7942
93.4226
93.6132
92.5363
94.9918
94.7417
94.766
95.8602
IPS.G321
2012
98.0903
97.4725
98.2069
99.7776
101.1851
99.1884
99.5748
99.8404
98.921
100.6659
103.2503
103.8267
IPS.G321
2013
104.9279
106.5673
105.4279
103.602
104.0396
103.9955
104.0478
105.6103
107.0795
107.7638
109.0048
107.3474
IPS.G321
2014
105.0321
104.9346
107.1353
107.6058
109.3083
109.5686
111.385
112.1464
111.1789
112.4458
112.0506
113.368
IPS.G321
2015
111.1515
110.2591
109.7365
109.574
108.836
108.8611
111.2143
111.6601
112.5653
113.3287
112.8498
114.869
IPS.G321
2016
116.2468

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.

Illustration 0-2

image

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?