Business Intelligence Module 3

Module 3
Business Intelligence Life cycle

Introduction
It’s no secret that Business Intelligence* (BI) projects are both time consuming
and resource intensive, often suffer from poor communication between the
business and IT, and are usually inflexible to changes once development has
started. This is due, in large part, to the method by which BI projects are
traditionally implemented. Regardless of the methodology employed, a
successful BI iteration requires:
• Business requirements identification
• Data analysis
• Data architecture and modeling
• Data integration (ETL, ELT, data virtualization, etc)
• Front-end development
• Testing and release management.

1.Business requirements identification
• Define requirements precisely
• Identify and Prioritize requirements
Eg: Swiggy - Working as a bridge between buyers and sellers using an
innovative technology platform that works as a single point of contact.
Business streategy - Not only Food delivery but also medicines, grocery,
gift shops, flower shops etc
2. Data analysis - is a process of inspecting, cleansing, transforming,
and modeling data with the goal of discovering useful information,
informing conclusions and supporting decision-making.

3. Data architecture and modeling - Data architecture is concerned with what
tools and platforms to use for storing and analyzing it while data modeling focuses
on the representation of the data. Data architecture is about the infrastructure
housing that data while Data modeling is all about the accuracy of data.
Eg:Microsoft Azure is a cloud computing service created by Microsoft for building,
testing, deploying, and managing applications and services through Microsoft-
managed data centers. It provides software as a service (SaaS), platform as a
service (PaaS) and infrastructure as a service (IaaS) and supports many different
programming languages, tools, and frameworks, including both Microsoft-specific
and third-party software and systems.
4. Data integration - refers to the technical and business processes used to
combine data from multiple sources to provide a unified, single view of the data.
• Extract, Transform and Load: copies of datasets from disparate sources are
gathered together, harmonized, and loaded into a data warehouse or database
• Extract, Load and Transform: data is loaded as is into a big data system and
transformed at a later time for particular analytics uses.

The five critical differences of ETL vs ELT:
• ETL is the Extract, Transform, and Load process for data. ELT is Extract, Load,
and Transform process for data.
• In ETL, data moves from the data source to staging into the data warehouse.
• ELT leverages the data warehouse to do basic transformations. There is no
need for data staging. (Data staging is one of the data warehousing processes
in BI. BI provides mechanisms for staging data (master data, transaction data,
metadata) from various sources. They also describe how the data can be
transferred from the sources. The extraction and transfer of data generally
takes place when requested by BI)
• ETL can help with data privacy and compliance by cleaning sensitive and
secure data even before loading into the data warehouse.
• ETL can perform sophisticated data transformations and can be more cost-
effective than ELT.

Business Intelligence Module 3

• Data virtualization - data from different systems are virtually
combined to create a unified view rather than loading data into a new
repository. It is an approach to data management that allows an
application to retrieve and manipulate data without requiring
technical details about the data, such as how it is formatted at source,
or where it is physically located, and can provide a single view of the
overall data.
• Unlike the traditional extract, transform, load ("ETL") process, the
data remains in place, and real-time access is given to the source
system for the data. This reduces the risk of data errors, of the
workload moving data around that may never be used, and it does
not attempt to impose a single data model on the data .

5. Front end development - refers to ‘client side’ development, where
is focus is on what users visually see first in their browser or
application. Front end developers are responsible for the look and feel
of the application. It uses
• HTML - HyperText Markup Language - create the structure, layout
• Cascading Style Sheets (CSS) - Stylize the website
• Javascript - Increase interactivity

6. Testing and release management - Testing verifies the staging data,
ETL process, BI reports and ensures the implementation is correct.
Release Management is the process that handles software
deployments and change initiatives. It starts with planning what will be
contained within a release, managing the software build through
different stages and environments, testing stability and finally,
deployment.

Business Intelligence Life cycle
The challenge with the way in which BI projects are usually
implemented is that the design and development steps across the
architecture aren't integrated with the inputs by the business.
In addition, since the tools usually employed for design and
development aren't integrated, both initial development and
subsequent changes require a significant level of manual effort and
change management.
If companies want to improve upon the traditional BI development
process, they need to start approaching this process as a business
driven lifecycle, as well as integrate and automate as much of the
development process as possible.

Meaning
Business intelligence lifecycle management is a design, development
and management approach to BI that incorporates business users into
the design process and focuses on generating the data models,
database objects, data integration mappings and front-end semantic
layers directly from the business user input.

