Download Data Fracking Strategy: Why Management Accountants Need It

Data Fracking Strategy:
Why Management
Accountants Need It
By Glen L. Gray, Ph.D., CPA, and Michael Alles, Ph.D.
EXECUTIVE SUMMARY
In the era of Big Data, management
accountants who want to move into
more strategic, future-facing roles
in their organizations must go beyond
traditional accounting data. Data
fracking is a strategy that uses
expanded data sources and advanced
data analytics to achieve that goal.
M A N A G E M E N T A C C O U N T I N G Q U A R T E R LY
n its mission statement, IMA® (Institute of Management
Accountants) lists broad roles and responsibilities for
management accountants:
◆ Managing functions that are critical to business
performance,
◆ Supporting organizational management and strategic
development,
◆ Providing accurate and insightful information for better
decisions,
◆ Ensuring that organizations operate with integrity and
proper governance,
◆ Planning for the long-term and helping to ensure sustainability, and
◆ Safeguarding the interests of the organization and its key
stakeholders.1
For this broad set of responsibilities, competence using
cutting-edge data analytics is essential. Management accountants need forward-looking data analytics to fully evaluate the
potential benefits and consequences of alternative actions and
decisions.
In 2004, Richard Landry, Roger Debreceny, and Glen L.
Gray wrote about the importance of data mining for management accountants, stating, “Data mining can help you better
understand your business by providing new insights into areas
such as customer buying and return patterns, product prof-
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itability, instances of general, selling and administrative
(GS&A) expenses, and fraud detection.”2 That notion
is still true today although the terminology used to describe data mining has changed over the years. It has
been called business intelligence (BI), data analytics,
predictive analytics, and prescriptive analytics.3
Whatever it is called, the best new data analytics go far
beyond traditional accounting data.
ities. The potential Big Data universe available for data
analytics can include things like Global Positioning
System (GPS) data, streaming data, social media data,
and even website visitor data.
Some of those items may seem far afield from traditional costing data, but subtle changes in e-mail volume
or the tone on social media can be highly correlated
predictors of future cost-related problems. For example,
if a company has a generic support e-mail address, more
e-mail sent to the address following a product launch
may indicate that customers are encountering more
problems with the new product than is normal.
Alternately, if the number of complaints increases or the
tone on social media turns negative, a new product
could be having problems.
Whether those issues are real or are only perceived as
problems, the publicity will still be negative. The company may have to lower its projections of future sales,
requiring adjustments to how fixed costs are allocated
to units and how capitalized costs are amortized. Those
negative activities could also mean that reserves for returns and warranty cost on the financial statements are
understated and must be adjusted upward. These leading indicators of potential issues and the future impact
on cost could be reported to management long before
these problems come to their notice when the financial
statements are published.
With all the hype about Big Data, most of the data an
organization collects could be classified as “dark data.”
The technology research and advisory company Gartner
defines dark data as “the information assets organizations collect, process and store during regular business
activities, but generally fail to use for other purposes
(for example, analytics, business relationships, and direct monetizing).”5 Consequently, it is a value-added
activity for management accountants to develop insights from their organization’s dark data. A joint paper
about Big Data by IMA and the Association of
Chartered Certified Accountants (ACCA) states, “As
more companies and industries make use of Big Data,
its competitive advantage will diminish. There will
probably be a race to unlock value from ‘dark data.’”6
Understanding Big Data
In 2012, B. Douglas Clinton and Larry R. White reported
that management accountants do not seem eager to
adopt new data analytic technologies given the demands
on modern organizations.4 Their survey found that management accountants had not moved beyond relying on
Excel spreadsheets, and the reason was not because of
financial constraints but was actually more about motivation. A disconcerting finding was that decision-making
management put a rather low priority on improving cost
reporting but a high priority on cost reduction.
Clinton and White question whether the management accountant is a significant contributor to the costreduction effort. Management views management accountants as reporters of historical cost information, not
as advisors to identify ways to reduce those costs. Yet
the same growing pressures for companies to stay relevant in the marketplace are pushing management accountants to stay relevant in their organizations. As
technology becomes more sophisticated, it is commoditized and replaces traditional management accounting
activities. Therefore, management accountants must
evolve as technology evolves.
Today that evolution includes Big Data and data
analytics, which are becoming increasingly relevant to
management accountants. Big Data is one of today’s de
rigueur terms in the expanding data universe. “Big”
means that the databases holding the data are too large
for anyone to analyze efficiently or effectively using traditional tools like Microsoft Excel. The data side of Big
Data includes an ever-expanding variety of traditional
and nontraditional data types, frequently unstructured,
that can change rapidly. The amount of data now involved in areas such as enterprise resource planning
(ERP), customer relationship management (CRM), and
the Web has led to a need for more data storage capabil-
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Analytics: Predictive or Prescriptive?
