UNIT II​ BUSINESS INTELLIGENCE

Data Warehouses and Data Mart – Knowledge Management – Types of Decisions –

Decision Making Process – Decision Support systems – Business Intelligence- OLAP-
Analytic Functions

1.​DATA WAREHOUSES AND DATA MART:

A Data Warehouse (DW) is an organized collection of integrated, subject oriented
databases designed to aid decision support functions. DW is organized at the right level of
granularity to provide clean enterprise-wide data in a standardized format for reports, queries
and analysis. DW is physically and functionally separate from an operational and transactional
database. Creating a DW for analysis and queries represents investment in time and effort. It
has to be constantly kept up-to-date for it to be useful.
A Data Warehousing (DW) is process for collecting and managing data from varied sources to
provide meaningful business insights. A Data warehouse is typically used to connect and
analyze business data from heterogeneous sources. The data warehouse is the core of the BI
system which is built for data analysis and reporting. It is a blend of technologies and
components which aids the strategic use of data. It is electronic storage of a large amount of
information by a business which is designed for query and analysis instead of transaction
processing. It is a process of transforming data into information and making it available to users
in a timely manner to make a difference.

Data warehouse system is also known by the following name:

•​ Decision Support System (DSS)

•​ Executive Information System
•​ Management Information System
•​ Business Intelligence Solution
•​ Analytic Application
•​ Data Warehouse
How Data warehouse works?

A Data Warehouse works as a central repository where information arrives from one or more
data sources. Data flows into a data warehouse from the transactional system and other
relational databases.

Data may be:

1.​ Structured
2.​ Semi-structured
3.​ Unstructured data

The data is processed, transformed, and ingested so that users can access the processed data in
the Data Warehouse through Business Intelligence tools, SQL clients, and spreadsheets. A data
warehouse merges information coming from different sources into one comprehensive
database.

By merging all of this information in one place, an organization can analyze its customers more
holistically. This helps to ensure that it has considered all the information available. Data
warehousing makes data mining possible. Data mining is looking for patterns in the data that
may lead to higher sales and profits.

Types of Data Warehouse

Three main types of Data Warehouses are:

1.​ Enterprise Data Warehouse:

Enterprise Data Warehouse is a centralized warehouse. It provides decision support service

across the enterprise. It offers a unified approach for organizing and representing data. It also
provides the ability to classify data according to the subject and give access according to those
divisions.

2.​ Operational Data Store:

Operational Data Store, which is also called ODS, are nothing but data store required 1.2 when
neither Data warehouse nor OLTP systems support organizations reporting needs. In ODS, Data
warehouse is refreshed in real time. Hence, it is widely preferred for routine activities like
storing records of the Employees.

3.​ Data Mart:

A data mart is a subset of the data warehouse. It specially designed for a particular line of
business, such as sales, finance, sales or finance. In an independent data mart, data can collect
directly from sources.
Components of Data warehouse

Four components of Data Warehouses are:

Load manager: Load manager is also called the front component. It performs with all the
operations associated with the extraction and load of data into the warehouse. These operations
include transformations to prepare the data for entering into the Data warehouse.

Warehouse Manager: Warehouse manager performs operations associated with the

management of the data in the warehouse. It performs operations like analysis of data to ensure
consistency, creation of indexes and views, generation of denormalization and aggregations,
transformation and merging of source data and archiving and baking-up data.

Query Manager: Query manager is also known as backend component. It performs all the
operation operations related to the management of user queries. The operations of this Data
warehouse components are direct queries to the appropriate tables for scheduling the execution
of queries.

End-user access tools:

This is categorized into five different groups like

1.​Data Reporting

2.​Query Tools

3.​Application development tools

4.​EIS tools,

5.​OLAP tools and data mining tools.

Who needs Data warehouse?

Data warehouse is needed for all types of users like:

•​ Decision makers who rely on mass amount of data
•​ Users who use customized, complex processes to obtain information from multiple data
sources.
•​ It is also used by the people who want simple technology to access the data
•​ It also essential for those people who want a systematic approach for making decisions.
•​ If the user wants fast performance on a huge amount of data which is a necessity for
reports, grids or charts, then Data warehouse proves useful.
•​ Data warehouse is a first step If you want to discover 'hidden patterns' of dataflows and
groupings.
 What Is a Data Warehouse Used For?

Here, are most common sectors where Data warehouse is used:

Airline:

In the Airline system, it is used for operation purpose like crew assignment, analyses of route
profitability, frequent flyer program promotions, etc.

Banking:

It is widely used in the banking sector to manage the resources available on desk effectively.
Few banks also used for the market research, performance analysis of the product and
operations.

