Business
intelligence refers to the type of granular information
that line-of-business managers seek as they analyze
sales trends, customer buying habits and other key performance
metrics of an organization. Companies use a wide range
of technologies and products to generate what's known
as business intelligence (BI). The most common tools
- simple query and reporting, online analytical processing,
statistical analysis, forecasting and data mining -
can be used in a variety of ways. Applications can provide
ad hoc access to a single piece of data, such as monthly
sales figures. Or they can be mission-critical, Web-enabled
engines used to drive business processes. The goal is
to turn what are often mountains of data into useful
information. The common platform to achieve this is
the database.
Business
Statistics is a science assisting you to make
business decisions under uncertainties based on some
numerical and measurable scales. Decision making processes
must be based on data, not on personal opinion nor on
belief. Statistical skills enable them to intelligently
collect, analyze and interpret data relevant to their
decision-making.
Statistical concepts and statistical
thinking enable them to:
- solve problems in a diversity
of contexts
- add substance to decisions.
- reduce guesswork
As an example
of statistical modeling with managerial implications,
such as "what-if" analysis, consider regression
analysis. Regression analysis is a powerful technique
for studying relationship between dependent variables
(i.e., output, performance measure) and independent
variables (i.e., inputs, factors, decision variables).
Summarizing relationships among the variables by the
most appropriate equation (i.e., modeling) allows us
to predict or identify the most influential factors
and study their impacts on the output for any changes
in their current values.Your organization database contains
a wealth of information, yet the decision technology
group members tap a fraction of it.
THE PROCESS
A) Basic Statistics
Before starting the advanced processing step it is necessary
to describe the contents of the matrix that will be
processed.This statistical treatment aims to show the
distributions of both row and columns elements in the
matrix (pies, histograms, tables of synthesis...). This
step has a two main goals. On one hand it provides quantitative
information about the data which can be directly used
as basic description of superficial phenomenons. On
the other hand it makes possible to detect some characteristics
which will help to adjust the advanced processings.
B) Classification
Process - The classification process aims to group homogeneous
clusters. The row elements will be grouped regarding
the most similar profiles towards the descriptive variables.
The association measure between row elements is based
upon specific criteria specially tuned to deal with
the kind of data we get from online databases. Different
criteria are available, following the data set characteristics.
Besides their powerful axiomatic properties, they prove
to be well suited to take into account that some information
are very dominant while others are much more rare although
fundamental.
C) Results Analysis
and Interpretation - The partition obtained from the
classification process has to be post analyzed with
comparison to the basic data.
Today's good decisions
are driven by data. In all aspects of our lives, and
importantly in the business context, an amazing diversity
of data is available for inspection and analytical insight.
Business managers and professionals are increasingly
required to justify decisions on the basis of data.
They need statistical model-based decision support systems.
Employees waste
time scouring multiple sources for a database. The decision-makers
are frustrated because they cannot get business-critical
data exactly when they need it. Therefore, too many
decisions are based on guesswork, not facts. Many opportunities
are also missed, if they are even noticed at all.
Probability,
Chance, Likelihood, and Odds
The concept of probability occupies an important place
in the decision-making process under uncertainty, whether
the problem is one faced in business, in government,
in the social sciences, or just in one's own everyday
personal life. In very few decision-making situations
is perfect information -- all the needed facts -- available.
Most decisions are made in the face of uncertainty.
Probability enters into the process by playing the role
of a substitute for certainty - a substitute for complete
knowledge. Probability is especially significant in
the area of statistical inference. Here the statistician's
prime concern lies in drawing conclusions or making
inferences from experiments which involve uncertainties.
The concepts of
probability make it possible for the statistician to
generalize from the known (sample) to the unknown (population)
and to place a high degree of confidence in these generalizations.
Therefore, Probability is one of the most important
tools of statistical inference.
|