Despite all its publicity, Big Data is only data and a
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means to an end—not an end itself. As Paul C.
Zikopoulos, et al., say, “The term Big Data is a bit of a
misnomer….Big Data is all about better analytics on a
broader spectrum of data, and therefore represents an
opportunity to create even more differentiation among
industry peers.”7 Big Data’s real value comes from the
ever-evolving analytics we perform on that data.
Analytics specialist Tarisai Masamvu says, “As organizations move from descriptive [data] analytics, which
merely describe what happened, to more forwardlooking predictive analytics and prescriptive analytics,
more significant business benefits will begin to be realized.”8 And Pavel Năstase and Dragoş Stoica say predictive analytics “helps shift from a ‘sense and response’
focus to a forward-looking ‘predict and act’ focus.”9
One of the goals of predictive analytics is to develop
predictive models and semantically oriented knowledge
about business processes. Once a financial executive
develops these models, he or she can perform a variety
of what-if simulations to analyze how modifying some
variables will impact future outcomes. In contrast, prescriptive analytics construct optimization models. With
the predictive model, the management accountant
could ask, “If we increase prices of our consumer products by 2%, what will be the resulting value of our
sales?” With the prescriptive model, however, the question is, “How much should we change our consumer
product prices in order to maximize our sales revenue?”
Consider a company that bottles and sells lemonade.
What factors will determine the forecast for lemonade
sales in the coming year? Initially, accountants must
forecast unit demand, a function of many variables. The
expected selling price is foremost among them. But that
is only determinative with respect to the company’s
manufacturing and distribution costs, the incomes of its
customers, and the prices of the competitors’ lemonade.
Also, lemonade is not the only beverage a customer
might purchase, so management accountants must also
know the alternatives and their selling prices. Apart
from forecasts of next year’s numbers, management accountants also need to factor in longer-term forces like
the shift away from carbonated beverages toward
healthier drinks. That might help the company if it can
position its lemonade as a natural fruit juice instead of a
sweetened drink.
Demand, highest during a hot summer, is also linked
to the weather. Local weather for the company’s lemon
and sweetener suppliers is important since the company’s and its competitors’ lemonade prices must reflect
raw material costs. Management accountants must perform the same analysis for other raw materials, such as
container paper or glass, water, food coloring, and labor.
There also are the costs of utilities for the factories and
diesel fuel for the delivery vehicles. The company must
also understand who its customers are and how to get
access to them.
A good sales forecast reflects the accountant’s understanding of how customers’ behavior changes with respect to numerous forces that determine when they will
want to drink lemonade, which brand they will prefer,
where they purchase it, and the price they are willing to
pay. The same kind of analysis must be conducted for
the cost side of the budget—the cost of raw materials as
well as the efficiency of their use and the utilization of
overhead. Other factors might include customer concern about using high fructose corn syrup sweetener,
causing a switch to cane sugar or beet sugar instead,
which involves different prices. The company also must
monitor social media, gauging customer opinion change
about its lemonade and actively trying to manage social
media discussions about its products. Finally, there are
also long-range issues, including whether to launch new
products containing artificial sweeteners and the associ-
Leveraging the Technology
Organizations are leveraging data analytic technologies
to be an integral, sometimes indispensable part of decision making. Analytics are becoming routine in sales,
marketing, and supply chain departments—another impetus for management accountants to improve their
data analytics competency, be proactive in using analytics, and be the catalyst for change. Financial executives
must also be active team members in other departments using these technologies.
Consider budgeting, one of management’s most important activities. If the underlying predictions and assumptions are faulty, the outcome is a flawed forecast
no matter how carefully management carries out the remaining tasks. That is particularly true of the sales forecast, which often drives the entire budget.
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ated costs and uncertainty with that decision.
If this simple, one-product example can rapidly become so complicated, imagine how difficult sales forecasting is at a company like GE that has thousands of
products! Clearly the only way management accountants can deal with today’s increasingly complex, highly
competitive, globalized market is by expanding the variety of data they use and how they analyze that data.
The ever-increasing critical importance of data in modern business demands it.
of the differentiating drivers so that actions—or
interventions—can be made to achieve a high payback
profit increase from customers.”12
Bridging the Talent Gap
The use of data analytics has been growing quickly.