Healthcare:

Healthcare sector also used Data warehouse to strategize and predict outcomes, generate
patient's treatment reports, share data with tie-in insurance companies, medical aid services, etc.

Public sector:

In the public sector, data warehouse is used for intelligence gathering. It helps government
agencies to maintain and analyze tax records, health policy records, for every individual.

Investment and Insurance sector:

In this sector, the warehouses are primarily used to analyze data patterns, customer trends, and
to track market movements.

Retail chain:

In retail chains, Data warehouse is widely used for distribution and marketing. It also helps to
track items, customer buying pattern, promotions and also used for determining pricing policy.

Telecommunication:

A data warehouse is used in this sector for product promotions, sales decisions and to make
distribution decisions.
Hospitality Industry:

This Industry utilizes warehouse services to design as well as estimate their advertising and
promotion campaigns where they want to target clients based on their feedback and travel
patterns.

Steps to Implement Data Warehouse

The best way to address the business risk associated with a Data warehouse implementation is
to employ a three-prong strategy as below

1.​ Enterprise strategy: Here we identify technical including current architecture and
tools. We also identify facts, dimensions, and attributes. Data mapping and transformation is
also passed.
2.​ Phased delivery: Datawarehouse implementation should be phased based on subject
areas. Related business entities like booking and billing should be first implemented and then
integrated with each other.
3.​ Iterative Prototyping: Rather than a big bang approach to implementation, the
Datawarehouse should be developed and tested iteratively.

Best practices to implement a Data Warehouse

•​ Decide a plan to test the consistency, accuracy, and integrity of the data.
•​ The data warehouse must be well integrated, well defined and time stamped.
•​ While designing Datawarehouse make sure you use right tool, stick to life cycle, take care
about data conflicts and ready to learn you're your mistakes.
•​ Never replace operational systems and reports
•​ Don't spend too much time on extracting, cleaning and loading data.

Step Tasks Deliverables

1 Need to define project scope Scope Definition

2 Need to determine business needs Logical Data Model

4 Acquire or develop Extraction tools Extract tools and Software

5 Define Data Warehouse Data requirements Transition Data Model

6 Document missing data To Do Project List

7 Maps Operational Data Store to Data D/W Data Integration Map

Warehouse

8 Develop Data Warehouse Database design D/W Database Design

9 Extract Data from Operational Data Store Integrated D/W Data Extracts

10 Load Data Warehouse Initial Data Load

•​ Ensure to involve all stakeholders including business personnel in Datawarehouse

implementation process. Establish that Data warehousing is a joint/ team project. You don't
want to create Data warehouse that is not useful to the end users. Prepare a training plan for the
end users.

Advantages of Data Warehouse:

•​ Data warehouse allows business users to quickly access critical data from some sources
all in one place.
•​ Data warehouse provides consistent information on various cross-functional activities. It
is also supporting ad-hoc reporting and query.
•​ Data Warehouse helps to integrate many sources of data to reduce stress on the
production system.
•​ Data warehouse helps to reduce total turnaround time for analysis and reporting.
•​ Restructuring and Integration make it easier for the user to use for reporting and analysis.
•​ Data warehouse allows users to access critical data from the number of sources in a
single place. Therefore, it saves user's time of retrieving data from multiple sources.
•​ Data warehouse stores a large amount of historical data. This helps users to analyze
different time periods and trends to make future predictions.
Disadvantages of Data Warehouse:

•​ Not an ideal option for unstructured data.

•​ Creation and Implementation of Data Warehouse is surely time confusing affair.
•​ Data Warehouse can be outdated relatively quickly
•​ Difficult to make changes in data types and ranges, data source schema, indexes, and
queries.
•​ The data warehouse may seem easy, but actually, it is too complex for the average users.
•​ Despite best efforts at project management, data warehousing project scope will always
increase.
•​ Sometime warehouse users will develop different business rules.
•​ Organizations need to spend lots of their resources for training and Implementation
purpose.

The Future of Data Warehousing

•​ Change in Regulatory constrains may limit the ability to combine source of disparate
data. These disparate sources may include unstructured data which is difficult to store.
•​ As the size of the databases grows, the estimates of what constitutes a very large database
continue to grow. It is complex to build and run data warehouse systems which are always
increasing in size. The hardware and software resources are available today do not allow to
keep a large amount of data online.
•​ Multimedia data cannot be easily manipulated as text data, whereas textual information
can be retrieved by the relational software available today. This could be a research
subject.
•​ retrieved by the relational software available today. This could be a research subject.

