1. Running head: Quantitative Business Modeling 1
Quantitative Business Modeling – A Perspective
Stephanie Wiltz
Grand Canyon University: Buss 660-O101
October 28, 2015

2. Quantitative Business Modeling 2
Quantitative Business Modeling – A Perspective
First and foremost, a company needs to embrace the quantitative analytics mantra and it
must permeate throughout all levels and all departments. The fundamental value of analytics
should be emphasized and a salacious appetite for numbers and other data should be had by all
who make decisions within an organization. A popular and still relevant article by Thomas
Davenport (2006) details the attributes of 32 organizations that have achieved successful
implementation of optimal, quantitative, analytics. Among the characteristics shared by these
companies are the uses of a variety of complex models, sophisticated data software, ‘think-
outside-the-box’ approach to statistical analysis and hiring practices centered on employing those
with an analytic edge. The author also opines the importance of continuity of operations by
ensuring that any data and models used are centralized and standardized.
In my current position as an accounting analyst for First American Title, the team I work
with is responsible for creating and implanting ways to lean out costly processes and utilize data
for internal controls and furtherance of efficiency. We often salivate over a new spreadsheet that
provides newly extracted data or a different way of interpreting data. Davenport (2006) cautions
that research reveals that twenty to forty percent of spreadsheets have errors, therefore, the more
spreadsheets used the more errors there will be. For this reason when we do come across a new
spreadsheet, we ensure that it either updates an outdated one or integrates across departments for
continuity purposes.
One industry that widely practices business analytics is the hospital and healthcare
industry. In an article by Rivera and Delaney (2015) a case study is highlighted where Orlando
Health Physicians Group implements business analytics to integrate their two revenue processing
systems, monitor physician performance, identify patients in need of bad debt write-offs and

3. Quantitative Business Modeling 3
denied claims and reports that detail discrepancies in patient billing. These and many other
features of their analytic endeavors have increased their ability to manage their revenue and
enterprise, implement optimal cost-saving and efficient processes and increase patient
satisfaction. Davenport (2006) stated that one source of strength for analytic competitors were
focusing the analytic endeavors towards a broader set of improvement goals. Orlando Health
focused primarily on patient satisfaction and operational collaboration. Through this they were
able to achieve unexpected and enduring savings via various analytics. This study strengthens
Davenports (2006) argument that the implementation of any analytic undertaking should be
conducive to, understandable and useable by the entire organization. Even if a model is not a
direct solution to the problem or problems at hand it should not be so quickly negated, as it may
be a solution to another problem that would free up resources that can be allocated to the primary
problem. Like Davenport, Rivera and Delaney (2015) emphasize the need for organizations to
prudently pinpoint their improvement needs and apply an adaptable business intelligence
solution. They also maintain that the use of predictive analytics is just as paramount as the use
of past data in order to keep up with industry changes and remain competitive.
Wilson and Demers (2015) are huge proponents of predictive analytics and advocate a
sort of ‘recreating the wheel’ concept when it comes to how businesses forecast demand. In what
they dub as a “revolution” and “evolution” approach to predictive statistics, they contend that
those companies that are utilizing such analytics are those that remain competitive. I suppose
that this mantra is easier said than done. How does a company sift through and find untapped
talent, such as an employee who is stuffed in a cubicle all day long, but who has the ability to
provide remarkable insight into facets of the company’s operations? Perhaps one method would
be to conduct employee surveys or interview employees to gain such knowledge. There really is

4. Quantitative Business Modeling 4
no limit to the data that can be obtained, keeping in mind resources and cost-benefit components.
Wilson and Demers (2015) also talk about predictive analytics in terms of it being disruptive-
innovation. I cannot agree enough with this and that is exactly what quantitative business
modeling should seek to achieve. Disrupt business as usual with innovations that can bring about
new found avenues of efficiency and an overall better way to do business.
Companies need to appropriately diagnose a problem or process improvement and then
prescribe a solution which is aided by descriptive statistics and other valuable analytical tools.
With today’s technology moving at the speed of light, business intelligence must persistently
revamp and work towards being able to quickly capture data and turn it into useful information.
Clearly those companies that fail to do this will be left in the data dust trail and thus, waver
among their competitors who are practicing what the aforementioned authors are preaching. I
need no convincing that quantitative business modeling is an essential tool to success in the
modern business world. From these articles I gather that while most professionals believe that
business analytics is a must, it is still an emerging practice yet to be fully taken advantage of and
implemented throughout.
I do agree with the authors that data should be obtained, organized, interpreted and
utilized to allow for operations to run more efficiently and effectively. However, I do not believe
that these objectives should impede upon ethical standards of practice. I do not believe that data
should be construed intentionally for the purpose of personal gain. I also believe that it should
not be used to invade the lives and privacy of customers, employees, shareholders or other
stakeholders. Rivera and Delaney (2015) stated the following in their article, “Whatever the
solution a health system chooses, the primary goal should be to establish common habits,
policies, and practices that benefit everyone…The solution should respect health system staff’s

5. Quantitative Business Modeling 5
diverse needs…” (pg. 67, para. 3). To me this is an idea pertaining to prescriptive statistics that
takes into account the needs of employee satisfaction and not simply increasing profits. Not all
solutions provided by quantitative business modeling are ones that will keep with standards set to
promote a quality of work life, excellent customer service and business relationships. I believe
that these solutions need to be re-analyzed to ensure that they conform to standards.
The sharing of data, or lack thereof, is another example of statistics crossing ethical
boundaries. An example comes from an article published by the Nature Publishing Group (2014)
that reports on pharmaceutical companies withholding valuable research and data from health
professionals and other researchers, because they believe it will be detrimental to their bottom
line. Drug companies are in a position to greatly impact and affect the lives of the public and that
position should be accompanied with the utmost transparency.
The purpose of business analytics is to instill change for the betterment of a company’s
operations. One should not misinterpret that to mean change for the betterment of a company’s
operations regardless of its impact on ethical and/or moral standards. I can see how easy it would
be for a company to get caught up in the idea of change and overlook its impact on the quality of
others. For this reason another concept that should be taught and upheld by all in an organization
is not only how to obtain data and construct models, but the subsequent steps of proper
implementation within an organization. Education of business analytics should emphasize and
entail this ethical collection and implementation. One’s faith and moral aptitude may reinforce
such standards, but businesses have a responsibility to instill and expect this of their employees.
Actions should be taken to ensure that leadership exudes the ethical use of data and regularly
conducts training on the subject for their staff. Just as businesses need to take a top-down
approach to quantitative analytics, they need to do so for its applied ethical domain.

6. Quantitative Business Modeling 6
References
Competing on Analytics: The New Science of Winning Thomas H. Davenport and
Jeanne G. Harris. (2013). Smart Business Indianapolis, 10(4), 15.
Rivera, J. & Delaney, M. (2015). using analytics to improve outcomes. Hfm (Healthcare
Financial Management), 69(2), 64-67.
Wilson, E. & Demers, M. (2014). Revolutionary and Evolutionary Approaches to
Leveraging Predictive Business Analytics, Journal of Business Forecasting, 33(4), 4-10.
Data sharing will pay dividends. (2014). Nature, 505(482), 131.