This document outlines a business analytics framework with 4 layers: 1) Data Layer which sources and transforms data, 2) Analytics Layer which analyzes data using descriptive, predictive, or prescriptive techniques like data mining and multidimensional analysis, 3) Reporting/Visualization Layer which uses tools like dashboards and reports, and 4) Access Layer. It also describes 4 types of analytics: descriptive (explains what happened), diagnostic (explains why it happened), predictive (forecasts what may happen), and prescriptive (recommends actions based on forecasts).

1. BUSINESS
ANALYTICS
FRAMEWORK
John Christopher V. Reguindin, MIS
Faculty
STI-West Negros University

2. 4 Layers of Business Analytics
Framework:
1. Data Layer
2. Analytics Layer
3. Reporting and Visualization
Layer
4. Access Layer

3. Framework for Business
Analytics
3

4. Data Layer
4
Sources of Data
Where data is being transformed
Data warehouse is a copy of transaction data
specifically structured for query and analysis
localized data warehouses, are small-sized data
warehouses, typically created by individual
divisions or departments to provide their own
decision support activities.

5. Analytics Layer
5
In this layer, data from Data
Warehouse/Data Mart are analyzed by using
descriptive, predictive, or prescriptive
analytics.

6. Analytics Layer
6
Various techniques used in this layer:
A. Data Mining –
The process of exploration and analysis, by semi-automatic or
automatic means, of huge quantities of data in order to discover
meaningful patterns and rules
The technique that includes management science, statistical,
mathematical and financial models and methods, used to find the vital
relationships between variables in the historical data, perform analysis on
the data or to forecast from data.

7. Analytics Layer
7
Various techniques used in this layer:
B. Multidimensional Data Analysis
Also known as Online Analytical Processing (OLAP), it is part of
the wider variety of business intelligence software that enables
managers, executives, and analysts to gain insight into data through
rapid, reliable, collaborative access to a wide range of multidimensional
views of information.
It also allows business analysts to rotate data, changing the
relationships to get more detailed insight into corporate information.

8. Reporting/Visualization
Layer
8
Various tools used in this layer:
Dashboards, Balance Scorecards, Reports,
Ad hoc Reports and Alert

9. TYPES OF
ANALYTICS
John Christopher V. Reguindin, MIS
Faculty
STI-West Negros University

10. 4 Types of Analytics :
1. Descriptive
2. Diagnostic
3. Predictive
4. Prescriptive.

11. Descriptive
Analytics
11
- Explains what happens
- Gives information about the past performance or state of
a business and its environment by using existing data
- Helps companies to gain insight from historical data with
reporting, scorecards, clustering and to look at the facts
like, what has happened, where, and how often.

12. Diagnostic
Analytics
12
- Explains why it happens
- focuses on past performance to determine the answer to
the questions like why it is happening or why something
happened.
- gives companies deep insight into a problem by
techniques such as drill-down, data discovery, data
mining, etc. to find out dependencies and to discover
patterns from the historical data.

13. Predictive Analytics
13
- Forecast what may happens
- determine the probable future outcome for an event, or
the likelihood of the situation occurring and identify
relationship patterns.
- Its objective is to understand the causes and
relationships in the data to make accurate predictions.

14. Prescriptive
Analytics
14
- Recommends an Action based on Forecast
- helps to choose the best possible outcome by evaluating
a number of possible outcomes.
- Combination of descriptive and predictive models
together with probabilistic and random methods such as
Bayesian models or Monte Carlo Simulation to assist in
the determination of the best course of action based on
various “what if” scenario assessments.

15. THANK YOU AND
GODBLESS