The Business Intelligence Framework – Built to Last


The success of a business intelligence program depends on the approach or methodology used to implement the framework and the related components. Incorporating key activities in the Software Development Life Cycle and the use of an architectural framework can implement business intelligence programs that are scalable, maintainable and aligned with the enterprise strategic direction.

Over the past few years, business
intelligence
has risen in prominence and popularity. One of the primary
reasons for this surge in the need for business intelligence services is the
recognition of the tremendous value it provides. An organization with business
intelligence services can now see their data from various perspectives. The
benefits of seeing data from different viewpoints enables an organization to
gain insights into new markets, identify new products to existing markets or
gauge the impact of their products in various marketing regions.

The working definition of business intelligence for this article
is that Business Intelligence (BI) is a set decision support applications that
deliver information used in operations such as forecasting, analytics,
dashboards, querying, reporting and data mining.

However, a major part of the success of a business intelligence
program is the approach or methodology used to implement the framework and the
related components. For example, if the approach used is one that simply
constructs a data mart
to support a specific set of business functions with no consideration for the
organization strategic direction, scalability or maintainability, this business
intelligence solution will certainly become a prime candidate for an expensive
re-engineering project once those same business functions outgrow the
application.

Now having said all of this, let’s take a look at how
incorporating key activities in the Software
Development Life Cycle
and the use of an architectural framework can
implement business intelligence programs that are scalable, maintainable and is
aligned with the enterprise strategic direction–and of course, with
documentation playing an integral part of the approach. Each phase should
produce documentation that is used as a method to obtain signed approval to end
one phase and begin the next.

Initial Assessment

One of the most important activities is conducting an initial assessment.
This assessment has two components to it; business and IT. The purpose of the
assessment is to identify the business needs and the impact that a business
intelligence solution would have on the organization. It is an effective way to
identify the amount of time, cost and risk to the organization. It also
provides the justification for business intelligence. This is a huge plus as it
enables continued executive sponsorship.

The IT assessment is a complete evaluation of the current infrastructure.
This answers the question "Does our organization have the tools and
infrastructure to facilitate a BI solution?" The answer is in the results
of a readiness assessment of the organization’s ability to support a business
intelligence framework and the related applications and solutions. The
readiness assessment should include a detailed review of the hardware, BI and
database software and operating system.

With this information, the team has everything it needs to produce a high
level conceptual design of the BI architectural framework and an executive
summary of the assessment.

These artifacts are the important business and IT drivers for the remaining
activities in the SDLC.

Planning

Planning is the next set of activities that puts the BI framework in place
that really and truly, puts everything in motion.

It is within this phase that the scope is clearly defined into sufficient
detail that sets the expectations of senior management and in essence gets all
members of the team to a common understanding of the goals of the project.

Once the scope has been defined and accepted by all members of the team
complete with signed approvals, the next step is to define roles and
responsibilities needed to complete the BI project. These roles and
responsibilities should include both business and IT. It is very important that
these roles are defined such that all is taken into account from a budget and
time perspective. This task also facilitates the communication protocols for
everything from project status reporting to incident management. These roles
should include but are not limited to business stakeholders and sponsors,
business SMEs, business analysts, architects, DBAs, infrastructure engineers,
ETL developers, BI developers (should be specific to the software solution such
as COGNOS, SQL Server SSAS, Business Objects, etc) and project managers.

Also in the planning stage, depending on the level of BI maturity in the
organization, a training assessment should be conducted to gain an
understanding of whether training is required for the BI suite of tools being
deployed into the organization. This assessment has direct impact on cost and
time lines as well.

The end results of the planning phase should be a fully fleshed out project
plan with assigned responsibilities and time lines with alignments to the
detailed budget for the project.