Analyze Business Requirements - Review business requirements to determine the
types of analysis user need to perform.
Design Data Model - Based on the business requirements, design the logical data
model, which shows the information that users want to analyze and the
relationships that exists within the data.
Design the Physical Schema - Using the data model, design the physical schema,
which defines the content and structure of the data warehouse.
Build the Data Warehouse - Build the data warehouse according to the schema
design and load data into the warehouse from source systems.
Create the Project Structure (Metadata) - Create the metadata and begin to
connect and map the metadata to table in the data warehouse.
Develop The BI Objects - Develop object, like attribute, fact, metric, reports and
dashboard.
Administer and Maintain the Project - Administer and maintain the project as it
undergoes continued development and changes, monitor performance and make
adjustments to improve it, manage security, and perform other ongoing
administrative tasks.

EPLC framework elements
Enterprise Performance Life Cycle (EPLC) is a framework to enhance
Information Technology (IT) governance through rigorous application of
sound investment and project management principles and industry's
best practices.
Enterprise Performance Life Cycle (EPLC) Framework Elements
describes the essential elements of the EPLC framework. The life cycle
stages, stakeholders, performances of various phases and deliverables,
exit factors, and project reviews. The EPLC framework is developed to
offer the flexibility which is required to effectively handle risk, funds,
and profits, while allowing for variations in project size, difficulty,
scope, period, and approach.

The EPLC framework manages the performances, deliverables, and reviews of a
project into various life-cycle stages. The EPLC framework offers a project
management technique that direct the performances of project managers,
industry owners, essential partners, IT representatives, and other stakeholders
all through the life cycle stages of the project to make sure that a project
viewpoint is maintained while scheduling, implementing, and while performing
other procedures. Even though one of the purposes of the EPLC framework is to
regulate the project management within the project based on most excellent
practices, the framework also allocates certain methods to accommodate the
particular conditions. For example, Volume, time duration, difficulty, and
achievement strategy of each venture.
Execution of the EPLC framework in a project management is planned to
minimize the risk within various projects and along with the investment groups.
IT projects will be handled and executed in a planned way, using effective
project management customs, and certifying good participation by industry
stakeholders and technical specialists all through the project‟s life cycle.

Life Cycle Phases
The EPLC framework involves ten life-cycle stages. Below are the stages
and a brief explanation of each stage:
1. Concept: This phase recognizes the superior level industry and
functional needs which are essential to enhance the product and the
overall advantages of the planned investment. The outcome of this
phase is the agreement of group stakeholders of the initial project
benefits, scope, price, plan, performance, and a vague evaluation of
project estimates.
2. Initiation: This phase classifies the industry requirements, vague
estimation of project expenditure and plan, and fundamental business
and procedural risks. The effect of the primary stage is the resolution to
invest in a complete business case evaluation, an agreed project
contract, and initial project management schedule.

3. Planning: This phase identifies the entire development of the whole
project management schedule and modification of project price, plan
and performance baselines as required. Outcome of complete planning
stage, sufficient project planning and adequate needs are determined
to certify the development and project baselines.
4. Requirements Analysis: This phase creates thorough functional and
non-functional needs. Also it creates Requirements Traceability Matrix
(RTM) and award agreements if required. The effect of the
Requirements Analysis Phase is award of necessary agreement and
sanction of the requirements.
5. Design: This phase prepares the design document. The result of the
design stage is finishing of industry product plan and successful
completion of initial and comprehensive design reviews.

6. Development: This phase helps in developing code and other
deliverables which are essential to assemble the industry product. The
effect of the development stage is successfully completing of all coding
and related documents; user, operator, and safeguarding documentation,
and test scheduling.
7. Test: This phase does detailed testing and audit of the industry
product‟s plan, coding, and documentation. The effect of the test phase is
ending with acceptance testing and speediness for preparation and
alteration of project plan.
8. Transition: This phase performs user and operator training, establishes
focus to execute, and carry out the performance plan, including any
efficient operation. The result of the transition stage is the successful
accomplishment of complete production capacity and finishing of the
post-implementation review.

9. Operations: This phase operates and handles the production method
and carries out annual operational assessment. The result of the
operations stage is that it successfully operates the assets against present
price, plan, and performance standards.
10. Disposition: This final phase withdraws the assets when effective
analysis signifies that it is no longer economical to activate the asset. The
result of this phase is the planned and methodical decommissioning of the
industry product with proper consideration of information archiving and
safeguarding. Also it helps in relocation of information or operation of
new assets, and implementation of programs learned over the investment
life cycle phases.
In, EPLC framework lifecycle, each stakeholder plays an important role
in implementation of the EPLC framework and the accomplishment of
projects. The responsibility of every stakeholder differs all through the
life cycle.