Data Warehouse Tools

There are many Data Warehousing tools are available in the market. Here, are some most
prominent one:

1.​ Mark Logic:

MarkLogic is useful data warehousing solution that makes data integration easier and
faster using an array of enterprise features. This tool helps to perform very complex search
operations. It can query different types of data like documents, relationships, and metadata.

2.​ Oracle:

Oracle is the industry-leading database. It offers a wide range of choice of data warehouse
solutions for both on-premises and in the cloud. It helps to optimize customer experiences
by increasing operational efficiency.
3.​ Amazon RedShift:

Amazon Redshift is Data warehouse tool. It is a simple and cost-effective tool to analyze
all types of data using standard SQL and existing BI tools. It also allows running complex
queries against petabytes of structured data, using the technique of query optimization.

Differences between Data Warehouse and Data Mart

 KNOWLEDGE MANAGEMENT (KM): CONCEPT, FEATURES AND
PROCESS
Concept of KM:
KM may be defined as follows:
Knowledge management is a process of acquiring, generating, accumulating and using
knowledge for the benefit of the organisation to enable it to gain a competitive edge for
survival, growth and prosperity in a globalized competitive economy.
According to some management experts, notably Peter F. Drucker, KM is a bad term; in as
much as knowledge cannot be managed.
Rather, KM requires conditions for the emergence of a learning organisation; which is
necessary for generation, sharing and use of knowledge residing in the minds of people.
Features of Knowledge Management
Some salient features of KM are described below:
(i)​ KM is a systematic process; consisting of standardized procedures to collect, store,
distribute and use knowledge. The essence of KM is to get right knowledge to right people,
at the right time.
(ii)​ Knowledge is of two types – explicit and implicit. Explicit knowledge is visible
information available in literature, reports, patents, technical specifications,
communication with customers, suppliers, competitors etc. It can be embedded in rules,
systems, policies and procedures etc. of the organization.
Tacit or implicit knowledge is personal knowledge residing in the minds of people as a
result of their personal beliefs, values, perspectives and experience. There is a need for a
learning organization for enhancement, sharing and utilization of tacit knowledge.
(iii)​KM is a continuous process; as the world economy is dynamic and full of challenges. It
requires constant creation of new skills and capabilities and improvement of existing ones.
(iv)​ KM requires whole-hearted support of top management, to provide cultural and technical
foundation for the origination and implementation of KM practices.
(v)​ The objective of KM is improvement in organizational performance; to enable the
organization acquire, sharpen and utilize its competitive edge for survival and growth in
the global economy of today.

Knowledge Management and Information Technology:

KM is not an outgrowth of IT. Rather, KM requires human skills, creativity and innovative
capabilities of people; which are the base of KM. In fact I there are tools of IT like Intranets,
Lotus Notes, MS-Exchange etc.; which provide an infrastructure for the free play of human
creativity and innovative powers for the formulation of corporation strategy, in a competitive
globalized environment.

The​ above​ ideas​ are​ illustrated​ with​ the​ help​ of​ the​ following
diagram:
Knowledge Management IT and Corporate Strategy
An Overview of the Process of KM:
KM broadly consists of the following major steps:
(i)​ Identification of Knowledge Needs:
The first step in KM is an identification of what type of knowledge is required for the
successful designing and implementation of corporate strategy.
(ii)​ Determination of Knowledge Assets:
The management must identify what are the knowledge assets of the organisation; which
basically are competitors, suppliers, governmental agencies, products and processes,
technology etc. Management must plan to get maximum returns out of knowledge assets.

(iii)​ Generation of Knowledge:

Generation of knowledge requires two sources:
(a)​ Acquisition of knowledge through knowledge assets e.g. knowledge about new products
(from competitors), new technologies, social, economic, political changes. It also requires
transformation of raw information into knowledge, useful to solve business problems.
(b)​ Generation of knowledge, by creating conditions for the emergence of a learning
organization. This is the most important internal source of knowledge generation which makes
tacit knowledge of individuals available for organizational purposes.
(iv)​Knowledge Storage:
It includes preserving existing and acquired knowledge in knowledge repositories. (A
knowledge repository is an on line computer based storehouse of organised information about a
particular domain of knowledge).
(v)​ Knowledge Distribution:
It is a process which allows members of the organisation to have an access to the collective
knowledge of the organisation.
(vi)​Knowledge Utilization:
It requires embedding knowledge in products, processes, procedures etc. of the organization.
Best utilization of knowledge takes place when managers utilize knowledge in organizational
decision making. A learning organization creates conditions for sharing and utilizing
knowledge in organizational contexts.
(vii)​ Feedback on Knowledge Management
Feedback on KM implies evaluating the significance of knowledge assets. It also includes
impact of KM on organizational performance; and devising techniques for betterment of KM
in future.
An overview of the process of KM- at a glance

