CBIS 4120 Chapter 1

Business Intelligence & Analytics Ch 1

Data analytics is the process that involves:

a. Identifying the problem

b. gathering relevant data that frquently are not in a uable form

c. Cleaning up the data

d. Loading in data storage models

e. Manipulating them to discover trends and patterns

f. Making decisions based on those insights

Identifying, Gathering, Cleaning, Loading, Manipulating, Making Decisions

Data

• Raw figures

• Metadata

• Nothing known apart from numbers and metadata

Information

• With context become information

• Reveals relationships between entities

• What?

Knowledge

• With added meaning

• Reveals trends and patterns

• Why?

Wisdom

• Reveals ideas, principles, biases

• Insights, over time

  • What could happen?D

Decision

• Course of action

• Implementation

• Monitoring

• Correction

• What should we do?

Information example

We could process sales revenue data to display the average revenue per customer or to reveal which customers did not make any purchases from us within a given time period.

Knowledge example

Which customers did not buy from us because of pricing and which customers did not buy from us because of quality?

Wisdom Example

By examining the reasons why customers do not buy from us, over time be gain the wisdom to identify and implement policies our company should persue to acquire and retain customers

Decision Example

If our goal is to retain customers rather than seeking new customers, then we might launch loyalty programs

Domain Knowledge

the expertise gained by individuals in certain areas or fields

• medicine

• business in general

• Public services

• Sports and entertainment

• In every area

T/F: Data Analytics applies the algorithms and models generated from data science

TRUE

T/F : An example of machine learning is training a computer to learn language structures in order to prompt autocomplete words in mobile applications

TRUE

Data exploration

Slicing and dicing (data manipulation)

Retail analytics

Used extensively to assist in pricing, timing of pricing strategies, and amount of discounts

Manufacturing analytics

A type of data analysis called demand forecasting is the core

Marketing analytics

Can analyze each customers behavior and use these data to predict future behaviors

• The effectiveness of the campaign is measured based on customer response

Customer service / help desk analytics

Based on the analysis of pr9ior work orders or help tickets, the procedures that succeeded, problem-solving metrics, and data from various sources

Forecasting and Budgeting:

• Based on historical data and knowledge of the business environment

• The more accurate these forecasts are, the more likely management will make appropriate operational decisions

• The use of large quantities of data and analytic tools helps improve

Audit and Analysis of Internal Controls:

Data from internal systems are used to analyze risk and determine how well systems comply with managements policy of internal controls

T/F: Computer Science improves capabilities to perform data analytics

TRUE

Why study analytics?

• Demand for employees

• huge growth in the amount of data available

  • Analytics can provide strategic advantages to an organization

T/F: Analytics is performed and used by individuals who may not have formal training

TRUE

Governments analytics:

• Resource allocations, tax compliance, demographic data

utilities analytics:

• Demand forecasting, management of power supplies

• predicting consumer demands for power and managing the supply of power from producers

Financial investors analytics:

• Sift through the data of numerous companies to determine which are acceptable investments and which are high risk or unacceptable

Other examples of data anlytics applications:

Science, Medicine, Sports, Fraud prevention, law enforcement, social media platforms

Analytics Methodology within a framework ENABLERS:

• Technology

• Infrastructure

• Tools

• Techniques

Essential components needed for the methodology to work

Analytics Methodology within a framework BENEFITS:

• Value/Profit

• Performance

• Safety

• Health/Longevity

10 Steps in Analytics Methodology within a framework

Identify goals, gather data, design model, apply model, review results, present findings, derive insights, make decision, deploy strategy, improve

Whenever a business event is recorded by an information system, the relevant data are written to and stored in the database as

transactional data

• who created it

• when it was created

• for what purpose

Transactional data example

sales order

• About an event

master data

represent business entities that support business transactions

GB uses an integrated system (ERP)

master data examples

• Data about customers, products, vendors, employees, fixed assets

• Do not change significantly over time

• ‘To’ and ‘Ship to"‘ Data

T/F: Master data changes significantly over time

FALSE

T/F: master data contain numerous attributes and not all of them have a role in analytics

TRUE