Forbes stated in 2014 that “the market for Big Data will
reach $16.1 billion in 2014, growing six times faster than
the overall information technology (IT) market,” and
Gartner forecasts that spending on BI will hit $17.1 billion by 2016.13
In terms of getting access to BI, management accountants often can piggyback on their company’s investments in ERP systems and data analytics. But more
fundamentally, management accountants are not taught
modern data analysis techniques. While introductory
management accounting texts have a chapter on determining how costs behave, the section on regression
analysis typically is relegated to an appendix. The chapter also might offer a discussion of the high-low
method, which implies that accountants cannot deal
with more than two pieces of data at once and that they
only have access to a pocket calculator.
The 2014 IMA Annual Report calls this need to upgrade skills a professional talent gap and a competency
crisis.14 But none of these valuable data analytic concepts comes from the world of accounting itself. Thus,
it is too easy for accountants to believe that data analytics tools are really just for statisticians or that predictive
analytics are only for managers.
Management accountants need to have a way of
thinking about data that is uniquely theirs. It must direct them toward data analytics technologies but also
motivate them to go outside their comfort zones in considering which data they analyze in the first place. That
is why we introduce a new term that management accountants can make all their own: data fracking.
A New Data Flood Is Coming
Technology watchers predict an even greater flood of
business data soon. It will come from the Internet of
Things (IoT)—a network of intelligently connected devices, machines, and physical objects with sensors that
will produce waves of data across the entire business
value chain. This will occur not just for manufacturing
but also in supply chains and retail distribution.
Accenture estimates that it could add more than $10 trillion to the global economy by 2030!10 Imagine doing
activity-based costing (ABC) with that amount of data.
Most important, the latency time available to analyze
data—or time to value (TtV)—is rapidly shrinking, and
companies must become increasingly agile. Companies
today are on a never-ending treadmill. They don’t have
the luxury of waiting to see how markets evolve. Just
ask the management at BlackBerry, Myspace, Circuit
City, or Borders Books about today’s diminishing leeway for adjusting strategies. Sony and Microsoft could
not have imagined how Apple—on the verge of bankruptcy in 1997 with only 2% to 3% of the desktop computer business—could possibly match their renowned
Walkman and Windows brands using gimmicks like a
digital music player (the iPod) or a computer in a phone
(the iPhone).
Today’s management accountants must quickly
identify what internal and external data their company
should collect, how to analyze it, and what action plan
the analysis demands. As Peter Trkman, et al., say,
“The purpose of business analytics has always been to
provide users with timely, reliable information they
can use to make the right decision sooner and more
consistently.”11 Similarly, Gary Cokins states, “The
goal [of data analytics] is to accelerate the identification
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Data Fracking
In gas or oil mining, traditional extraction methods involve drilling a hole and pumping out the gas or oil. In
fracking, the rock is fractured, and pressurized water
with sand and chemicals is pumped in. Then drillers
extract the oil or gas. Fracking has led to a revolution in
the industry and made the United States the largest oil
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and gas producer in the world. Management accountants must emulate the strategic mind-set of the frackers, who went beyond the standard practices of their industry to extract something valuable.
Data fracking can give management accountants
both the tools and the motivation to use expanded
sources of data and advanced data analytics, adding
value and fulfilling their broad roles and responsibilities
as defined by IMA. The key to a successful data fracking strategy is to actively find the most decision-specific
data to analyze, rather than waiting for the data to become available as accounting data and then apply analytics to it. It also involves going beyond traditional accounting data by including nontraditional internal or
external data. Like oil and gas fracking, management
accountants will only get back as much as they put into
it in terms of expanding their Big Data and data analytics activities.
But data fracking is only a means to an end. Users
apply technology to create value from underlying resources. No one should get overly enamored with any
particular technology. What technology journalist Jeff
Bertolucci said about Big Data applies to trendy data
analytic tools: “These ill-defined objectives are the
primary cause of the majority of Big Data failures.
Generally, it is a disconnect between the output and a
clearly defined business driver or goal….And along the
way, people get engulfed in the technology.”15
The danger of that is clear: Bertolucci emphasizes
that, even in 2105, nearly eight out of 10 organizations
have Big Data projects under way—but only 27% describe their efforts as successful, and a scant 8% said
they were very successful. To avoid that, make sure
the decision objective—not any particular analytic
technology—guides the choice of data and the way data
is analyzed. Focus on identifying and capturing events
that affect the company in both the short and long term.
ket baskets from 25 Osco Drug stores, a team of
Teradata mining consultants discovered a high correlation between the purchase of beer and diapers on
Thursdays and Saturdays—and specifically on
Thursdays between 5 p.m. and 7 p.m. The purchasers
were fathers who came to the store to buy diapers and
rewarded themselves by buying some beer for the
weekend. From knowing this relationship, the data analytic experts suggested that Osco Drugs move the diaper and beer displays closer together and never discount beer on Thursdays.16
The second example involves Physicians Mutual
Insurance Company (PMIC), which provides a wide
range of supplemental insurance for individuals. In
2011, PMIC was concerned about its direct response
TV (DRTV) programs. DRTV programs are commercials where the audience is asked to respond by calling
a phone number or visiting the company’s website. In
addition to DRTV, PMIC used a mix of direct mail, radio, online, agency, and telephone sales.
