Business Intelligence and Big Data (Week 8)

Question 1

What is Business Intelligence (BI)?

Answer 1

Tools and processes for analysing external and internal data and decision-making

Question 2

Examples of BI

Answer 2

–> Data Warehousing

–> Dashboards

–> OLAP (Online Analytical Processing)

–> Data mining analytics

Question 3

Availability of data

Answer 3

Data can be sourced from:
–> social media

–> ‘open data’ e.g. google maps

–> ‘smart’ devices connected to the internet

Question 4

What drives BI?

Answer 4

–> Implementing performance management systems

–> Adhering to new regulations

–> Prioritizing Customer Relationship Management (CRM) and personalised marketing

–> Adapting to market trends like globalization and mergers

–> Embracing digital business, marketing, and social media

–> Fostering a data-driven organizational culture focused on analytics

Question 5

What is Corporate Performance Management (CPM)?

Answer 5

Performance management that aids strategic decisions. Also includes processes and tech for measuring and monitoring performance

Question 6

What is Customer Relations Management (CRM)?

Answer 6

Systems that manage customer interactions and maximise the customer lifetime value for the firm

Question 7

Analytical CRM

Answer 7

Analysis of customer data to provide insights or models to optimise aspects of our customer relationships

e.g. which customer segment to target for retention campaign

Question 8

Operational CRM

Answer 8

Systems supporting customer-facing processes

e.g. call-centres and customer service support

Question 9

What is Regulatory Compliance?

Answer 9

Rules that firms have to abide to. Digital tech allows regulatory bodies to monitor and manage more efficiently

Question 10

What is Data Warehousing?

Answer 10

A database of copied transactions that is to be then analysed and aid decision making

Question 11

Subject-Orientated Data Warehousing

Answer 11

Organised around the major subjects of an enterprise (e.g. customers, products, and sales) rather than the major application areas (e.g. customer invoicing, stock control, and order processing)

Question 12

Integrated Data Warehousing

Answer 12

Combines data from different sources to keep a consistent and unified perspective for analysis. (Centralised and cross-functional)

Question 13

Time-Variant Data Warehousing

Answer 13

Data that is accompanied with time to help provide historic accuracy

Question 14

Non-Volatile Data Warehousing

Answer 14

New data is always added as a supplement to the data warehouse, rather than as a replacement

Question 15

What is Extract, Transformation, Load (ETL)?

Answer 15

Tools that set up and configure an automated system that regularly updates the data warehouse

E (extract): data from source systems

T (Transform): data

L (Load): transformed data into the data warehouse

Question 16

What is Big Data?

Answer 16

Data that has high:
–> Volume (size of data)

–> Velocity (how fast new data is generated)

–> Variety (many different forms)

Question 17

What is a Data Lake?

Answer 17

Data that is stored in its original format because it has potential to be analysed and used for decision making

Question 18

What is Hadoop?

Answer 18

Open-source framework that has become popular for distributed storage and parallel processing of massive amounts of data

Question 19

What is Online Analytical Processing (OLAP)?

Answer 19

Interactive analysis of large volumes of data from multiple dimensions

Question 20

What is the 3 Elements of Multi-Dimensional Analysis

Answer 20

–> Dimensions: perspectives from which to analyse data
e.g. time, product, geography, etc

–> Hierarchies within a dimension: level of detail
e.g. geography: world region – country – city – shop

–> Numeric values such as units, revenue, cost, etc.,
or values derived from them, e.g. profit

Question 21

What is Drilling?

Answer 21

Navigating through a dimension hierarchy to desired level of detail

Question 22

What is Drilling Down?

Answer 22

Go down the hierarchy or introduce extra dimension

e.g. –> Total sales
–> Total sales per city
–>Total sales per city per –> shop

Question 23

What is Drilling Up?

Answer 23

Climb up hierarchy or reduce dimensions

(e.g. get measure at more whole level)

Question 24

What is Drilling Across?

Answer 24

Within same dimension select another attribute value

e.g. After viewing the results for 2011, change the selection to 2012

Question 25

What is Slicing?

Answer 25

Take horizontal or vertical cut of cube, i.e. restrict one dimension

e.g. –> Sales data for product X
–> Sales data for shop A

Question 26

What is Dicing?

Answer 26

Restricting two or more dimensions

e.g. Sales data for products X and Y, in shops A and B, during the summer

Question 27

Disadvantages of using OLAP

Answer 27

–> Inefficient to manually investigate 10,000’s of data

–> No prediction for the future

–> Customer attrition (customers lost)

Question 28

What is Data Mining/ Analytics?

Answer 28

Applying computational techniques to find interesting patterns or derive a predictive model

Question 29

What is Predictive Analytics?

Answer 29

Using past data to predict future outcomes for individuals based on observable variables

3 tasks:
–> Classification

–> Regression or estimation

–> Forecasting

Question 30

What is Descriptive Analytics?

Answer 30

Identifying and describing patterns present in the data

Via:
–> Association Analysis

–> Segmentation/ clustering

Question 31

Predictive Analytics: Classification

Answer 31

Use input variables to classify subject into one of two or more predefined target classes

(e.g. predict whether individual customer will be good or bad payer)

Example Models: Decision tree, Scorecard

Question 32

Predictive Analytics: Regression/ estimating

Answer 32

Predict value of a continuous (numeric) target variable

(e.g. profit in GBP, loss, etc.)

Question 33

Predictive Analytics: Forecasting

Answer 33

Regression over time-series data

Question 34

Descriptive Analytics: Association

Answer 34

Detect frequently occurring patterns of items in a large transaction database

Question 35

Descriptive Analytics: Segmentation/ Clustering

Answer 35

Identify clusters or segments of homogenous subjects

(e.g. having similar values for a series of variables)

Question 36

Types of Big Data Analytics

Answer 36

–> Text Mining

–> Image Processing

–> Social Network Analytics