bi an endless story
TRANSCRIPT
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Business IntelligenceAn Endless StoryA White Paper
This white paper focuses on reasons on why BI, an strategic initiative by which
organizations measure and drive the effectiveness of their competitive strategy is
an ongoing activity.
2011
MAIA Intelligence
July 2011
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Contents
1. Executive Summary ................................................................................. 3
2. BI Project Life Cycle ................................................................................. 4
3. Why BI projects never end? .................................................................... 8
4. Conclusion ............................................................................................. 12
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1. Executive SummaryBusiness Intelligence (BI) is a strategic initiative by which organizations measure
and drive the effectiveness of their competitive strategy. In achieving this grand
goal, there is need for analyze, software, resources, technical leadership,
process leadership, executive champions and much more. It is a long term
process and it can be broken down to goals, which are periodically analyzed for
a good manage of resources and growth. It becomes difficult for anyone to
comprehend where the BI project is heeded or when the project will finally end.
Complexities related to BI project are numerous and come to fore only once the
project is in process. BI projects have always been in progressive mode. Each up-
gradation in its maturity level has had its share of problems. And to remove
those problems, BI project have been endless in its journey. Advancement in
technologies, Request for new Key performance indexes (KPIs), Complexities of
multiple interlinked systems and various other factors keep the BI project from
ending. Let us look at the life cycle of a BI project and study the advancement in
its maturity level and analyze factors which keep on extending the timeline of BI
project.
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2. BI Project Life CycleThe Project Life Cycle refers to a logical sequence of activities to accomplish the
projects goals or objectives. Regardless of scope or complexity, any project
goes through a series of stages during its life. There is first an Initiation or Birth
phase, in which the outputs and critical success factors are defined, followed bya Planning phase, characterized by breaking down the project into smaller
parts/tasks, an Execution phase, in which the project plan is executed, and lastly
a Closure or Exit phase, that marks the completion of the project. Like any other
project, BI project too has a lifecycle. Let us understand a BI project lifecycle and
its associated complexities. Various stages of BI project lifecycle are:
Business Case Assessment Enterprise Infrastructure Evaluation Project Planning Project Requirement Definition Data Analysis Application Prototyping Metadata Repository Analysis Database Design ETL Design Metadata Repository Design ETL Development Application development Data Mining Metadata Repository Development Implementation Release Evaluation
http://www.visitask.com/project-initiation-phase.asphttp://www.visitask.com/project-management-planning-phase.asphttp://www.visitask.com/project-management-planning-phase.asphttp://www.visitask.com/project-initiation-phase.asp -
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1. Business Case Assessment:It includes lot of activities such as ROI, Cost benefit analysis, Risk assessment
etc. There is no straightforward way to calculate ROI to justify the value of BI.
A lot of the justification of an enterprise data warehouse really is based on
someone at a very high level being able to conceptualize and envision the value
of something that doesn't exist. You are dealing with things like the value of
providing better service to customers and the value of people making better
decisions faster.
- Frank Brooks, senior manager of data resourcemanagement and chief data architect,
BlueCross BlueShield of Tennessee
Most of the benefits achieved from BI are intangible benefits of strategic value
such as faster reporting, better management information, better decision
making, and more productive users etc which are tough to convert in figurative
format. Most of the times executive get stuck in trying to quantify intangible
benefits to approve BI projects from heads increasing wastage of time in 1ststep itself.
2. Enterprise Infrastructure Evaluation:It involves Technical Infrastructure evaluation and Non-technical
infrastructure evaluation. Technical infrastructure evaluation requires
examining current hardware, middleware and DBMS platforms. Non-
Technical infrastructure evaluation involves enterprise architecture and
standards. Activities such as are the hardware, middleware and DBMS
platforms compatible with new technology, would they require changes, can
they be integrated with new technology etc. But technical evaluation is
always easier than non-technical evaluation. Non-technical evaluationrequires interaction and inputs from business heads. Non-technical
evaluation is difficult to gauge until tool is used.
3. Project Planning:It involves defining and planning the BI/data warehouse project i.e.
identification of all stakeholders, defining stakeholders matrix, extensive
documentation of risks, full scope baseline development with explorations of
alternative means of delivering the project scope, work based schedules,
broadly developing timelines, human resource staffing , acquisition and team
development plans. Also includes estimating cost.
4. Project Requirement definition: It requires analyzing and documentinggeneral business requirement, project specific requirement, and project
requirement definition activities. Business analyst needs to run their
creativity and imagination and create different scenarios while drafting
requirement documents. It requires also carrying out data gathering activities
like interviewing business users, reviewing documents related to old system.
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5. Data Analysis:It involves Business focused data analysis, top-down logical data modelling,
bottom up source data analysis, data cleansing, data analysis activities etc.
Top down logical data modelling involves integrating logical data model
which is fully normalized and populated with key business attributes. Bottom
up data modelling technique involves validating and mapping source data
into logical data model, finding dirty data in source files and normalize it.
Data cleansing or data scrubbing is the process of detecting and correcting
corrupt or inaccurate records from record set, table or database(source:
Wikipedia).
