Presented By: Leah R. Smith, PMP Ju ly, 2 011
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 market opportunities & market share Better understand their profitability drivers Identify unacceptable cost areas / savings opportunities Recognize business areas of high performance Identify 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 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
#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. #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. #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. #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. #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.
#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
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. Determine the top business questions that need to be answered Determination of key data subject areas needed, key metrics & KPI s desired, etc. 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
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 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
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: 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: Organization readiness to move forward, to design, build, and utilize a new information capability, is critical to the overall prioritization process.
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
Develop the 1-3-5 year rollout plan for future BI releases Develop long-term BI strategy for organization Master data planning for future BI releases Reassess business needs & priorities annually 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
Build Project Team all experts, core project team, extended team Conduct Formal BI Program Launch / Project Kickoffs for all releases Develop Project Charter & Scope Definition Develop Project Plan 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
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