Requirements Gathering

With the project plan and budget approved, the functional and non-functional
requirements are defined. The functional requirements are the articulated
business needs that must be met with this BI solution. It is within these work
sessions that requirements related to various types of reporting (ad-hoc and
canned), the need for dashboards, key performance indexes (KPIs) and
presentation displays are gathered and documented. Metadata management
activities are a part of the requirements gathering process as it relates to
the business context definitions of data. Also included in the requirements
gathering from a business perspective is identifying the data sources that will
be used to populate the data mart that will be the supporting data layer for
the BI solution (in most cases). Ideally, the identification of these data
sources should mainly be contained in the Data Warehouse.
This is an important component. Why? Because if the data for the BI solution is
sourced from the Data Warehouse, there is a high level of confidence in the
quality of the data and minimal use of an ETL solution. If, however the data
sources are disparate then the opposite holds true.

Non-functional requirements includes gathering information related to
security, an essential and integral part of the BI solution. It is extremely
important that the information being delivered is being consumed by authorized
users only. Remember, each and every solution/application within the
organization must survive an internal or external audit. Think Sarbanes-Oxley
and HIPAA.

Other non-functional requirements include the definition of infrastructure
needs as it relates to hardware, software and network components. These
requirements should address the needs of a BI solution in terms of server
memory configuration, use of SAN/NAS for storage, number of CPUs per server and
parallel processing capabilities.

Once all this information and data has been compiled, a business
requirements document is produced that frames the context of the business and
IT requirements related to the BI solution that are in scope of the project.

Design Guidelines

The purpose of the design phase is to translate the business
requirements document into design specifications. These specifications will be
used as the blueprint for constructing the solution.

However, in designing a BI solution, there is a further
refinement of the requirements as it relates to the type of data models that
will be designed to deploy the data mart. This means that based on the business
needs for reporting and the BI solution proposed, design decisions are made on
whether a star schema or snowflake schema or hypercube be used as the
supporting data layer for the application. Factors that weigh heavily in this
decision are primarily related to the type of BI reporting. For trending, drill
through, slice and dice, and analytics, then use of the hypercube works best.
However, caution here, loading of the hypercube is resource intensive and
complex and should be factored into the availability SLAs. So each design
decision does have its tradeoffs; choose wisely!

Other design components within this phase include major enhancements
to the security framework. This is often sensitive data. Only the users meant
to view specific sets of information should have the required permission. Also
included are infrastructure enhancements related to storage, server and network
configurations.

The document created that captures the output and results of this
phase are the Technical Design Specification.

Construction and Deployment Guidelines

The construction phase is just that. It is constructing, creating
or installing the components that have been presented for the BI solution.
However, before the actual construction can begin, the details of the Technical
Design Specification should be reviewed with all members of the business and IT
teams to ensure that there is a common understanding of all components of the
BI solution. All work efforts should be focused on building the components of
the BI solution. This includes the creation of the data models, the database or
hypercubes, development of the application and presentation layers, installation
and upgrade of the hardware and software.

The message here is that completion of the previous phases
ensures that the BI solution is constructed and deployed with minimal
refinements and modifications of the design specifications.

The Last Word

Following the software development life cycle with a specific
focus on the build out of the BI solution with awareness of the organization
needs for information will enable the BI program that scales to meet the
growing needs of the organization.

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Denise Rogers

Denise Rogers
Denise Rogers
enise is a data architect with several years experience in the deployment of data architectures at major healthcare insurance companies and state government. She is a certified PMP that has designed and deployed a number of data solutions ranging from transactional to decision support within various architectural and project management frameworks. She has also spearheaded a number of efforts related to database environment assessments, capacity planning and performance tuning. In the past, Denise has held several user group positions including participation in International DB2 User Group (IDUG) and internal architectural groups. She has presented solutions to division heads at the within state government as well as conducted a number of company related training and information sharing sessions on database performance tuning techniques, best practices, etc . She has also mentored and coached project team members at various levels of expertise including university recruits, business users and senior IT staff. Denise graduated from Greater Hartford Community College Cum Laude in 1983 with an Associate’s degree in Management Information Systems.

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