ITE4 - Business Intelligence

Business Intelligence

__ uses technologies, processes, and applications to analyze mostly internal, structured data and business processes

competitive intelligence

gathers, analyzes and disseminates information with a topical focus on company competitors.

Bl Business Purposes

• Measurement

• Analytics

• Reporting/Enterprise Reporting

• Collaboration/Collaboration Platform

• Knowledge Management

Measurement

program that creates a hierarchy of Performance metrics (Metrics Reference Model) and Benchmarking that informs business leaders about progress towards business goals (AKA Business process management).

Analytics

program that builds quantitative processes for a business to arrive at optimal decisions and to perform Business Knowledge Discovery.

Reporting/Enterprise Reporting

program that builds infrastructure for strategic Reporting to serve the Strategic nanagement of business, NOT Operational Reporting.

Collaboration/Collaboration platform

program that gets different areas (both inside and outside the business) to work together through Data sharing and Electronic Data Interchange.

Knowledge Management

program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge.

Learning Management and Regulatory compliance/Compliance

Knowledge Management leads to _____________

Data Mining

Is the process of identifying valid, novel, potentially useful and ultimately comprehensible information from databases that is used to make crucial business decisions

Data Mining

Predicts future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions

Task Solved by DM

• Predicting

• Classifying

• Detection of relations

• Explicit Modeling

• Clustering

• Deviation Detection

Predicting

A task of learning a pattern from examples and using the developed model to predict future values of the target variable

Classification

A task finding a function that maps an example into one of several discrete classes

Detection of relations

A task of searching for the most influential independent variables for a selected target variable

Explicit modeling

A task of finding explicit formulae describing dependencies between various variables

Clustering

A task of identifying a finite set of categories or clusters that describe data

Deviation detection

A task of determining the most significant changes in some key measures of data from previous or expected values

Technologies Used in DM

• Neural Networks

• Rules Induction

• Evolutionary Programming

• Case based Reasoning

• Decision Trees

• Genetic Algorithms

• Nonlinear regression models

Neural networks

Nonlinear predictive models that learn through training and resemble biological neural networks in structure

Rules induction

The extraction of useful if-then rules from data based on statistical significance

Evolutionary programming

Automatically formulates hypothesis about the dependence of the target variable on other variables, in the form of programs expressed in an internal programming language

Case based reasoning

To forecast a future situation, or to make a correct decision, such systems find the closest past analogs of the present situation and choose the same solution, which was the right one in those past situations.

Decision trees

Tree-shaped structures that represent sets of decisions

Genetic algorithms

Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution

Nonlinear regression models

Based on searching for a dependency of the target variable on other variables. Most applied in financial markets or medical diagnostics

Business Cases for the data-mining algorithms

• Market Basket Analysis

• Churn Analysis

• Market Analysis

• Forecasting

• Data Exploration

• Unsupervised learning

• Web site Analysis

• Campaign analysis

• Information quality

• Text analysis

Market Basket Analysis

To identify which items are generally purchased in the same check-out or shopping basket

Churn Analysis

To identify the patterns behind customer churn (turnover)

Market Analysis

Assist in grouping similar customers into different segments in order to better understand customer demographic

Forecasting

Allows to input past data in order to predict future values such as inventory levels or sales information

Data Exploration

Permits to explore the various components of data, analyzes profit margin of a particular product across demographic segments

Unsupervised learning

Identifies relationships between components of your business that you might not have known existed

Web site Analysis

To fully understand how customers and potential customers use your website.

Campaign analysis

Targets a marketing campaign and attempts to quantify the results (e.g. analyze how a particular product or demographic responds to a particular promo offer

Information quality

Helps clean and organize data coming into a system

Text analysis

To analyze feedback coming in from customers or clients

Online Analytical Processing (OLAP)

A type of application that allows a user to interactively analyze data

Online Analytical Processing (OLAP)

Online transaction processing that focuses on processing transactions such s orders, invoices Or general ledger transactions

OLAP Applications

• Sales and Marketing analysis

• Financial reporting and consolidation

• Budgeting and planning

• Product profitability and pricing analysis

• Activity based costing, Manpower planning

• Quality analysis

OLAP Rules

1. Multidimensional conceptual view

2. Transparency

3. Accessibility

4. Consistent reporting performance

5. Client/server architecture

6. Genetic dimensionality

7. Dynamic sparse-matrix handling

8. Multiuser support

9. Unrestricted cross-dimensional operations

10. Intuitive data manipulation

11. Flexible

12. Unlimited dimensional and aggregation levels

OLAP Key Features

• Multidimensional Views

• Calculation-Intensive capabilities

Data Analytics (DA)

is the science of examining raw data with the purpose of drawing conclusions about that information.

