Lesson 1.2
➢ Categorization of
Analytical Methods
➢ Big Data
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By: Ana Leah A. Alconis
Fundamentals
of Business
Analytics

A Categorization of Analytical Methods
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A Categorization of Analytical Methods and
Model
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Descriptive
Analytics
Predictive Analytics
Prescriptive
Analytics
Descriptive analytics answers
the questions what happened
and why it happen
Predictive analytics answers
the question what will happen
Prescriptive analytics anticipates
what will happen, when it
happened, and also why it happened

A Categorization of Analytical Methods and
Model
• Descriptive analytics: It encompasses the set of techniques that
describes what has happened in the past.
Examples - data queries, reports, descriptive statistics, data
visualization (data dashboards), data-mining
techniques, and basic what-if spreadsheet
models.
• Data query - It is a request for information with certain
characteristics from a database.
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A Categorization of Analytical Methods and
Model
• Data dashboards - Collections of tables, charts, maps, and
summary statistics that are updated as new data become
available.
• Uses of dashboards
• 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.
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Process involved using Descriptive Analytics.
Data Analyze
(Descriptive
Model)
Provides and
Generate
Reports
Smart Decisions

▪Descriptive analytics answers the questions what happened and
why it happened.
▪A sample picture shows data of the context of the source data
“Course Pro Campaign” used in a report.
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A Categorization of Analytical Methods and
Model
• Predictive analytics: It consists of techniques that use models
constructed from past data to predict the future or ascertain the
impact of one variable on another.
• Survey data and past purchase behavior may be used to help
predict the market share of a new product.
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A Categorization of Analytical Methods and
Model
• Techniques used in Predictive Analytics: contd.
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• Used to find patterns or relationships among
elements of the data in a large database; often used
in predictive analytics.
Data mining
• It involves the use of probability and statistics to
construct a computer model to study the impact of
uncertainty on a decision.
Simulation

Process involved using Predictive
Analytics.
Data Analyze
(Predictive
Model)
Provides
Predictions and
Forecasting
Smart Decisions

▪Predictive analytics answers the question what will happen, A
sample picture shows predictive analytics workbench. A predictive
analytics workbench allows a user to create, validate, manage, and
deploy predictive analytic models. A predictive analytics workbench
consists of these components
Predictive
Analytics
1

A Categorization of Analytical Methods and
Models
• Prescriptive Analytics: It indicates a best course of action to take
• Models used in prescriptive analytics:
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• Models that give the best decision subject to constraints of the situation.
Optimization models
• Combines the use of probability and statistics to model uncertainty with
optimization techniques to find good decisions in highly complex and highly
uncertain settings.
Simulation optimization
• Used to develop an optimal strategy when a decision maker is faced with
several decision alternatives and an uncertain set of future events.
• It also employs utility theory, which assigns values to outcomes based on
the decision maker’s attitude toward risk, loss, and other factors.
Decision analysis

Process involved using Prescriptive
Analytics.
Data Analyze
(Prescriptive
Model)
Provides ,Generate
Recommendation
using Mathematical
Algorithms
Smart Decisions

▪Prescriptive analytics not only anticipates what will happen and
when it will happen. but also why it will happen. Prescriptive
analytics software has the potential to help during each phase of the
oil and gas business through its ability to take in seismic data, well
log data, and their related data sets to prescribe where to drill, how
to drill there and how to minimize the environmental impact.
Prescriptive
Analytics
1

A Categorization of Analytical Methods and
Model
• Optimization models
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Model Field Purpose
Portfolio models Finance Use historical investment return data to
determine the mix of investments that yield the
highest expected return while controlling or
limiting exposure to risk.
Supply network
design models
Operations Provide the cost-minimizing plant and distribution
center locations subject to meeting the customer
service requirements.
Price markdown
models
Retailing Uses historical data to yield revenue-maximizing
discount levels and the timing of discount offers
when goods have not sold as planned.

Big Data
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Big Data
• Big data: A set of data that cannot be managed, processed, or
analyzed with commonly available software in a reasonable
amount of time.
• Big data represents opportunities.
• It also presents analytical challenges from a processing point of
view and consequently has itself led to an increase in the use of
analytics.
• More companies are hiring data scientists who know how to
process and analyze massive amounts of data.
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Big Data
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BIG DATA is characterized
by a large volume of
different types of data (e.g.
social, web, transaction,
etc.) that builds very
quickly. It exceeds the
reach of commonly used
hardware environments
and software tools to
capture, manage and
process in a timely manner
for its users.

Big Data
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Big Data
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Big Data
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Big Data
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Type of Data
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• Structured Data
➢ Stored in tabular format
➢ Clearly defined
➢ Data is stored in a pre-defined
data model
• Unstructured data
➢ No pre-defined structure
➢ No data model
➢ Data is irregular and ambiguous
• Semi-Structured Data
➢ It falls between structured and
unstructured data
➢ It is a combination of both

Characteristics of Big Data
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• Volume – Data Size
• Velocity – Speed of
Change
• Variety – different forms
of Data Sources
• Veracity – Uncertainly of
Data
• Value – Business Value

Activity 1.2
1.What do you know about the term “Big
Data”?
1.How is big data analysis helpful in
increasing business revenue?
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