Presented By: Leah R. Smith, PMP July, 2011

Presented By:
Leah R. Smith, PMP
July, 2011
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Business Intelligence is commonly defined as "the process of
analyzing large amounts of corporate data, usually stored in large
scale databases (such as a Data Warehouse), tracking business
performance, detecting patterns and trends, and helping enterprise
business users make better decisions“
Business Intelligence is a set of methodologies, processes,
architectures, and technologies that transform raw data into
meaningful and useful information used to enable more effective
strategic, tactical, and operational insights and decision-making
Business intelligence aims to support better business decisionmaking, and is often referred to as a Decision Support System (DSS).
The BI environment generally includes these basic components:
• Data warehouse – a consolidated repository for key yet disparate
business data that has been cleaned, organized and integrated; it is
often the fundamental enabler of BI solutions
• Extract, transform, and load (ETL) tools and data integration tools –
methods to move and merge data from various business
applications into the data warehouse
• BI query and reporting tools – typically called information delivery
(ID) tools, these extract and present meaningful data from the data
warehouse and/or related sources
• Analytic applications – tools to uncover meaningful patterns and
draw insight from the data warehouse information
Benefits of developing a Business Intelligence Program
A Business Intelligence Program helps organizations to:
 Identify
 Better
market opportunities & market share
understand their profitability drivers
 Identify
unacceptable cost areas / savings opportunities
 Recognize
 Identify
business areas of high performance
the key performance indicators [KPI's] to use to measure capability
 Tracking
strategies for certain markets or customers are working and driving business value
 Visibility
to profitability & organizational expenditures
 Visibility
to historical data for trending purposes for improved planning & projections
 Deliver
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Business Value:
Business Value based on addressing business processes to reflect a business ‘need’ for BI
Identification of KPI’s & Metrics to help provide visibility to business capabilities, corporate savings
opportunities, potential new market areas, profitable business units / products, etc.
Business Intelligence Projects:
Critical Success Factors
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#1 Business alignment
– Too often, BI or data warehouse solutions are attempted by IT groups for the wrong reasons and without critical business
involvement. BI solutions, and in this context, enterprise BI, are long-term and expensive activities. Sustaining success
must be funded, and this will not happen if it isn’t driven by, and closely tied to, key business drivers. Just as critically,
even with sufficient funding.
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#2 Executive sponsorship
– This CSF is related closely to #1, and sometimes, adequate business alignment does not happen without first establishing the executive
sponsorship. Yet it is possible to achieve a level of business alignment and participation without full executive sponsorship, and vice
versa. The critical element here is ensuring the level of support and funding for the project / program.
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#3 Adequate project management and governance
– Like any complex IT project, BI solutions require proper management. The important element here is that BI is not a typical IT project.
Because the effort crosses so many significant boundaries - political, organizational, and cultural - the obstacles come from many
directions. Yet, too often, project managers are assigned to the project without prior BI experience.
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#4 Architectures for Enterprise Information Management
– Architecture is fundamental to many different elements of EBI, yet one of the biggest technical mistakes in undertaking an
enterprise BI solution is insufficient attention to architecture. Among other things, the data, ETL, ID, metadata, and
technical architectures must consider the enterprise even though the full enterprise solution is not in scope for the initial
project deployments.
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#5 Incremental and iterative development
– Developing and successfully deploying a final BI solution is a very large undertaking, and generally spans multiple years.
Yet no organization is willing to fund a BI effort that takes years to see any results. BI projects must be broken into smaller
project phases of between four and six months, each project phase deploying a solution that incrementally builds upon the
work and foundations laid by the earlier project phase.
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#6 – Metadata foundations
Why is metadata a CSF for enterprise BI? The answer is foundational. BI solutions cannot exceed without information quality (see CSF#7), and
information or data quality cannot be managed without an understanding of that data and what quality means; hence, metadata. At the most
fundamental and practical level, quality cannot be assessed without meaning, and the meaning of data, its business definition, is the first element of
metadata that must be addressed. Metadata is a fundamental source of documentation for the BI solution. Yet, as important and fundamental as
metadata is, it is frequently overlooked. The important element to recognize is that metadata should not be put off to later stages of development any
more than documenting software code should be put off to after going to production.
#7 – Information quality
Far too often, organizations are in a hurry to see a BI solution, and they too quickly try to bring the data together from multiple sources and
deliver the results to the business. Sufficient attention is not given to basic but subtle data definition variances or inconsistencies and ultimate
data quality. As a result, when the solution is deployed, the data quality is compromised and the overall information quality of the solution
does not satisfy business needs. Users may not only stop using the solution, and resort to their previous “reliable” techniques, but the BI
solution now has a negative reputation that may be difficult to overcome.
#8 – Adequate development and technical skills
Much like it is important to have a skilled project manager experienced in BI, it is also critical to have architects, data modelers, and BI
developers who are experience and skilled in the nuances of high quality, sustainable BI solutions. For example, data modelers who have years
of experience with transactional systems and standard relational modeling are typically not going to fully understand the important
distinctions that BI and analytic solutions bring to the table.
#9 – Appropriate tools and processes
Like the proper skills, it is critical to have the right tools. Processes, too, are critical to success, especially those related to data (and metadata)
management and governance. Indeed, the biggest challenges in building successful EBI solutions are tied much more to establishing the proper
processes than with anything else.
