Introduction to Business Data Analytics – Global Homework Experts

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1 Introduction to Business Data Analytics: A Practitioner View
The Introduction to Business Data Analytics: A Practitioner View introduces
business analysis concepts, activities, tools, techniques, skills and how
they’re applied when performing business data analytics related work.
Business data analytics has become an area of great interest for
organizations, as it has been recognized as a means by which organizations
can obtain valuable insights from data; supporting more informed business
decision-making. As a result, more organizations are investing in business
data analytics as a means to deliver on their strategic imperatives, innovate,
and obtain competitive advantages in their marketplace. Such investments
are driving the demand for more skilled professionals with business data
analytics knowledge and experience.
This Introduction to Business Data Analytics: A Practitioner View explores the
relationship of business data analytics to business analysis, emphasizing how
analysis experience coupled with business analysis techniques and
competencies can support business data analytics initiatives across the
organization.
What is Business Data Analytics?
As a broad definition, business data analytics is a practice by which a specific
set of techniques, competencies, and procedures are applied to perform
continuous exploration, iteration, and investigation of past and current
business data for the purposes of obtaining insights about a business that can
lead to improved decision-making. Business data analytics can be defined
more specifically through several perspectives.
These perspectives include, but are not limited to business data analytics as
a:
• movement,
• capability,
• data-centric activity set,
• decision-making paradigm, and
• set of practices and technologies.
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Introduction to Business Data Analytics: A Practitioner View Business Data Analytics Objectives
Business Data Analytics Objectives
Organizational leaders frequently make business decisions based on personal
expertise and instinct. Business data analytics removes cognitive and
personal biases from the decision-making process by using data as the
primary input for decision-making. When performed well, business data
analytics can create a competitive advantage for the organization.
For example, algorithms based on weather, soil, and other conditions have
been found to be more accurate in predicting the price and quality of red wine
after it has been aged compared to the wine experts who influence the
decision-making based on their own cognitive biases as to what they enjoy
and do not enjoy in a wine.
In a broad sense, the objective of business data analytics is to explore and
investigate business problems or opportunities through a course of scientific
inquiry. The specific objectives of business data analytics are dependent on
the type of analysis that is being performed.
There are four types of analytics methods:
Descriptive: provides insight into the past by describing or summarizing
data. Descriptive analytics aims to answer the question “What has
happened?”
Diagnostic: explores why an outcome occurred. Diagnostic analytics is
used to answer the question “Why did a certain event occur?”
Predictive: analyzes past trends in data to provide future insights.
Predictive analytics is used to answer the question “What is likely to
happen?”
Prescriptive: utilizes the findings from different forms of analytics to
quantify the anticipated effects and outcomes of decisions under
consideration. Prescriptive analytics aims to answer the question “What
should happen if we do …?”
TIME / QUESTION T YPE WHAT WHY
PAST DESCRIPTIVE
What happened?
DIAGNOSTIC
Why did it
happen?
PAST/FUTURE
FUTURE
PREDICTIVE
What is likely to happen based on past trends?
PRESCRIPTIVE
What should happen if we take a certain path?
What is the best outcome given the uncertainty?
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