Significance of Knowledge Management

Significance of KM could be highlighted with reference the following advantages which KM
provides to the organisation:

(i)​ Building and Sharpening Competitive Edge:

KM enables a corporation to build and sharpen its competitive edge, for survival and growth in
the competitive globalized economy. In fact, KM aided by IT tools enables a corporation to
design and implement most appropriate corporate strategies.
(ii)​ Betterment of Human Relations:
KM is basically built on the knowledge generated, shared and utilized through a learning
organisation. There is no doubt that learning organisation provides the foundation on which the
building of KM could be built. A learning organisation through facilitating interaction among
people of the organisation, leads to betterment of human relations; which is a very big
permanent asset an organisation can boast of to possess.
(iii)​Improvement in Organisational Efficiency:
KM provides knowledge which can be embedded in organizational processes. It makes
knowledge available for decision-making purposes. Thus it helps to improve organizational
efficiency, resulting in reduced costs and increased profits, for the organization.
(iv)​Enhancement of Human Capital Capabilities
KM-its concept and practices – motivate people to enhance their intellectual capabilities,
resulting in new skills, improvement of existing skills etc. Thus not only does KM enhance the
intellectual elements of people; but also indirectly prevents depreciation of human capital.
(v)​ Enhancement of Enterprise Goodwill:
Initiation and practices of KM help an enterprise enhance its goodwill in the global market;
enabling it to acquire more success and prosperity.
 TYPES OF DECISIONS IN BUSINESS INTELLIGENCE
The characteristics of decisions faced by managers at different levels are quite different.
Decisions can be classified as structured, semi structured, and unstructured. Unstructured
decisions are those in which the decision maker must provide judgment, evaluation, and
insights into the problem definition. Each of these decisions is novel, important, and
nonroutine, and there is no well-understood or agreed-on procedure for making them.

Structured decisions, by contrast, are repetitive and routine, and decision makers can
follow a definite procedure for handling them to be efficient. Many decisions have elements of
both and are considered semi structured decisions, in which only part of the problem has a
clear-cut answer provided by an accepted procedure. In general, structured decisions are made
more prevalently at lower organizational levels, whereas unstructured decision making is more
common at higher levels of the firm.

Senior executives tend to be exposed to many unstructured decision situations that are
open ended and evaluative and that require insight based on many sources of information and
personal experience. For example, a CEO in today’s music industry might ask, “Whom should
we choose as a distribution partner for our online music catalog— Apple, Microsoft, or Sony?”
Answering this question would require access to news, government reports, and industry views
as well as high-level summaries of firm performance. However, the answer would also require
senior managers to use their own best judgment and poll other managers for their opinions.
Middle management and operational management tend to face more structured decision
scenarios, but their decisions may include unstructured components. A typical middle level
management decision might be “Why is the order fulfillment report showing a decline over the
last six months at a distribution center in Minneapolis?” This middle manager could obtain a
report from the firm’s enterprise system or distribution management system on order activity
and operational efficiency at the Minneapolis distribution center. This is the structured part of
the decision. But before arriving at an answer, this middle manager will have to interview
employees and gather more unstructured information from external sources about local
economic conditions or sales trends. Rank-and-file employees tend to make more structured
decisions. For example, a sales account representative often has to make decisions about
extending credit to customers by consulting the firm’s customer database that contains credit
information. In this case the decision is highly structured, it is a routine decision made
thousands of times each day in most firms, and the answer has been preprogrammed into a
corporate risk management or credit reporting system.
The types of decisions faced by project teams cannot be classified neatly by
organizational level. Teams are small groups of middle and operational managers and perhaps
employees assigned specific tasks that may last a few months to a few years.
Their tasks may involve unstructured or semi structured decisions such as designing
new products, devising new ways to enter the marketplace, or reorganizing sales territories and
compensation systems.
 SYSTEMS FOR DECISION SUPPORT

There are four kinds of systems used to support the different levels. We introduced some of
these systems in Management information systems (MIS) provide routine reports and
summaries of transaction- level data to middle and operational-level managers to provide
answers to structured and semi structured decision problems.
1.​ Decision-support systems (DSS) are targeted systems that combine analytical models
with operational data and supportive interactive queries and analysis for middle managers who
face semi structured decision situations.
2.​ Executive support systems (ESS) are specialized systems that provide senior
management making primarily unstructured decisions with a broad array of both external
information (news, stock analyses, industry trends) and high-level summaries of firm
performance. The purpose of ESS to help the C- level managers to focus on the information
that really affect the overall profitability and success of the firm. The leading methodology for
understanding the really important information needed by the firm’s executive is called the
Balanced Score Card Method, a frame work for operationalizing the firm’s strategic plan by
focusing on measurable outcomes on four dimensions of firm performance. Financial, business
process, customer, learning and growth. Performance on each dimension is measured using
KPI’s.
3.​ Group decision-support systems (GDSS) are specialized systems that provide a group
electronic environment in which managers and teams can collectively make decisions and
design solutions for unstructured and semi structured problems. GDSS guided meetings takes
place in a conference room with special software and hardware tools to facilitate group decision
making. It makes possible to increase the meeting size and increase in productivity. Because
individuals contribute simultaneously at the same time rather than one at a time.