Human factors in BI
Business intelligence will always get synchronized with business data to
take resolutions rationally. As the duration of business is growing, a
variety of business intelligence solutions are getting introduced.
Whenever any organization chooses business intelligence solutions,
they anticipate a good profit from an investment. Some organizations
turn this chance into business development and once more begin the
root cause analysis to find out the reason for not achieving it. Every
successful business intelligence execution, irrespective of the volume
and capacity, must deal with how the project is influenced by human
factors. Greatest possible procedures can be assembled and acquire
high operate software and infrastructure machineries.

Human Factors in BI Implementation refers to abilities, methodologies,
equipments, technology and customs used to enhance decision making.
Therefore a BI system is known as a decision support system. Even though
there are various aspects that could influence the execution method of a BI
system, a study reveals the below mentioned significant aspects for a
business intelligence implementation by human factors:
• Focus on business methods and needs: Frequently organizations get
focused upon methodological capabilities and pay no attention on how
the business operations to be carried out and what are the chief business
needs. Once this has been established, it is easy to get engaged in a more
efficient BI system.
• Focus on attaining a strong ROI (Return on Investment): This needs to
enlarge a sophisticated business case, setting up key performance
methods, establishing baselines and objectives for those methods, and
evaluating performance.

• Enhanced project managing and resource assurance: Make sure
there is an efficient project manager to encourage team and take part
in the project.
• Dedication from organization executive: Along with the support from
a manager, encouragement from the CEO and higher executives is
also essential.
• Take time to schedule future events: Make sure things are scheduled
in a proper way at the starting of the project rather than wasting time
to resolve the problems in future.
• Sufficient training and change in administration: Assist people to
understand and efficiently use the BI structure.

Business Intelligence strategy
A business intelligence strategy refers to all the steps undertaken in
order to implement business intelligence in company. It starts with
diving into the BI process, defining the stakeholders and main actors, to
assessing the situation, defining the goals and finding the performance
indicators that will help to measure the efforts to achieve these goals.
We define the strategy in terms of vision, organization, processes,
architecture and solutions, and then draw a roadmap based on the
assessment, the priority and the feasibility.

How To Create A Business Intelligence Strategy
• Assess the situation: analyze the organizational structure, processes and
software stack – or the absence of such. Find out what is working and what
isn’t, to save time on already functioning processes. Ask the right business
questions and define the strategic goals that company wants to achieve.
• Building the BI roadmap: It refers to establishing the steps before hitting the
road. It is to prepare ourselves for surprises and problems to handle.
• Defining your team: from the head of BI to the business analyst to the
developer, a solid team with clear roles that will be able to carry out the
different tasks are needed.
• Organizing your BI system: the data warehouse, the data sources, the
software drawing out insights… There’s a lot of thinking behind this that
shouldn’t be neglected, as it will be the central tool to navigate the data and
bring out insightful analytics.

• Get ready to hit the road - As one would say, company is now ready
to start. It has all the keys in hands to start the first step of roadmap
and launch new BI strategy.

BI Transformation process
BI Transformation Process (or Business Intelligence Transformation Process) is
an encapsulation of the depth and breadth of effort required to transform Data
Analytics and BI functions to excel in the digital age to support business
decisions and transform the company into a data-driven intelligent enterprise.
BI Transformation Approach
Value Proposition
Once the company is convinced about the value that BI brings to the
organization, they have to share that value proposition with others. The
concerned people have to “sell” the concept to the employees. It can be a
tough transition from making decisions based on instinct to making decisions
based on the analysis of data. Most companies have been successful in top-
down approach; once the board and top management are convinced, it’s easier
to convince lower level management.

Strategy
In this step that cmpany has to be clear about the following - What is company’s
vision of this system? What should BI do for the company? What are company’s
business objectives and key performance indicators (KPIs)? Many businesses share
three main KPIs: risk level, productivity, and financial value, but how to measure
those for a particular business? What are the informational needs for the entire
organization? Perhaps customer expectations are more important than income of
the company.
Plan
Once the strategy is developed, an implementation plan that is practical needs to
be developed. It is better to start small, even if you have big ideas. It is likely that a
will have limited IT staff and an even more limited budget, so developing the data
analysis and essential reporting that will maximize cost-benefit is required. Find
initial areas of business that make sense to undergo transformation. Companies
that have already implemented BI state that there are natural areas to use as
starting points, with many companies beginning with finance, logistics, and
customer relations management.

Build and Implement
Most companies use a mixture of vendors and products to build their BI system.
Better to keep it simple and think about ease of use for all people involved—
employees, customers, IT department and external partners. Ideally, groups
within the company should be able to create their own reports according to
their own unique needs, and should be able to share data across departments.
The system should be easy to configure, and it should be easy to import data
into and extract data out of the system. The data warehouse is the foundation
on which companies build other modules and reports, and the system should be
easily scalable as business changes and grows. The data interface and
presentation capabilities are key, and a majority of time should be spent on
designing the interface for various users.