PMIC did not know how to optimize the synergy of
its multipronged direct marketing approach. Also, developing schedules for the call centers to respond to
these campaigns was very challenging. A key measure
for these campaigns is the response rate of those who
were exposed to the marketing campaigns. But since
PMIC pursued several of these approaches at the same
time, it could not specifically determine what each part
of the campaign contributed to the response rate.
Another factor affecting the response rate involved
whether PMIC was directing these campaigns to the
appropriate group of consumers—those people most
likely to respond to the campaigns. By manipulating the
elements of the campaign (for example, using just direct mailing in one market), PMIC was able to determine how to identify the most appropriate audience
and the mix of the various campaign elements.
Because this study was so successful, PMIC expanded its DRTV from limited local TV markets to national TV markets. Interestingly, PMIC also discovered
that advertising during “The Price Is Right” TV show
was the most productive ad for its DRTV campaign.
PMIC increased its DRTV spending during its two-year
study by a factor of eight, and the number of people responding to the campaigns increased by a factor of 14.
Examples of Data Fracking
Here are two examples of successful data analytics that
used the kind of data fracking strategy we are suggesting. In both these examples, the companies and their
managers moved way beyond traditional accounting
data.
In 1992, after analyzing the goods in 1.2 million mar-
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it can be analyzed using various decision-making tools
to produce the knowledge that forms the basis for the
optimal decision to be determined. Critically, moving
up the information value chain results in higher and
higher value-added activities.
Elliott’s model does not explicitly refer to data analysis, only to data refining and combining. The recording
of data specifically means the way accountants have traditionally recorded business transactions in ledgers as
debits and credits. There is a mention of “information
from the outside and so forth,” but that is clearly only
meant to supplement the accounting data. The model
also goes in only one direction, from business events
and accounting data to decision making. It does not
consider that the nature of the decision being made
may affect the data needed to make the decision. Thus,
the earlier lemonade example started with the problem
that the management accountant faced—develop an accurate sales forecast—and then proceeded to introduce
numerous factors that will influence next year’s sales.
What we did not do in the example was look at the
Overall, PMIC decreased its cost per lead by 35%. By
better understanding the responses to its campaigns,
PMIC was also able to better optimize staffing the call
centers.17
The Elliott Value Chain
To put the concept of data fracking into a broader perspective, consider the well-known model illustrating accounting information’s value chain (see Figure 1). It
was developed by Robert (Bob) Elliott, former chairman of the American Institute of CPAs (AICPA) and
partner at KPMG. At the left end of Elliott’s information value chain, business events and transactions are
taking place. But they have to be recorded first before
they enter the value chain. While that happens instantaneously in an ERP system, recording can be delayed
if there are still manual steps involved. The key step in
the transformation of data into knowledge is the application of training by the management accountant to determine what data is relevant to the decision problem at
hand and what is not. Once the information is gathered,
Figure 1: Elliott’s Information Value Chain
Overlaying the vision
Co
m
m
pi un
ct ic
ur a
e tin
g
Information Knowledge
Decisions
op C
po rea
rt tin
un g
iti
es
Tr
an
sf
o
to rm
re in
al g
ity vi
si
on
Data
Tr
an
sl
to atin
kn g
ow inf
le orm
dg a
e tio
n
Business
events
Source: Lecture by Robert K. Elliott, “Accounting in the 21st Century,” November 2, 1998, Newman Library Digital Collections, Baruch
College, CUNY, New York, N.Y., http://newman.baruch.cuny.edu/digital/saxe/saxe_1998/elliott_98.htm.
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data already available and then refine and combine it to
get next year’s sales forecast. That is the approach that
leads to the “take last year’s sales and add 2%”
approach.
Data fracking starts by identifying the decision the
management accountant faces. Next is finding the data
that is needed to make that decision. Data fracking relates to the three middle pieces of Elliott’s model—
those concerning the transformation of data into information and knowledge.
Management accountants should use whatever technologies are needed to extract value from that data, obtaining decision-specific information in a cost-efficient
way and being realistic about the costs of data extraction. Some techniques are very costly to implement and
require highly trained personnel, but users can be smart
about data analysis tools without reverting to the highlow method.