6. Application Prototyping:It involves activities related to creating a prototype of the application i.e.
incomplete version of the software program being developed. A prototype
typically simulates only a few aspects of the final solution and may be
completely different from the final product. It becomes easier for business
users to relate with the final solution before the final solution is actually
developed.
7. Metadata Repository Analysis (MRA):It involves Metadata classification, metadata repository challenges, logical
meta model and metadata repository analysis activities. Metadata
classification includes business metadata and technical metadata. Metadata
components ownership, descriptive characteristics, rules and policies, and
physical characteristics. Working out challenges associated with metadata i.e.
technical, staffing, budget, usability and political challenges. MRA activities
include analyzing metadata repository, interface, access and reporting
requirement.
8. Database Design:It involves logical and physical database design. It becomes difficult to create
new database design over old database design and also modifying old
database design possesses a challenge to project team.
9. ETL Design:It involves preparing for the ETL process, designing the extract,
transportation and load program and process flow. Multiple applications and
databases make ETL design a complex task. ETL designs need to be revisited
at regular intervals to accommodate changes in business environment.
10.Metadata Repository Design:It involves designing metadata repository or/and licensing (buying) a
metadata repository. Every time ETL design is edited/updated or improved,
metadata repository design also needs to be revisited. Bigger the
organization, greater will be the complexity in designing metadata repository.
It is very difficult to have a perfect metadata repository in one shot.
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11.ETL Development:It involves source data transformation, reconciliation, peer reviews, ETL
testing, and development activities. Complex the design, more complex will
be the development. Data warehouses are typically assembled from a variety
of data sources with different formats and purposes. As such, ETL is a key
process to bring all the data together in a standard, homogeneous
environment.
12.Application Development:It involves online analytical processing tools, multidimensional analysis
factors, online analytical processing architecture, and development
environment. Application development is always in iterative mood as
requirements keep on changing. Proper documentation and change
management can help development team in accommodating change request.
Design analysts should establish the scalability of an ETL system across the
lifetime of its usage.
13.Data Mining:It involves defining data mining, specifying data mining techniques and
operations. A data mining system may work perfectly with one set of data
and perform significant worse with another set of data. Bigger the size of
database, slower will be the result. Development team needs to introduce
various methods to increase speed of output. If BI tool is slower in
performance, it could lead to total failure of the tool.
14.Metadata Repository Development:It involves populating the metadata repository, metadata repository interface
processes, metadata repository testing and preparing for metadata
repository rollout.
15.Implementation:It involves security management, data backup and recovery, monitoring the
utilization of resources, growth management. Many a times a system may
perform exceptionally well at test site but fail on implementation. System
needs to be tested keeping in mind the configuration of the site on which the
system will be implemented.
16.Release Evaluation:It involves post-implementation reviews. Post-implementation various
aspects, issues, advancement, future requirements come to the fore.
Business users may ask for changes to current reports or development of
new reports or KPIs etc. Once the business users start using BI tool, they
understand how BI can help them in business and subsequently bring about
changes in tool itself.
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3. Why BI projects never end?During the early days when the term business
intelligence was yet to be coined, data was just
being stored in varied ways and places. It was
difficult to manage data i.e. input and output ofdata. And as there was no enterprise
management system, there was no central
repository of data. Lot of problems such as
duplication of data, normalization of data etc
existed. With inherent problem of management
of data, it was next to impossible to carry out
analysis of data. The most used software for
maintaining data was spreadsheets.
Also currently when new companies are being setup, we generally find data
being maintained in spreadsheet or document files etc. But one thing common
between companies in early 70s and start-up companies today is that data is
used to answer the same questions Which products are best, How are my sales,
How are my people performing etc.
The companies whose IT departments are
on the beginning stage, generally find
themselves revolving around excel sheets,
surrounded by paper works, trying to go
through hundreds of documents in search
of answers many a times which has to be
done manually and also guessworksometimes. Companies also keep few
employees who are assigned the work of
analyzing such data and finding out answer
to the questions asked and generate reports to help various departments in
their quest to take decisions. Such team is also called BI team. BI team would
have the work assigned for managing the data, analyzing the data and
generating reports.
Once the companies grow, they generally look out for an ERP solution which
would help them in maintaining data. This would help the company to maintain
data in database and reduce lot of manual and paper work. But the effort put in
by BI team is not affected much. The BI team still needs to put in lot of effort as
data may not be normalized or required technical expertise to extract the
required data. BI consumers are mostly concentrated among executives and
managers, with a small group of analysts or operations users doing the manual
work of pulling together data from various sources and creating basic reports
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and analyses to feed to management. Reports and analyses are mostly provided
on a quarterly or monthly basis, there is little capacity to deliver lower levels of
information latency. Projects in the areas of finance, marketing or sales
reporting can often be successfully deployed at this stage. An example of these
types of projects would include departmental financial reporting, local or
regional sales history and some level of sales forecasting. This BI project are
generally carried out by in-house team or outsourced.