Business Intelligence Transformation Drivers
What is motivating big companies to take the BI transformation
plunge? The following are the reasons and drivers as to why it makes
sense for one to transform a company’s business intelligence systems
and processes.
Business, as Usual, is a Death Knell
One of the main drivers for business intelligence transformation is the
cost of not taking the plunge. Companies that delay the transformation
do so at a significant cost. Companies without appropriate business
intelligence transformation risk competitive advantage and at times
their very survival in the fast-changing dynamics of the new economy.

Cost Factors
Maintenance of old inflexible systems is costly, and the lack of
scalability means that most companies spend money on other
applications or third party systems to make the antiquated system do
what they want the systems to do. A recent study showed that
organizations which moved to standardized BI tools had 36 percent
lower BI spend as a percentage of revenue.
There is also a hidden, more intangible cost of operational inefficiency
that comes with not having simple and consolidated BI tools for
employees, not to mention the lost opportunities of not having data
insight that can lead companies to new customers, product lines and
markets.

Constant Innovation
Today’s marketplace is one of constant innovation of products and their features.
Products, as well as new features of existing products, are released in a lightning-fast
time-to-market environment. If companies are trying to broaden sales scope, they
need better BI, because competitors are likely to have it. There is cut throat
competition, and more and more companies are turning to BI to survive in the
marketplace because it greatly reduces the time they need to react to market changes
and pressure from the competition. Smart companies also use it to reduce many risks
they face in today’s global market.
Effectiveness
Companies today have to be lean and effective, and BI dramatically increases
employees’ efficiency which in turn improves financial performance. Successful
companies are those that quickly respond in a company-wide fashion to the vastly
changing competitive marketplace. It’s not easy–in fact, it can be an onslaught.
Changes in legislation or technology, customer demand, and competitors’ actions all
require companies to be fast and nimble.

Underperformance
Many companies that have experienced declines in profits, market share and sales
have used BI transformation to turn low performance around.
Non-Traditional Thinking
Perhaps the most exciting driver, and sometimes the hardest one to follow, is that a
different way of thinking is an imperative in today’s market. We’re all used to the
traditional BI reports with historical information, which are no doubt useful. But BI
shifts the playing field by changing the kinds of questions you can ask and get
answered about your business. Traditional reports are after-the-fact: how was our
performance last quarter, and so on. With BI, there is predictive value. You can ask
questions like “What will happen if I change this part of my product line?” Companies
that are asking this type of question see the impacts that certain choices will have to
their overall strategy. They are the ones recognizing new risks as well as new niches.
The effectiveness and insight that a transformed business intelligence function
provides add tremendous value to the organization. There are many Business
intelligence transformation drivers. Irrespective of which business intelligence
transformation drivers are necessary, the key is to start BI transformation now to reap
the results.

Parallel development tracks
Every BI decision-support project has at least three development tracks
running in parallel after the project requirements have been defined
and before implementation.
1. The ETL Track
The ETL track is often referred to as the back end. The purpose of this
development track is to design and populate the BI target databases.
The ETL track is the most complicated and important track of a BI
decision-support project. The fanciest OLAP tools in the world will not
provide major benefits if the BI target databases are not designed
properly or if they are populated with dirty data. The team working on
the ETL track is usually staffed with knowledgeable business analysts,
experienced database administrators, and senior programmers.

2. The Application Track
The Application track is often referred to as the front end. The purpose
of this development track is to design and build the access and analysis
applications. After all, the key reasons for building a BI decision-support
environment are to:
- Deliver value-added information
- Provide easy, spontaneous access to the business data
The team for the Application track is usually staffed with subject matter
experts, "power users," and programmers who know Web languages,
can effectively use OLAP tools, and have experience building
client/server-based decision-support applications that incorporate
graphical user interfaces.

3. The Meta Data Repository Track
Meta data is a mandatory deliverable with every BI application. It can
no longer be shoved aside as documentation because it must serve the
business community as a navigation tool for the BI decision-support
environment. Therefore, the purpose of this development track is to
design, build, and populate a meta data repository. The team members
are responsible for designing and building the access interfaces as well
as the reporting and querying capabilities for the meta data repository.
The team working on the Meta Data Repository track is usually staffed
with a meta data administrator and developers who have experience
with building client/server-based interfaces and are knowledgeable
about Web applications.