In February 2015, PwC released a paper that called
for data analysis to become central in the training of
new accountants. The paper noted, “Regardless of size,
the real power of data comes from the more informed
decisions it enables executives to make. Making big decisions better, faster, and cheaper is the next competitive advantage that organizations seek.…Company leaders told us the biggest barriers to using more data
analytics in decision making include low data accuracy
and quality; difficulties knowing what data is useful;
and senior management lacking the skills needed to
work with data.”18
The PwC study also found that business leaders use
a mix of intuition, advice from others, and data analytics
to make major decisions. That “advice from others”
component can mean trouble for management accountants. When profits are at stake, management always
values those who help improve the decision-making
process. If management accountants will not do that or
are unable to, others will be very happy to take their
place. A data fracking strategy will not guarantee job security for financial executives, but it is smart and
proactive.
Figure 2: Data Fracking Framework
Events
Data
Capture
Opportunities
& Threats
Long Term
Data
Available
Decision
Problems
Short Term
Short Term
Data
Filter
Choice of
Analytics
Short Term
Data
Used
Data
Choice
Data
Analysis
DecisionRelevant
Information
Data Fracking Framework
DecisionMaking Model
Intuition
Figure 2 illustrates a framework for the data fracking
approach to decision making. It begins with general
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Advice
from Others
events. Recalling the lemonade company example, nonbusiness events like weather can impact sales forecasts.
People can measure these events and record them as
data. Some data will be recorded internally by the business, and some by other parties outside the business—
for example, the weather service, the census bureau, or
customer comments on social media websites.
The events represent opportunities and threats that
give rise to subsequent decisions that management
must address. To address those decisions, managers or
management accountants have to decide on the most
appropriate data and analytics to use. Those two decisions are interrelated—different analytic choices can result in different data choices and vice versa. For example, choosing to adopt a process mining approach
requires the construction of an event log from the ERP
system, while using Big Data analytics necessitates the
acquisition of a wide range of financial and nonfinancial
data from inside and outside the business.
The choice of analytics and data depends on what
data is immediately available and what other internal
and external data the management accountant can get
within the decision-making time frame. This is somewhat a cost/benefit issue. But the management accountant’s willingness to obtain data beyond traditional accounting records is a greater constraint. An old saying
reminds us that if all you have is a hammer, every problem looks like a nail. Many management accountants
assume that every problem is a nail, and they reach for
their traditional tools and data regardless of the problem
at hand. To change, they must realize that they now
face problems that are broader, more complex, and require a shift in mind-set, not just methodologies.
The amount of time available is also important. If
management must make a decision quickly, the only
choice is to use data currently available. But in the
longer run, the data fracking strategy encourages management accountants to take ownership of the datacapture process and ensure that they have the data necessary for their chosen analytic choice. Even in the case
of Big Data, where there already is an overwhelming diversity of data, there is still the possibility that additional data will be needed to address a specific decision.
Because some data exists only for a short time, management accountants must make sure that the data they
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want is captured when it is available. For example,
time-driven activity-based costing requires knowledge
of the length of processes. Accountants may not have
gathered this information in the past because they saw
no need for it, but they can establish measurement devices to capture that information going forward.
A data filtering stage is needed if the amount of data
available exceeds what is required to perform the chosen analytics. The accountant must verify the integrity
of the data and its decision relevance and impose
cost/benefit criteria on the timing and aggregation level
used. IT’s increasing power and decreasing cost means
accountants no longer need to settle for data sampling;
they can use whole data populations instead, reducing
the amount of filtering they do. Unfortunately, including too many data elements can lead to numerous false
positives and spurious relationships that will take significant time to investigate.
Working interactively with the chosen analytic
method and available data, the accountant must decide
which data is likely to be particularly decision relevant
and which will simply add noise. Analytic tools such as
t-statistics can help this data selection process. It is important not to exclude a source of data as irrelevant or
impractical too quickly, however, because the objective
of data fracking is to gain value from data previously
considered unusable.
The data filtering stage yields the data used in the
data analysis, which in turn produces the decisionrelevant information with which a decision can be
made. Other inputs into the decision-making model are
the intuition of the decision makers as well as information from others involved with the process. Finally, the
decision made gives rise to the events affecting the
business, which begins the cycle again.
This data fracking framework differs from Elliott’s in
the framework’s emphasis on the role of choice as to
what data and analytics the management accountant
adopts. That data choice has to be consistent with the
data available, which is not always a given in the long
run. Management accountants need to take control of
the data capture stage to the extent that they can in
order to ensure that they have access to the widest
range of metrics regarding the events that impact the
business.
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Figure 3: Paths to Expand Data Analytics in Management Accounting
Data Analysis/Mining Techniques
Traditional (Excel)
Extended
Data
Traditional
Today
B
Sources
Extended
A
C
analytics on social media is also critical to understanding
customers. Management accountants also could be using interrogation tools and standardized reports produced by ERP vendor software packages. Because of
the Sarbanes-Oxley Act of 2002 (SOX) and other compliance requirements, customer demand for reports has
significantly increased, and ERP vendors have greatly
expanded the content and number of reports they offer.
