If you have researched the benefits of business intelligence, you may have seen from other sources that the failure rate of BI implementation is 70-80%.

The reason for this high rate is because of the transition into adopting business intelligence may not be accurately matched to the businesses that try to carry them out. By tailoring your BI Roadmap to accurately address the pain points of your business, your company's data will become a successful asset. 

 The components of a successful BI Implementation

roadmap pyramid-1

Readiness Focused - Creating the foundation for implementing BI takes time, effort, budget, and buy in. 
Plan for Change & Adoption - BRanching into the actions to be taking, businesses should be listening to both employees and clients, and consider the impact of BI to them. 
Execution Focused - YOur business requirements and governance goals plays a huge part in what BI technology you should invest in.


Readiness Focused

Measured Business Value - If an enterprise analytics initiative or even a specific use case hypothesis does not specifically identify the value and overall contribution to the organization’s strategic goals, then it should not be pursued. Similarly, if a use case does not clearly identify the value and a mechanism for measuring that value, then it should not be included in an analytics use case prioritized roadmap. Analytics use case value can be quantified in various ways, including, for example, increased savings/revenue, increased safety, reduced risk, improved operational efficiency, and improved reliability.

Operational Support & Resources - A proper strategy, prioritization, resource skill sets, business processes, funding, data quality, and leadership support are all necessary for proper analytics support. Understanding the roles of a BI implementation and determine how you will fill them is also important. The most common roles to consider adding are:

  • Architect – establishes technical and data foundational environments; establishes development standards; assures BI solution integrates into existing structures or a new structure is determined
  • Developer – fulfills requirements with a technical solution; includes data and front end development; can be performed in any project delivery methodology environment (waterfall, Scrum, etc..)
  • Requirements specialist / Integration analyst – gathers requirements, assures alignment to business processes and integrates new data solutions into existing processes & structures
  • Project Manager – manages the overall project schedule, coordinates across all resources, manages project communications, risks, issues, changes, etc.. Role can be full or part time.

Executive Sponsorship -  An executive sponsor is responsible for the overall success of the program, and continuously assure alignment to corporate strategy, and delivery of value. Without that buy in and sponsorship, projects are susceptible of dying out without success. 


Plan for Change & Adoption

Change Management - Embrace an “iterate to success” approach and start small and fail often but fail forward. Change management requires transformation capacity and a true analytics transformation will require:

  • Well-defined change and communications management roadmap
  • Top-down and bottom-up stakeholder engagement
  • Strong executive leadership support
  • Plan for ongoing communications and value assurance

Promotion & Adoption - Building awareness throughout the organization, awareness of what the analytics organization’s goals and capabilities are, and how those goals and capabilities will potentially benefit the organization will create “evangelists” for analytics. These evangelists will be key when it comes time to requesting feedback. Pay attention to feedback and be prepared to explain (repeatedly) how the analytics team is supporting the organization’s overall strategy, mission and vision.

Data Driven Culture - Google defines data literacy as “The process of expanding business information and the tools to analyze it out to a much broader audience than traditionally has had access.” Improving data literacy hones your decision-making skills by learning to ask the right questions of your data, interpret your findings and take informed action. By de-siloing data, people, and ideas you can drive this data-driven transformation.data culture graphic

Execution Focused

Tools & Technology - The technology approach will likely be at least slightly different for each organization and should establish a thorough understanding of their business goals and requirements for their analytics program before making decisions regarding technology (“what before how”). Consider the following when selecting a technology best suited for your organization:

  • Technology and tools should align with business user needs and sponsorship
  • Strive for “natural analytics” solutions with seamless integration
  • Beware long implementation timelines and long time to value cycles
  • Consider “1st to be 2nd” approach
  • Plan for data growth, both longer and wider
  • Focus locally to assure effectiveness, be prepared for enterprise needs in parallel
  • Balance with business plans; scale when needed, but not much “build it and they will come”

bi processOne of the key reasons BI Implementation Projects fail is that they go for the “cheapest” tool and don’t consider total cost of ownership or they spend all their time researching tools and lose sight of the other components to a successful BI roadmap.

Governance - Balance the goals of self-service and governance according to your goals. 

Goals of Self- Service
Goals of Governance
  • Enable timely and informed business decisions
  • The ability to ask and answer new questions
  • Efficiently use resources
  • Connect analysis with decisions
  • Agility to move with the business
  • Foster innovation
  • Limit or eliminate information bottlenecks
  • Faster analysis through reusable content
  • Protect the company’s secretes
  • Increase margins through efficiency
  • Internal transparency
  • Ability to make decisions with confidence
  • Better communication through a common language
  • Single version of the truth!


BI Project Pitfalls and How to Avoid

 It is common for BI projects to fail or get hung up due to lack of BI strategy, lack of training/education, using unstructured data, and most commonly lack of adoption. We recommend treating BI as a business process improvement initiative rather than an IT-centric undertaking, focus on supporting key business objectives with better information embedded in specific business  processes, and the use of a BI-specific development methodology, such as Scrum or Decision Path’s BI Pathway method. These are all part of building a data culture within your organization. 


Getting Started

Some organizations may not have the support to deploy a full enterprise analytics program with a big bang approach. An evolutionary transformation might be in order. For example, your deployment roadmap could provide for a “crawl-walk-run” approach that can help you score some quick wins and build credibility. Start with a proof of concept and execute one use case or a logical grouping of use cases with low complexity and high value potential. After that success, proceed to a pilot, perhaps rolling out the analytics program to one business unit or functional area (e.g., customer operations). Remember to track, measure and evangelize your successes. At this point, other business units will likely be coming to you!