#10 – BI training
It is not sufficient to deliver an exceptional technical BI solution to the business. It is just as important that the business users understand
the system and know how to use it. Ideally, users should be enabled to achieve maximum productivity with the new BI tools, and this
cannot happen without adequate training. Worse, without proper training, the system simply is not used by its intended audience. This
can delay or prevent the expected return on investment. Ultimately, user adoption of any solution is critical to its success.
Preliminary Steps for Planning
a BI Project
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Conduct interviews with key executives / project sponsors to be able to articulate the
strategic direction, determine the goals and objectives of the business group, and high
level discussion as to how improved access to business information will be valuable as a
determination of business needs.
Conduct brainstorming sessions to further assess business needs – include subject matter
experts (SME’s), business managers, process experts, cross-functional teams, etc.
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Determine the “top business questions” that need to be answered
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Determination of key data subject areas needed, key metrics & KPI’s desired, etc.
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Determine how the business needs to analyze the data – what are the various dimensions
by which data needs to be presented (by geography/region, by business unit, by
Prioritize business needs based on assessment, depending on factors such as business
value, ROI, data readiness, organizational readiness, merger/acquisition, corporate
strategic initiative, system critical capability, etc.
Conduct interviews with SME’s, business analysts, and other key members of the
organization as part of Business Needs Assessment.
Existing Landscape Assessment
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Analysis of Existing Landscape (Architecture Review)
Investigate current system architecture
– Review hardware / software utilized in current environment
– Identify Vendor alignment / capabilities / gaps
– Assess capacity / sizing of current systems
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Identification of all source systems, data flows,
transactional systems, data repositories
Determination what is possible vs. not possible to
provide to the business community based on corporate
data currently available.
Preliminary planning for Future State Landscape
– Identify key system / hardware & software gap areas for BI / Data Warehouse implementation
– Sizing estimates for capacity constraints
– Assessment of available master data, gaps for master data needed, etc.
– Identification of hardware / software requirements
Business Information Processes
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The purpose of the capability Readiness Assessment activity is to gain a general
understanding of the overall relative “cost” that would be required for the client
to implement the various proposed capabilities. The higher the readiness, the
lower the cost.
Overall readiness has two key components; organizational readiness and data
readiness.
Data readiness consists of multiple factors, including:
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Data quality and accessibility of the major data entities required by the client’s business
community,
General stability and maturity of the applications or sources that hold this data
(including existing interim data repositories). Accessibility in turn is a function of
multiple elements, including availability and complexity.
Organizational readiness consists of:
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Organization readiness to move forward, to design, build, and utilize a new
information capability, is critical to the overall prioritization process.
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Determine the overall BI Vision for the
organization based on the organization’s strategy
& key business drivers
Start with a BI Master Plan / industry standard
methodology)
Develop a BI Roadmap for the organization
(phased approach to implementing BI solutions in
the organization)
Based on BI Roadmap, deliver on a phased
approach until the BI strategy is realized & a
successful BI program emerges
Maintain & support current BI environment as
subsequent phases are delivered
Use an Industry Standard
Methodology / Master Plan
Conduct full lifecycle SDLC
project approach/ use PMI
project mgmt methodology
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Develop the 1-3-5 year rollout plan for future
BI releases
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Develop long-term BI strategy for organization
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Master data planning for future BI releases
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Reassess business needs & priorities annually
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Conduct ROI analysis for overall BI roadmap
& future BI releases
Prioritize business needs for organization for
1-3-5 year roadmap
Next Steps for Planning a BI
Program/Project
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Build Project Team – all experts, core project team, extended team
Conduct Formal BI Program Launch / Project Kickoffs for all
releases
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Develop Project Charter & Scope Definition
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Develop Project Plan
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BI Projects – defining dimensions, master data, subject areas, KPI’s
& metrics, scorecards, reports, etc. starts EARLY
Scoping – definition of scope must include BI specific details
Requirements Gathering – interviews with SME’s, discussions for how
data is to be used, how data needs to be analyzed, report mock-ups, and
master data MUST be defined at this stage to include in formal business
requirements
Analysis / Design – BI team participate in dimensional modeling / design planning
sessions
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Establish sponsorship early in the project & identify key stakeholders
Determine the Business Value / ROI for the project & exactly how scope items will deliver the
business value / savings/ ROI
Bring in business / technical SME’s early in the project and throughout the project & assign project
issues to appropriate SME’s.
Identify key data sources & complexity of ETL process as part of analysis/planning phases (before
Design) to ensure appropriate skilled resources are aligned & appropriate software/tools are being
used
Plan for a formal program launch & project kickoff at initiation of project
Follow a full SDLC lifecycle phase approach for projects & ensure that all project phases are
completed with Exit Reviews/signoff
Follow PMBOK approach throughout project & select experienced program/project managers who
are PMP certified or have practiced PMBOK/PMI guidelines.
Develop a Risk Management plan and track & manage project risks & mitigate/accept risks
throughout the life of the project
Build project team & obtain required technical resources (internal vs. “best-shored” vs. consulting)
with required skill sets early in project & ensure that all appropriate technical & functional crosstraining is provided (software training, existing platform training, etc.)
Schedule weekly sponsor reviews to review all significant project issues/risks & include sponsors in
all phase exit reviews, require signoff for major milestones prior to implementation, etc.
Open Discussion:
Please contribute additional BEST
PRACTICES for successful BI projects &
implementations…