STAGES IN THE DECISION-MAKING PROCESS

Making decisions consists of several different activities. Simon (1960) describes four different
stages in decision making: intelligence, design, choice, and implementation
 The decision-making process can be described in four steps that follow one another in a
logical order. In reality, decision makers frequently circle back to reconsider the previous stages
and through a process of iteration eventually arrive at a solution that is workable.

Intelligence consists of discovering, identifying, and understanding the problems

occurring in the organization—why is there a problem, where, and what effects is it having on
the firm. Traditional MIS that deliver a wide variety of detailed information can help identify
problems, especially if the systems report exceptions.

Design involves identifying and exploring various solutions to the problem. Decision
support systems (DSS) are ideal in this stage for exploring alternatives because they possess
analytical tools for modeling data, enabling users to explore various options quickly.

Choice consists of choosing among solution alternatives. Here, DSS with access
extensive firm data can help managers choose the optimal solution. Also group decision
support systems can be used to bring groups of managers together in an electronic online
environment to discuss different solutions and make a choice.

Implementation involves making the chosen alternative work and continuing

to monitor how well the solution is working. Here, traditional MIS come back into play by
providing managers with routine reports on the progress of a specific solution. Support systems
can range from full-blown MIS to much smaller systems, as well as project- planning software
operating on personal computers.

In the real world, the stages of decision making described here do not necessarily follow
a linear path. You can be in the process of implementing a decision, only to discover that your
solution is not working. In such cases, you will be forced to repeat the design, choice, or
perhaps even the intelligence stage.

For instance, in the face of declining sales, a sales management team may strongly
support a new sales incentive system to spur the sales force on to greater effort. If paying the
sales force, a higher commission for making more sales does not produce sales increases,
managers would need to investigate whether the problem stems from poor product design,
inadequate customer support, or a host of other causes, none of which would be “solved” by a
new incentive system.
Trends in Decision Support and Business Intelligence

Systems supporting management decision making originated in the early 1960s as early MIS
that created fixed, inflexible paper-based reports and distributed them to managers on a routine
schedule. In the 1970s, the first DSS emerged as standalone applications with limited data and a
few analytic models. ESS emerged during the 1980s to give senior
managers an overview of corporate operations. Early ESS were expensive, based on custom
technology, and suffered from limited data and flexibility.

The rise of client/server computing, the Internet, and Web technologies has made a major
impact on systems that support decision making. Many decision-support applications are now
delivered over corporate intranets. We see six major trends:

​ Detailed enterprise-wide data. Enterprise systems create an explosion in firmwide, current,

and relatively accurate information, supplying end users at their desktops with powerful
analytic tools for analyzing and visualizing data.
​ Broadening decision rights and responsibilities. As information becomes more
widespread throughout the corporation, it is possible to reduce levels of hierarchy and grant
more decision-making authority to lower-level employees.
​ Intranets and portals. Intranet technologies create global, company-wide networks that
ease the flow of information across divisions and regions and delivery of near real- time data
to management and employee desktops.
​ Personalization and customization of information. Web portal technologies provide great
flexibility in determining what data each employee and manager sees on his or her desktop.
Personalization of decision information can speed up decision making by enabling users to
filter out irrelevant information.
​ Extranets and collaborative commerce. Internet and Web technologies permit suppliers
and logistics partners to access firm enterprise data and decision-support tools and work
collaboratively with the firm.
​ Team support tools. Web-based collaboration and meeting tools enable project teams, task
forces, and small groups to meet online using corporate intranets or extranets. These new
collaboration tools borrow from earlier GDSS and are used for both brainstorming and
decision sessions.

BUSINESS INTELLIGENCE

Business intelligence combines business analytics, data mining, data visualization, data

comprehensive view of your organization’s data and use that data to drive change, eliminate
inefficiencies, and quickly adapt to market or supply changes. Modern BI solutions prioritize
flexible self-service analysis, governed data on trusted platforms, empowered business users,
and speed to insight
Business Intelligence is a set of processes, architectures, and technologies that convert raw data
into meaningful information that drives profitable business actions. It is a suite of software and
services to transform data into actionable intelligence and knowledge.