First time BI projects often end up being unsuccessful, reason being mainly
overambitious project scope and poor data quality that mostly cause project to
run behind schedule and over budget. And when the results are finally delivered
to the organization, business users are never satisfied citing reason as the new
system does not answer their business question the way they anticipated. But
the problem is that business users find it difficult to describe what they want
until they see it. This ever changing targets and goals make it difficult for BI
developers to succeed. Thus, a BI project that takes six to nine months todelivercommon for early-stage BI projectsmay answer the wrong questions
and address the wrong problems.
Consequently, many business users find themselves returning back to
spreadsheets or access databases to collect, analyze, and report on business
data. These documents generally provide conflicting views of information and
performance that reduce decision making capabilities and prevent strategic
alignment.
With BI, Corporate and management can trace trends and analyze anomalies
and ultimately align employee welfare to corporate goals. By using historical
data, company can predict the number of employees it would need at certain
point in time based on past movements in and out of the organization. This
information can help companies plan and act in proactive manner rather than
reactive one when it comes to recruiting.
It is typically up to the spreadsheet developer
to decide what metrics are important, what
data needs to be included, how the data is
formatted, and what level of aggregation is
necessary. Spreadsheets become isolated
and inconsistent data silos, and are difficult
for analysts to extract, transform and load
data into a central database to be interpreted
at an enterprise level.
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Problems with Spread-sheets
Reporting conducted in rows and columns Low degree of collective use High degree of inaccuracy and variability Limited security Limited collaboration
Many organizations overcome these
challenges and build successful BI solution,
most of which are departmental in scope.
Once the company has grown a bit in terms
of turnover and no of employees, the
company tries to custom built successful BI
solutions. These tools are the basic initial
level tool providing single dimension reports
such as view used just for reporting
purposes.
These organizations learn the importance of building a data warehouse one
subject area at a time rather than all at once, to minimize scope creep and data
quality issues. Also multiple systems start to exist in the company creating
integration problem. Data is departmentalized or even within specific
application. Effort is put into development of ETL to develop views for individual
reports or requirements stated by business heads. But because of lack of
integration capabilities, most of the work is still done manually to gather data,
and display it in proper format. BI teams work has not yet ended, it has justincreased. Companies at this level have invested in BI for a limited number of
managers or executives who need to drive tactical decisions. Employee and
managers use their own metrics to run specific parts of business. Organizations
still face major infrastructure issues to address, stemming disparate systems that
create doubt about relevance and consistency of data and analysis. Executives
lack confidence in quality and reliability of data.
To remedy this problem and achieve a
consistent view of shared business
information, many executives initiate anenterprise wide data warehouse project. This
executive which were previously fed with
departmental data are given goal of
consolidating data warehouses and deliver a
more consistent set of corporate information
and reports across all departments.
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With a fast changing environment, delivering a common set of business
semantics among changing strategies and new economic conditions becomes a
tedious and difficult task for organization to spend time and money. Getting all
departments to work together is a huge task in itself. Getting different
departments and business units to abandon their customized solution, let alone
agree to use standard terms, definitions, and rules or adopt a corporate
standard for BI reporting and analysis tools, is never easy. Once all of the above
tasks are carried out, stronger commitment is found towards BI and PM among
senior executives. Metrics are formally defined to enable management to
analyze departmental performance and there is a rising demand for
management dashboards. BI tool are designed to be more user-friendly,
interactive reports via dashboards, scorecards, and parameterized reports that
make BI more accessible to majority of users in the organization. They also begin
to augment the historical data in their data warehousing environment with time-
sensitive or real-time datadata that is delivered to users within hours or
minutes of an event or transactionso users can work proactively to solve
problems and capitalize on opportunities.
Moreover, the business value of their endeavors grows exponentially as more
data and users are supported by the new enterprise environment. The purpose
of the BI solution is no longer only to gain understanding and awareness, but to
deliver actionable information that can spell the difference between business
success and failure. Here, BI becomes a mission-critical system designed to
optimize processes and performance on a day-to-day basis, and in some cases,
on a minute-by-minute basis.
These BI systems run the business, and in some
cases, drive the market by providing a
competitive advantage. Finally the BI tool is
designed to reach across to all employees of the
organization with the help of security features
that help designate report to each and every
employee. This is known as Pervasive BI.
Thus BI project has been continuous as the organization has grown.
Even after this the work in BI project is yet to end. BI market has seen
advancement happening at continuous intervals. Advancements such as Mobile
BI, BI as a service, Improvements in visual representation of data such as Maps,
increase in speed, advance reporting, predictive analytics etc. do not allow BI
team to rest. Companies have understood the value of BI and what BI can
deliver. The work allotted to BI team may increase or decrease but the project
may never end.
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4. ConclusionBI has always been one of the most progressive projects in IT of any
organization. BI team has been working day and night to keep up pace with
advancement in BI market. BI projects have always been continuous and are yetto see the day when BI project will end. BI projects have always been on a roll
leaping from one roll-out to another. But the question is will BI project ever end?
It seems that a BI project in any organization is an endless story, which everyone
likes and uses utmost.
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