Page 103 - DCAP208_Management Support Systems

Management Support Systems

Notes Explaining why a certain result occurred (statistical analysis, quantitative analysis)
Experimenting to test previous decisions (A/B testing, multivariate testing)
Forecasting future results (predictive modeling, predictive analytics)
This discipline helps business owners and executives to obtain valuable insight about how their
particular businesses are performing, on average. It also helps them to determine how to plan
successfully for improvement.

Once the business goal of the analysis is determined, an analysis methodology is selected and
data is acquired to support the analysis. Data acquisition often involves extraction from one or
more business systems, cleansing, and integration into a single repository such as a data
warehouse or data mart. The analysis is typically performed against a smaller sample set of
data. Analytic tools range from spreadsheets with statistical functions to complex data mining
and predictive modeling applications. As patterns and relationships in the data are uncovered,
new questions are asked and the analytic process iterates until the business goal is met.
Deployment of predictive models involves scoring data records (typically in a database) and
using the scores to optimize real-time decisions within applications and business processes.
BA also supports tactical decision making in response to unforeseen events, and in many cases
the decision making is automated to support real-time responses.
The process of business analytics focuses specifically on understanding a company’s overall
business performance. Through these intricate activities, new ideas can develop that may help
to better prepare a business for future, or continued, success against its competitors. People who
work in the field of business analytics rely heavily on a wide variety of data to help them with
their daily tasks. Analysts who work in these types of jobs make regular use of quantitative and
statistical analyses, and they are also usually quite involved in predictive modeling, which is
essentially the process of predicting the likelihood of a particular outcome.
Practices of business analytics may be used to address issues of human activities and decisions,
or may be more focused on automated functions. People who work in this field are generally
quite good at coming up with answers to a wide range of questions that executives and company
owners are likely to ask, in an effort to determine the best course of action on a company-wide
scale. Analytics solutions usually involve the study of data in large quantities, so most people in
this profession are comfortable working with numbers on a grand scale.

!
Caution In order for analytics practices to be the most successful and beneficial for
companies, there must generally be massive quantities of data available for analysis.
When only small amounts of data are available, analytics tends to be less worthwhile for
businesses. Essentially, this means that in some cases, business analysts are quite likely to be
dealing with data that spans at least a few years. These positions, as a result, are most suited to
individuals who work well under pressure and who thrive on deadlines.
For the most part, people involved in business analytics are also responsible for anticipating the
changing needs of customers, which requires an insightful mind and shrewd deductive skills.
Professionals within this field are usually capable of solving difficult problems fairly quickly.
They are also usually able to suggest unique, innovative ideas to company executives for
consistent, regular, positive change regarding business practices.

7.1.2 Data Visualization

Data visualization is a pretty literal term that means, quite simply, the visual representation of
quantitative data.

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