BI has a direct impact on organization’s strategic, tactical and operational business decisions.
BI supports fact-based decision making using historical data rather than assumptions and gut
feeling.
BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and
charts to provide users with detailed intelligence about the nature of the business.

Why is BI important?

•​ Measurement: creating KPI (Key Performance Indicators) based on historic data Identify
and set benchmarks for varied processes.
•​ With BI systems organizations can identify market trends and spot business problems that
need to be addressed.
•​ BI helps on data visualization that enhances the data quality and thereby the quality of
decision making.
•​ BI systems can be used not just by enterprises but SME (Small and Medium Enterprises)

How Business Intelligence systems are implemented?

step 1) Raw Data from corporate databases is extracted. The data could be spread across
​ multiple systems heterogeneous systems.
step 2) The data is cleaned and transformed into the data warehouse. The table can be
linked, and data cubes are formed.

Step 3) Using BI system the user can ask quires, request ad-hoc reports or conduct any other
analysis.

Examples of Business Intelligence System used in Practice Example

1:

In an Online Transaction Processing (OLTP) system information that could be fed into product
database could be

•​ add a product line

•​ change a product price

Correspondingly, in a Business Intelligence system query that would beexecuted for the
product subject area could be did the addition of new product line or change in product price
increase revenues

In an advertising database of OLTP system query that could be executed

•​Changed in advertisement options

•​Increase radio budget

Correspondingly, in BI system query that could be executed would be how many new clients
added due to change in radio budget
In OLTP system dealing with customer demographic data bases data that could be fed would be

•​ increase customer credit limit

•​ change in customer salary level

Correspondingly in the OLAP system query that could be executed would be can customer
profile changes support support higher product price

Example 2:

A hotel owner uses BI analytical applications to gather statistical information regarding average
occupancy and room rate. It helps to find aggregate revenue generated per room.

It also collects statistics on market share and data from customer surveys from each hotel to
decides its competitive position in various markets.

By analyzing these trends year by year, month by month and day by day helps management to
offer discounts on room rentals.

Example 3:

A bank gives branch managers access to BI applications. It helps branch manager to determine
who are the most profitable customers and which customers they should work on.

The use of BI tools frees information technology staff from the task of generating analytical
reports for the departments. It also gives department personnel access to a richer data source.

Four types of BI users

Following given are the four key players who are used Business Intelligence System:

1.​ The Professional Data Analyst:

The data analyst is a statistician who always needs to drill deep down into data. BI system
helps them to get fresh insights to develop unique business strategies.

2.​ The IT users:

The IT user also plays a dominant role in maintaining the BI infrastructure.

3.​ The head of the company:

CEO or CXO can increase the profit of their business by improving operational efficiency in
their business.
4.​ The Business Users”

Business intelligence users can be found from across the organization. There are mainly two
types of business users

1.​ Casual business intelligence user

2.​ The power user.

The difference between both of them is that a power user has the capability of working with
complex data sets, while the casual user need will make him use dashboards to evaluate
predefined sets of data.

Advantages of Business Intelligence

Here are some of the advantages of using Business Intelligence System:

1.​ Boost productivity

With a BI program, It is possible for businesses to create reports with a single click thus saves
lots of time and resources. It also allows employees to be more productive on their tasks.

2.​ To improve visibility

BI also helps to improve the visibility of these processes and make it possible to identify any
areas which need attention.

3.​ Fix Accountability

BI system assigns accountability in the organization as there must be someone who should
own accountability and ownership for the organization’s performance against its set goals.

4.​ It gives a bird’s eye view:

BI system also helps organizations as decision makers get an overall bird’s eye view
through typical BI features like dashboards and scorecards.

5.​ It streamlines business processes:

BI takes out all complexity associated with business processes. It also automates analytics by
offering predictive analysis, computer modeling, benchmarking and other methodologies.
6.​ It allows for easy analytics.

BI software has democratized its usage, allowing even nontechnical or non-analysts users to
collect and process data quickly. This also allows putting the power of analytics from the
hand’s many people.

BI System Disadvantages
1.​ Cost:

Business intelligence can prove costly for small as well as for medium-sized enterprises. The
use of such type of system may be expensive for routine business transactions.

2.​ Complexity:

Another drawback of BI is its complexity in implementation of datawarehouse. It can be so

complex that it can make business techniques rigid to deal with.

3.​ Limited use

Like all improved technologies, BI was first established keeping in consideration the buying
competence of rich firms. Therefore, BI system is yet not affordable for many small and
medium size companies.