Data Analytics (DA)

is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories.

Process mining

• is a process management technique that allows for the analysis of business processes based on event logs.

Process mining

aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs.

Moreover, such event logs can also be used to compare event logs with some ____ model to see whether the observed reality conforms to some prescriptive or descriptive model.

Business performance management

is a set of management and analytic processes that enable the management of an organization's performance to achieve one or more pre-selected goals.

corporate performance management & enterprise performance management

Synonyms for "business performance management" include ______

Benchmarking

is the process of comparing one's business processes and performance metrics to industry bests and/or best practices from other industries.

Benchmarking Types

• Process benchmarking

• Financial benchmarking

• Benchmarking from an investor perspective

• Performance benchmarking

• Product benchmarking

• Strategic benchmarking

• Functional benchmarking

• Best-In-Class benchmarking

• Operational benchmarking

• Energy benchmarking

Text mining

sometimes alternately referred to as text data mining, roughly equivalent to text analytics

Text mining

refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.

Text mining Tasks

• text categorisation,

• text clustering

• concept/entity extractions

• production of granular taxonomies

• sentiment analysis

• documentation summarisation

• entity relation modeling

Predictive analytics

is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behaviour patterns.

Predictive analytics

relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.

Predictive models

analyse past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness.

Descriptive models

quantify relationships in data in a way that is often used to classify customers or prospects into groups.

Descriptive models

Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), _____ identify many different relationships between customers or products.

Decision models

____ are generally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.

Types of Forms of Reports

• List Reporting

• Interactive Analysis

• Ad-hoc Querying

• Metric Management

• Dashboard

• Balance Scorecards

List Reporting

The most common usage of Enterprise Reporting is the formatted displays or presentations of organizational data lists through list, text, graphics or other rendering formats for periodic business operation.

Interactive Analysis

Enterprise users needs to perform analysis upon large set of data to understand or find presentation of the data.

Ad-hoc Querying

Ability to allow advanced business users for ad-hoc i data needs and play "what-if" scenarios to determine what are the best use of enterprise data.

Metric Management

In many organizations, business performance is managed and measured through outcomeoriented metrics.

Dashboard

Another way for enterprise to consume their reporting data is publishing them into customized dashboard views, mostly hosted within enterprises' internet portal.

Balance Scorecards

A method attempts to present an integrated view of success in an organization.

Classification of Reports

• Parameterised Reports

• Linked Reports

• Snapshot Reports

• Cached Reports

• Clickthrough Reports

• Drilldown Reports

• Drillthrough Reports

• Subreport

The Role of Data Visualisation in BI

Provides a quick and effective way to communicate information in a universal manner using visual information.

Common Types of Data Visualisation

• Table

• Bar graph

• Pie Chart

Complicated techniques of Data Visualisation

• Infographics

• Bubble Clouds

• Bullet Graphs

• Heat Maps

• Time series charts

Line charts

Line charts. This is one of the most basic and common techniques used. Line charts display how variables can change over time.

Area charts

This visualization method is a variation of a line chart; it displays multiple values in a time series -- or a sequence of data collected at consecutive, equally spaced points in time.

Scatter plots

This technique displays the relationship between two variables. A scatter plot takes the form of an x- and y-axis with dots to represent data points.

Treemaps

. This method shows hierarchical data in a nested format. The size of the rectangles used for each category is proportional to its percentage of the whole. Treemaps are best used when multiple categories are present, and the goal is to compare different parts of a whole.

Population pyramids

This technique uses a stacked bar graph to display the complex social narrative of a population. It is best used when trying to display the distribution of a population.