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Business analytics questions & right answers.
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Business analytics questions & rightanswers.
Strategic decision ANS - A decision that involves higher-level issues and that is concerned with the
overall direction of the organization, defining the overall goals and aspirations for the organization's
future
tactical decision ANS - A decision concerned with how the organization should achieve the goals and
objectives set by its strategy.
- responsibility of mid-level management.
-span a year
operational decisions ANS - A decision concerned with how the organization is run from day to day
-domain of operations managers.
+ closest to the customers.
It is the responsibility of managers to ANS - plan, coordinate, organize, and lead their organizations to
better performance. Ultimately, managers' responsibilities require that they make strategic, tactical, or
operational decisions.
decision making can be defined as the following process: ANS - Identify and define the problem.
Determine the criteria that will be used to evaluate alternative solutions.
Determine the set of alternative solutions.
Evaluate the alternatives.
Choose an alternative.
What makes decision making difficult and challenging? ANS - Uncertainty is probably the number one
challenge.
business analytics ANS - The scientific process of transforming data into insight for making better
decisions.
,Used for data driven or fact based decision making.
also called:
- data analytics
- data intelligence
- busincess science
Analytics is generally thought to comprise of three broad categories of techniques: ANS - - descriptive
analytics
- predictive analytics
- prescriptive analytics
Descriptive analytics ANS - Analytical tools that describe what has happened.
and why did it happen?
Examples are data queries, reports, descriptive statistics, data visualization including data dashboards,
some data-mining techniques, and basic what-if spreadsheet models.
data query ANS - A request for information with certain characteristics from a database.
data dashboard ANS - A collection of tables, charts, and maps to help management monitor selected
aspects of the company's performance.
used to help management monitor specific aspects of the company's performance related to their
decision-making responsibilities.
For corporate-level managers, daily data dashboards might summarize sales by region, current inventory
levels, and other company-wide metrics; front-line managers may view dashboards that contain metrics
related to staffing levels, local inventory levels, and short-term sales forecasts.
,data mining ANS - the use of a variety of statistical analysis tools to uncover previously unknown
patterns in the data stored in databases or relationships among variables.
-extraction of data from data base.
For example, by analyzing text on social network platforms like Twitter, data-mining techniques
(including cluster analysis and sentiment analysis) are used by companies to better understand their
customers. By categorizing certain words as positive or negative and keeping track of how often those
words appear in tweets, a company like Apple can better understand how its customers are feeling
about a product like the Apple Watch.
predictive analytics ANS - Techniques that use models constructed from past data to predict the future
or to ascertain the impact of one variable on another.
what will happen and when will it happen?
For example, past data on product sales may be used to construct a mathematical model to predict
future sales. This mode can factor in the product's growth trajectory and seasonality based on past
patterns. A packaged-food manufacturer may use point-of-sale scanner data from retail outlets to help
in estimating the lift in unit sales due to coupons or sales events. Survey data and past purchase
behavior may be used to help predict the market share of a new product.
used to forecast financial performance, to assess the risk of investment portfolios and projects, and to
construct financial instruments such as derivatives
what falls under predictive analysis and is referred to as risk analysis? ANS - Linear regression, time
series analysis, some data-mining techniques, and simulation
data mining example for predictive analysis ANS - For example, a large grocery store chain might be
interested in developing a targeted marketing campaign that offers a discount coupon on potato chips.
By studying historical point-of-sale data, the store may be able to use data mining to predict which
customers are the most likely to respond to an offer on discounted chips by purchasing higher-margin
items such as beer or soft drinks in addition to the chips, thus increasing the store's overall revenue.
simulation ANS - The use of probability and statistics to construct a computer model to study the
impact of uncertainty on the decision at hand.
, For example, banks often use simulation to model investment and default risk in order to stress-test
financial models. Simulation is also often used in the pharmaceutical industry to assess the risk of
introducing a new drug.
Prescriptive Analytics ANS - Techniques that analyze input data and yield a best course of action.
why wil it happen?
provide a forecast or prediction, but do not provide a decision.
used to construct optimal portfolios of investments, to allocate assets, and to create optimal capital
budgeting plans.
For example, GE Asset Management uses optimization models to decide how to invest its own cash
received from insurance policies and other financial products, as well as the cash of its clients, such as
Genworth Financial. The estimated benefit from the optimization models was $75 million over a five-
year period
rule based model ANS - A prescriptive model that is based on a rule or set of rules.
optimization models ANS - A mathematical model that gives the best decision, subject to the situation's
constraints.
simulation optimization ANS - The use of probability and statistics to model uncertainty, combined with
optimization techniques, to find good decisions in highly complex and highly uncertain settings.
Decision Analysis ANS - A technique used to develop an optimal strategy when a decision maker is
faced with several decision alternatives and an uncertain set of future events.
utility theory ANS - The study of the total worth or relative desirability of a particular outcome that
reflects the decision maker's attitude toward a collection of factors such as profit, loss, and risk.
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