We do not expect that management accountants will
become data frackers overnight, but they should move
deeper into using that strategy on a continuousimprovement basis. Commitment to the data fracking
strategy is the force that would move management accountants from one cell to another.
As management accountants begin to expand their
activities, cell A is probably the easiest to move toward.
It is incremental and involves taking small steps in applying familiar tools to explore including more nonfinancial data in spreadsheets. Cell B and cell C require
learning new technology skills and developing a much
better semantic understanding of how all the components of the business processes affect and interact with
each other.
For management accountants and others new to data
analytics, “Frequently Asked Questions about Data
Analytics” provides answers to some commonly asked
questions, and “Resources for Improving Your Data
Analytic Skills” offers a plan and some resources to increase knowledge and expertise.
Where to Go from Here
Figure 3 illustrates the broad areas that management
accountants could move into to expand their valueadded activities and improve their relevance to their
organizations.
The “Today” cell in Figure 3 represents management accountants using Excel to analyze primarily
financial accounting data. In the movement to cell A,
management accountants expand the universe of data
they include in their analysis using traditional tools (for
example, Excel). Big Data can provide dark data that
will keep management accountants busy for a long
time.
Management accountants could next move to cell B,
where they explore additional data analytic tools and
techniques that can be applied to data they traditionally
use. We characterize most of the data analysis performed by management accountants today as data extraction and descriptive statistics using tools such as
Excel. In the short term, management accountants
could explore using more inferential statistics (for example, time series and regression analysis). In the
longer term, management accountants should expand to
use a variety of data analytic techniques, eventually
moving to predictive and prescriptive analytics.
Cell C includes both expanding the universe of data
and employing additional data analytic techniques. This
could include data mining e-mails and other text data
using text-based data mining tools. Performing various
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Frequently Asked Questions about Data Analytics
We do not expect that management accountants will become data frackers overnight, but we are suggesting that management accountants move deeper into data fracking on a continuous-improvement basis. For those new to data analytics, here
are answers to some common questions.
Q: What are the most effective Big Data or dark data groups for data fracking?
A: First figure out what data the company already has and is not looking at. That is the real dark data because it is both specific to the business and already available for free. Make a list of all the data the company collects: warranty registration
cards; customer feedback responses; daily and weekly sales figures; and social media comments on company Facebook,
Twitter, LinkedIn, or other services. Then search using terms specific to the decision problem at hand.
For example, if the company wants to decide if it should make a product in a smaller size that offers users more convenience, search the social media comments for terms like “too large” or “inconvenient.” Check sales figures to see what demographic groups seem to prefer smaller sizes of competitors’ products. There are also companies that now aggregate and resell data that management accountants should be aware of (for example, gnip.com for Twitter data and datasift.com for
Facebook data).
Q: What data is most underutilized? What gems are neglected?
A: The more specific the data is to your company, the more important it is. Comments left on social media sites provide a
wealth of feedback about what customers really want, their gripes, and suggestions about how your company can better
meet their needs. What do they say they want? Which new products elicited only lukewarm responses? Are they using your
product in unexpected ways? Would a different size or packaging improve their unexpected use?
Other neglected gems might be comments on warranty registration cards or comments after an item is repaired. But the
data must be searchable using the terms you use to talk about the problem you are trying to solve. Of course, none of this
data will be utilized unless management accountants go beyond their comfort zone of only looking at traditional financial data.
Q: What kind of data might be a waste of time to look at?
A: The best kinds of data that management accountants should seek are those that predict future problems or identify unexpected opportunities, such as a hot new trend. Less relevant are more of the financial data that they are comfortable with because a better understanding of the past is only relevant if one expects that it can be used to predict an unchanged future—
and today’s dynamic business environment change is too constant for that assumption to hold. Most important of all, get
outside your comfort zone. Make it a point every quarter to include a data set that you have not used before.
Q: What common data fracking mistakes might management accountants make? How do they avoid them?
A: The most obvious pitfall that management accountants face with data fracking is staying in the cell labeled “Today” in
Figure 3—reducing the decision problem to the data that they are comfortable with. Even decisions firmly within the accounting environment, such as costing, cannot be done accurately without the use of nonfinancial information, as the time-driven
activity-based costing movement shows.
Q: What data would most CFOs or controllers like to have that can easily be found through using data fracking techniques?