4.​ Time Consuming Implementation

It takes almost one and half year for data warehousing system to be completely implemented.
Therefore, it is a time-consuming process.

OLAP (ONLINE ANALYTICAL PROCESSING)

OLAP (online analytical processing) is a computing method that enables users to easily and
selectively extract and query data in order to analyze it from different points of view. OLAP
business intelligence queries often aid in trends analysis, financial reporting, sales forecasting,
budgeting and other planning purposes. For example, a user can request that data be analyzed to
display a spreadsheet showing all of a company's beach ball products sold in Florida in the
month of July, compare revenue figures with those for the same products in September and then
see a comparison of other product sales in Florida in the same time period.
How OLAP systems work
​ To facilitate this kind of analysis, data is collected from multiple data sources and
stored in data warehouses then cleansed and organized into data cubes.
​ Each OLAP cube contains data categorized by dimensions (such as customers,
geographic sales region and time period) derived by dimensional tables in the data
warehouses.
 ​ Dimensions are then populated by members (such as customer names, countries and
months) that are organized hierarchically. OLAP cubes are often pre-summarized
​ across dimensions to drastically improve query time over relational databases.
Types of OLAP Servers
We have four types of OLAP servers −
​ Relational OLAP (ROLAP)
​ Multidimensional OLAP (MOLAP)
​ Hybrid OLAP (HOLAP)
​ Specialized SQL Servers
1.​Relational OLAP
ROLAP servers are placed between relational back-end server and client front-end tools. To
store and manage warehouse data, ROLAP uses relational or extended-relational DBMS.
ROLAP includes the following −
​ Implementation of aggregation navigation logic.
​ Optimization for each DBMS back end.
​ Additional tools and services.
2.​Multidimensional OLAP
MOLAP uses array-based multidimensional storage engines for multidimensional views of
data. With multidimensional data stores, the storage utilization may be low if the data set is
sparse. Therefore, many MOLAP server use two levels of data storage presentation to handle
dense and sparse data sets.
3.​Hybrid OLAP
Hybrid OLAP is a combination of both ROLAP and MOLAP. It offers higher scalability of
ROLAP and faster computation of MOLAP. HOLAP servers allows to store the large data
volumes of detailed information. The aggregations are stored separately in MOLAP store.
4.​Specialized SQL Servers
Specialized SQL servers provide advanced query language and query processing support for
SQL queries over star and snowflake schemas in a read-only environment.
OLAP Operations
Since OLAP servers are based on multidimensional view of data, OLAP operations in
multidimensional data.
Here is the list of OLAP operations −
​ Roll-up
​ Drill-down
​ Slice and dice
​ Pivot (rotate)
 ​ Roll-up. Also known as consolidation, or drill-up, this operation summarizes the
data along the dimension.
​ Drill-down. This allows analysts to navigate deeper among the dimensions of data,
for example drilling down from "time period" to "years" and "months" to chart
sales growth for a product.
​ Slice. This enables an analyst to take one level of information for display, such as
"sales in 2017."
​ Dice. This allows an analyst to select data from multiple dimensions to analyze,
such as "sales of blue beach balls in Iowa in 2017."
​ Pivot. Analysts can gain a new view of data by rotating the data axes of the cube.

OLAP software then locates the intersection of dimensions, such as all products sold in the
Eastern region above a certain price during a certain time period, and displays them. The result
is the "measure"; each OLAP cube has at least one to perhaps hundreds of measures, which are
derived from information stored in fact tables in the data warehouse.
OLAP begins with data accumulated from multiple sources and stored in a data warehouse. The
data is then cleansed and stored in OLAP cubes, which users run queries against.

