BI an Endless Story

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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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Business Intelligence An Endless Story

MAIA Intelligence White Paper May 2011

2

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
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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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BI an Endless Story