A: CFOs and controllers must ask themselves which parts of their strategy are working and where the danger signs are. The
balanced scorecard is a well-known tool used by management accountants to develop and implement strategy. It already
uses nonfinancial data, and it can become an even better tool by extending that to Big Data through data fracking. For example, Wells Fargo had great difficulty in linking its customer satisfaction measures to branch profitability because even customers happy with their local branch tended to shift to another bank that had more convenient ATMs or a better location relative to home or work. Studying social media such as Yelp.com would have helped the bank listen in on the conversations
customers have about their banking needs and get a better measure of what motivates their behavior.
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Resources for Improving Your Data Analytic Skills
The first step in data fracking is to develop a broader vision of the firm’s strategy and how nonfinancial data fits into its mapping and implementation. The best way to do that is with the balanced scorecard. As a management accountant, you should
already be familiar with it, but if not, pick up any of the books by its creators, Robert Kaplan and David Norton, such as the
one listed below.
The next step is to learn the rudiments of data analytics. Even if you turn to a specialist to actually run the regression and
tests, it is important to know the basics of how data is analyzed, how to test data for reliability, and how much weight to
place on the results. The second book in the list is an excellent, comprehensive, and inexpensive textbook-like introduction to
data mining. The third book, a good IBM introduction to Big Data, is available free from the IBM website.
In addition, searching the Internet using terms such as “Big Data tutorials,” “Big Data overview,” and “Big Data blogs” will
result in a wide variety of resources. Because Big Data and data analytics are intertwined, those search terms will provide resources for both Big Data infrastructure and data analytics. Searching for “data analytic tutorials” also will result in additional
resources.
One useful Big Data search result is IBM’s Big Data and Analytics Hub. It includes Big Data blogs and a wide variety of reports and other publications. As with any vendor, IBM publications do discuss IBM’s products, but the general discussions
are very informative. To varying degrees, other Big Data and data analytics vendors have similar resources.
YouTube.com is another excellent resource for presentations on the tools of data fracking. For example, the website listed
from Rutgers University hosts a large number of short video presentations walking you through various data analytic
techniques—including an entire series called “Special Topics in Audit Analytics.”
For those seeking formal continuing professional education (CPE) training, there are numerous organizations that offer
courses in data analytics. For example, the Center for Professional Education, Inc. offers a CPE seminar on using Big Data
analytics to increase profits.
Finally, marketing research organizations such as Forrester maintain Big Data blogs that provide updates on Big Data vendors and the marketplace as well as Big Data tips and techniques.
Books
Robert S. Kaplan and David P. Norton, Alignment: Using the Balanced Scorecard to Create Corporate Synergies, Harvard
Business Review Press, Boston, Mass., 2006.
Ian H. Witten, Eibe Frank, and Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition,
Morgan Kaufmann Publishers, Burlington, Mass., 2011.
Paul C. Zikopoulos, Dirk deRoos, Krishnan Parasuraman, Thomas Deutsch, David Corrigan, and James Giles,
Harness the Power of Big Data: The IBM Big Data Platform, McGraw-Hill, New York, N.Y., 2013,
www-01.ibm.com/software/de/big-data/pdf/assets/Harness.PDF.
Websites
IBM’s Big Data and Analytics Hub: www.ibmbigdatahub.com/blogs
Rutgers University has a website hosting videos on data analytic techniques, including the series called “Special Topics in
Audit Analytics”: www.youtube.com/user/rutgersweb
Formal Continuing Professional Education (CPE) training: www.cpeonline.com/seminar/seminar/using-big-data-analytics-toincrease-profits-56
Forrester Big Data Blog: http://blogs.forrester.com/category/big_data
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7 Paul C. Zikopoulos, Dirk deRoos, Krishnan Parasuraman,
Thomas Deutsch, David Corrigan, and James Giles, Harness
the Power of Big Data: The IBM Big Data Platform, McGraw-Hill,
New York, N.Y., 2013, www-01.ibm.com/software/de/big-data/
pdf/assets/Harness.PDF.
8 Tarisai Masamvu, “5 Steps to Bringing Analytics to Life,”
CGMA Magazine, November 4, 2014, AICPA, New York, N.Y.,
www.cgma.org/magazine/news/pages/201411231.aspx.
9 Pavel Năstase and Dragoş Stoica, “A New Business
Dimension—Business Analytics,” Accounting & Management
Information Systems, December 2011, pp. 603-618.
10 Paul Dougherty, Prith Banerjee, Walid Negm, and Allan E.
Alter, “Driving Unconventional Growth through the Industrial
Internet of Things,” Accenture, 2014, www.accenture.com/
SiteCollectionDocuments/PDF/Accenture-DrivingUnconventional-Growth-through-IIoT.pdf.