Effective roadmap to implement successful BI
strategy

Steps in BI road map
1. Go into the process with eyes wide open
Right mindset will help to address issues like complicated data
problems, change management resistance, waning sponsorship, IT
reluctance and user adoption challenges.
2. Determine stakeholder objectives
Though everyone at the organization benefits from increased data
access and insights, determining key stakeholders are important. Then
find out what they need: visible and vocal executive sponsorship is a
must. Gathering and setting executive team expectations early is
paramount.

3. Choose a sponsor
While a business intelligence strategy should include multiple
stakeholders, it is imperative to have a sponsor to spearhead the
implementation.It should be sponsored by an executive who has
bottom-line responsibility, a broad picture of the organization’s strategy
and goals and knows how to translate the company mission into
mission focused KPIs. CFOs and CMOs are good fits. They can govern
the implementation with a documented business case and be
responsible for changes in scope.
4. BI is not just a technology initiative
To succeed, BI deployment must have the support of key business
areas. IT should be involved to ensure governance, knowledge transfer,
data integrity, and the actual implementation. But every stakeholder
and their respective business areas should also be involved throughout
the process.

5. Employ a Chief Data Officer (CDO)
CDO is required for data gathering, management, optimization, and security .
6. Assess the current situation
Usually a BI deployment isn’t quick or easy. There is a lot of work to do on the
front end. One of the biggest sections on a business intelligence roadmap
should be assessing the current situation. Once all the right stakeholders are
agreeing for BI implementation, the next step is analyzing the current software
stack, and the processes and organizational structures surrounding it. Find out
what is working and document everything that isn’t working.
7. Clean the data
Cleaning data may not be simple, but it will ensure the success of BI. It is
crucial to guarantee a solid data quality management, as it will help to maintain
the cleanest data possible for better operational activities and decision-making
made relying on that data.

8. Develop a “Data Dictionary”
For business intelligence to succeed there needs to be at least a
consensus on data definitions and business calculations. The lack of
agreement on definitions is a widespread problem in companies today.
For example, finance and sales may define “gross margin” differently,
leading to their numbers not matching.
9. Identify key performance indicators (KPIs)
KPIs are measurable values that show how effectively a company is
achieving their business objectives. They sit at the core of a good BI
strategy. KPIs indicate areas businesses are on the right track and
where improvements are needed. When implementing a BI strategy, it
is crucial to consider the company’s individual strategy and align KPIs to
company’s objectives. It may be tempting to create KPIs for everything.
It is best to start with the most important KPIs; then create standards
and governance with KPI examples in mind.

10. Choose the right tool for business
Companies need to make sure to choose a solution that can start small but
easily scale as your company and needs grow. Look for flexible solutions that
address the needs of all the user in the company.
11. Pursue a phased approach
A successful BI strategy takes an iterative approach. Think “actionable” and take
baby steps. Choose a few KPIs and build a few business dashboards as
examples. Gather feedback. Repeat again with new releases every few weeks.
Continuously check what is working and what stakeholders are benefiting.
A good BI roadmap doesn’t have an end date. Organization should be invested
in it for the long term. Companies should continually measure and refine
processes, data and reports.

BI Framework
The advantages of Business Intelligence to your organization are many.
But the trick is making BI work at the lowest possible cost. How to
make sure the right information reaches the right user, and in the right
form? And without generating an entire battery of management
dashboards and reports. How to tackle this issue smartly and without
investing a ton of money, while maintaining the proper, high quality?
BI framework seamlessly connects the various elements of a business:
organizational roles, KPIs, authorization, and visualization. This helps to
implement Business Intelligence plans both easier and faster.
A BI framework helps to structure the improvement process of
Business Intelligence. On top of that, it lets to implement BI strategy in
a very cost-effective way. Developing this framework makes high-
quality BI available at a reasonable price.

Why do you need a BI framework?
• Relevant information which is effortlessly available to whoever needs
it, at any given time.
• Information is 100% current, interactive, and fast.
• Users no longer need to struggle with complex file structures or click
through myriad tabs looking for the right report or dashboard.
Advantages
• Making a new dashboard available in just a few clicks.
• Developing new BI applications within few days.
• The ability to create new mobile BI applications quickly.
• Lower administrative costs and a lower Total Cost.
• Excellent performance due to the high rate of reuse and caching.
(Caching - data stored for future use)

The results of our Business Intelligence framework
• More effective use of Business Intelligence software by more users.
• More successful BI projects that cost less and add more value.
• More efficiency across the organization, meaning higher margins.

Business Intelligence Module 3

More Related Content

What's hot (20)

Similar to Business Intelligence Module 3 (20)

More from Home (19)

Recently uploaded (20)

Business Intelligence Module 3