1. The document discusses the scope of business analytics, including descriptive analytics which uses data to understand past performance, predictive analytics which seeks to predict the future by examining patterns in historical data, and prescriptive analytics which uses optimization to identify the best alternatives. 2. It defines data sets as collections of data and databases as collections of related files containing records on people, places or things. Big data is characterized by its large volume, variety, velocity and veracity. 3. Decision models are logical or mathematical representations of business problems or situations that can be used to analyze or facilitate decision making. They include constant data, uncontrollable variables, and controllable decision variables.

1. BUSINESS
ANALYTICS
P1.1
JASON C. SANTOS, MBA

3. AGENDA
- Scope of Business Analytics
- Data sets and Databases
- Decision Models

4. A VISUAL
PERSPECTIVE OF B.A.

5. SCOPE
1. Descriptive Analytics – The use of
data to understand past and current
business performance and make
informed decisions. It is the most
commonly used and most well-
understood type of analytics. It is
categorizing, characterizing,
classifying and converting of data to
useful information.

6. 2. PREDICTIVE ANALYTICS – seeks to predict
the future by examining historical data,
detecting patterns or relationships in these
data and then extrapolating these
relationships forward in time. Ex. A marketer
might wish to predict the response of different
customer segments to an advertising
campaign, a commodities trader might wish to
predict short-term movements in commodities
prices, or a skiwear manufacturer might want
to predict next season’s demand for skiwear of
a specific color and size. Predictive analytics
can predict risk and find relationships in data
not readily apparent with traditional analyses.

7. 3. PRESCRIPTIVE ANALYTICS – Many problems
such as aircraft or employee scheduling and
supply chain design, for ex., simply involve too
many choices or alternatives for a human decision
maker to effectively consider. Prescriptive
analytics uses optimization to identify the best
alternatives to minimize of maximize some
objective. Prescriptive Analytics is used in many
areas of Business including operations marketing
and finance. For. Ex, we may determine the best
pricing and advertising strategy to maximize
revenue, the optimal amount of cash to store in
ATMS, or the best mix of investments in a
retirement portfolio to manage risk.

8. DATA SETS AND
DATABASES
A Data set is simply a collection of data. Marketing
survey responses, a table of historical stock
prices, a collection of measurements of
dimensions of a manufactured item are examples
of data sets. A Database is a collection of related
files containing records on people places or
things. A database for an online retailer that sells
instructional fitness books and DVD’s for instance,
might consist of a file for three entities: publishers
from which goods are purchased, customer sales
transactions, and product inventory.

9. EX. OF DATABASE IN
EXCEL

10. BIG DATA
Today, nearly all data are captured digitally. As a result, data
have been growing at an overwhelming rate, being measured
in terabytes (𝟏𝟎 𝟏𝟐) Petabytes (𝟏𝟎 𝟏𝟓) Exabytes (𝟏𝟎 𝟏𝟖) , and
even by higher dimensional terms. Just think of the amount
of data stored on Facebook, Twitter, or Instagram servers. Or
the amount of data acquired daily from scanning items at a
national grocery chain such as SM SuperMalls, or Ayala Malls
that can reach up to or over a million transactions per hour.
(petabytes of data) These data is available real time and
much of which is uncertain or unpredictable. IBM calls these
characteristics the 4 V’s: volume, variety, velocity and
veracity.
What’s BIG today will be even Bigger tomorrow.

11. DECISION MODEL
A Decision Model is a logical or mathematical representation
of a problem or business situation that can be used to
understand analyze or facilitate making a decision.
1. Data which are assumed to be constant for purposes of
the model. Some examples would be costs, machine,
capacities and intercity distances.
2. Uncontrollable variables which are quantities that can
change but cannot be directly controlled by[ the decision
maker or uncertain. Some examples would be customer
demand, inflation rates and investment returns.
3. Decision variables which are controllable can be selected
at the discretion of the decision maker. Some examples
would be production quantities, staffing levels and
investment allocations.

12. NATURE OF DECISION
MODELS