​ Association Rule Mining

Association analysis is useful for discovering interesting relationships hidden in large data sets.
The uncovered relationships can be represented in the form of association rules or sets of
frequent items. Given a set of transactions, find rules that will predict the occurrence of an item
based on the occurrences of other items in the transaction Market- Basket transactions
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3.Bread, Milk, Diaper, Coke
Implication means co-occurrence, not causality! Example of Association Rules
{Beer}-{Diaper}
{Milk, Bread}-{Eggs, Coke}
{Beer, Bread}-{Milk}
Support Count ( ) – Frequency of occurrence of a item set. Here
({Milk, Bread, Diaper})=2
Frequent Item set – An item set whose support is greater than or equal to minus threshold.
Association Rule – An implication expression of the form X -> Y, where X and Y are any
2 item sets.
Example: {Milk, Diaper}->{Beer}
Rule Evaluation Metrics –
Support(s) –
The number of transactions that include items in the {X} and {Y} parts of the rule as
percentage of the total number of transaction. It is a measure of how frequently the collection of
items occurs together as a percentage of all transactions.
Support = (X+Y) ÷ total –
It is interpreted as fraction of transactions that contain both X and Y. Confidence(c) – It is the
ratio of the no of transactions that includes all items in {B} as well as the no of transactions that
includes all items in {A} to the no of transactions that includes all items in
{A}.
Conf(X=>Y) = Supp(X Y) ÷ Supp(X) –
It measures how often each item in Y appears in transactions that contains items in X
also.
Lift (l) –The lift of the rule X=>Y is the confidence of the rule divided by the expected
confidence, assuming that the item sets X and Y are independent of each other. The expected
confidence is the confidence divided by the frequency of {Y}.
Lift(X=>Y) = Conf(X=>Y) ÷ Supp(Y) –
Lift value near 1 indicates X and Y almost often appear together as expected, greater than 1
means they appear together more than expected and less than 1 means they appear less than
expected. Greater lift values indicate stronger association
​ Example – From the above table, {Milk, Diaper}=>{Beer}
s= ({Milk, Diaper, Beer}) ÷|T|
= 2/5
= 0.4
 ​ c= (Milk, Diaper, Beer) ÷(Milk, Diaper)
= 2/3
= 0.67
​ l= Supp({Milk, Diaper, Beer}) ÷Supp({Milk, Diaper})*Supp({Beer})
= 0.4/ (0.6*0.6)
= 1.11
The Association rule is very useful in analyzing datasets. The data is collected using bar- code
scanners in supermarkets. Such databases consist of a large number of transaction records
which list all items bought by a customer on a single purchase. So the manager could know if
certain groups of items are consistently purchased together and use this data for adjusting store
layouts, cross-selling, promotions based on statistics.

ANALYTIC FUNCTION
Analytic Functions is defined as a function that is locally given by the convergent
power series. The analytic function is classified into two different types, such as real analytic
function and complex analytic function. Both the real and complex analytic functions are
infinitely differentiable. Generally, the complex analytic function holds some properties that do
not generally hold for real analytic function.

What is Analytic Function?

A function is said to be an analytic function if and only if its Taylor series about x0
converges to the function in some neighbourhood for every x0 in its domain.

Types of Analytic Function

Analytic Functions can be categorised into two different types, which are similar in some ways,
but it has some different characteristics. The two types of analytic functions are:

●​ Real Analytic Function

●​ Complex Analytic Function

Real Analytic Function

A function “f” is said to be a real analytic function on the open set D in the real line if for any
x0 ∈ D, then we can write:
where the coeffienets a0, a1, a2, … are the real numbers and also the series is convergent to
the function f(x) for x in the neighbourhood of x0.

In other words, the real analytic function is defined as an infinitely differentiable function, such
that the Taylor series at any point x0 in its domain converges to the function f(x) for x in a
neighbourhood of x0 pointwise.

The collection of all the real analytic function on a given set D is represented by Cω (D).

Complex Analytic Function

A function is said to be a complex analytic function if and only if it is holomorphic. It means

that the function is complex differentiable.

Properties of Analytic Function

●​ The limit of a uniformly convergent sequence of analytic functions is also an analytic

function
●​ If f(z) and g(z) are analytic functions on U, then their sum f(z) + g(z) and product f(z).g(z)
are also analytic
●​ If f(z) and g(z) are the two analytic functions and f(z) is in the domain of g for all z, then
their composite g(f(z)) is also an analytic function.
●​ The function f(z) = 1/z (z≠0) is analytic
●​ Bounded entire functions are constant functions
●​ Every nonconstant polynomial p(z) has a root. That is, there exists some z0 such that p(z0) =
0.
●​ If f(z) is an analytic function, which is defined on U, then its modulus of the function |f(z)|
cannot attains its maximum in U.
●​ The zeros of an analytic function, say f(z) are the isolated points unless f(z) is identically
zero
●​ If F(z) is an analytic function and if C is a curve connecting two points z0 and z1 in the
domain of f(z), then ∫C F’(z) = F(z1) – F(z0)
●​ If f(z) is an analytic function defined on a disk D, then there is an analytic function F(z)
defined on D such that F′(z) = f(z), called a primitive of f(z), and, as a consequence, ∫C f(z)
dz =0; for any closed curve C in D.
●​ If f(z) is an analytic function and if z0 is any point in the domain U of f(z), then the function,
[f(z)-f(z0)]/[z – z0] is analytic on U as well.
●​ If f(z) is an analytic function on a disk D, z0 is a point in the interior of D, C is a closed
curve not passing through z0, then W = (C, z0)f(z0) = (1/2π i)∫C [f(z)]/[z – z0]dz, where
W(C; z0) is the winding number of C around z