11 Peter Trkman, Kevin McCormack, Marcos Paulo Valadares De
Oliveira, and Marcelo Bronzo Ladeira, “The impact of business analytics on supply chain performance,” Decision Support
Systems, June 2010, pp. 318-327.
12 Gary Cokins, “Mining the Past to See the Future,” Strategic
Finance, November 2014, pp. 23-30.
13 Gil Press, “$16.1 Billion Big Data Market: 2014 Predictions
From IDC and IIA,” Forbes.com, December 12, 2013,
www.forbes.com/sites/gilpress/2013/12/12/16-1-billion-big-datamarket-2014-predictions-from-idc-and-iia; and “Gartner Says
Worldwide Business Intelligence Software Revenue to Grow
7 Percent in 2013,” February 19, 2013, Gartner, Inc., Stamford,
Conn., www.gartner.com/newsroom/id/2340216.
14 www.imanet.org/2014-Annual-Report.
15 Jeff Bertolucci, “Big Data Success Remains Elusive: Study,”
InformationWeek, February 2, 2015, www.informationweek.com/
big-data/big-data-analytics/big-data-success-remains-elusivestudy/d/d-id/1318891.
16 Dan Power, “What is the ‘true story’ about using data mining
to identify a relationship between sales of beer and diapers?”
DSS News, November 10, 2002, www.dssresources.com/faq/index.php?action=artikel&id=41.
17 Al Lee, Sarah Giusti, Brendan Kitts, Dyng Au, Jane Shepard,
Lisa Moore, Stefanie Zlomek, Craig Schwartz, John Crim,
Mark Nelson, Kim Lesinski, Nina Rongisch, Mike Carstens,
Jason Bangs, Brenton Pyle, Paul Nygard, and Mike Ebeling,
“Physicians Mutual Insurance Company: Insurance For All Of
Us,” 2014 David Ogilvy Awards Winning Case Study, The
Advertising Research Foundation, New York, N.Y., 2014,
www.appliedaisystems.com/papers/Physicians_Mutual_
CaseStudy4_SanitizedforPublic.pdf.
18 PwC, “Data Driven: What students need to succeed in a
rapidly changing business world,” February 2015,
www.pwc.com/us/en/faculty-resource/assets/PwC-Datadriven-paper-Feb2015.pdf.
19 Rita L. Sallam, Bill Hostmann, Kurt Schlegel, Joao
Tapadinhas, Josh Parenteau, and Thomas W. Oestreich,
“Magic Quadrant for Business Intelligence and Analytics
Platforms,” Gartner, Inc., February 25, 2015, revised May 22,
2015, www.gartner.com/technology/reprints.do?id=12ACLP1P&ct=150220&st=sb.
For those who are ready to start using data analytics
software, Gartner published “Magic Quadrant for
Business Intelligence and Analytics Platforms,” which
compares the strengths and weaknesses of the business
intelligence and data analytics products from a variety
of vendors.19
Cost Reporters or Cost Advisors?
Most of the work management accountants perform today uses financial accounting data, which is like driving
a car while looking in the rear-view mirror. That is because accounting data is at the end of a long stream of
data-generating activities and represents only a tiny portion of the analyzable data available inside the organization. To step into a broader role, management accountants must move well beyond traditional reports,
scorecards, and dashboards based on historical accounting data. Pursuing a data fracking strategy can get them
there and keep them relevant. ■
Glen L. Gray, Ph.D., CPA, is a professor in the Accounting
and Information Systems Department of the David
Nazarian College of Business & Economics at California
State University at Northridge in Northridge, Calif. You can
reach him at (818) 677-2461 or [email protected].
Michael Alles, Ph.D., is an associate professor in the
Department of Accounting and Information Systems of
Rutgers Business School at Rutgers University in Newark,
N.J. You can reach him at (973) 353-5352 or
[email protected].
Endnotes
1 www.imanet.org/about-ima/our-mission.
2 Richard Landry, Roger Debreceny, and Glen L. Gray, “Grab
Your Picks and Shovels! There’s Gold in Your Data,” Strategic
Finance, January 2004, pp. 24-28.
3 For this article, unless we need to specifically distinguish between types of analytics, we use the term “data analytics” as a
generic term to include any data analytic techniques, such as
data mining, BI, data analytics, predictive analytics, and prescriptive analytics.
4 B. Douglas Clinton and Larry R. White, “The Role of the
Management Accountant: 2003-2012,” Management Accounting
Quarterly, Fall 2012, pp. 40-74.
5 www.gartner.com/it-glossary/dark-data.
6 ACCA and IMA, “Big Data: Its Power and Perils,” ACCA,
New York, N.Y., 2013, www.accaglobal.com/content/dam/
acca/global/PDF-technical/futures/pol-afa-bdpap.pdf.
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