How to Unlock the Potential of Your BI Strategy: BI Implementation

There are a lot of articles out there that state Business Intelligence (BI) projects have a 70-80% failure rate —this is why finding a reputable BI as a Service partner is critical—one of the most notable sources is Gartner. So you may be asking yourself if that failure rate is so high, why bother with implementing BI?

Failure can be greatly controlled by planning and developing a BI strategy and roadmap tailored to your pain points and business challenges. Building a BI strategy has three core phases that will decrease your chance of failure, improve your time to value, and make data your company's best asset. We will delve into each of these stages in more detail, but let’s start with the main objective of each stage.

Readiness Focused

Think of this stage as a construction crew building the foundation for a house. BI Implementation takes time, effort, budget, and most importantly buy in.

Plan for Change & Adoption

Following with the construction analogy, you have to build the walls. This is where you listen to your end users, prepare them for change, and share with them the potential impact of BI.

Execution

This is your roof, choose a roof that is the right choice for you in the long run. Your business requirements and governance goals plays a huge part in what BI technology you should invest in.

Readiness Focused BI Strategy

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 - Certain capabilities are needed to support analytics like strategy, prioritization, resource skill sets, business processes, funding, data quality, and leadership support. It is also necessary to understand the roles of a BI implementation and determine how you will fill them. Most success is found when a digital transformation consulting firm supports a few of these roles, you get a first win for the organization and then provide training for future self-sufficiency.

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 advanced data solutions into existing processes & structures
• Project Manager – manages the overall project schedule, coordinates across all resources, manages project communications, risks, issues, changes, etc. The role can be full or part-time.
  
  

Executive Sponsorship -  Executive buy-in and sponsorship is critical to success and projects can get squashed quickly OR die a slow painful death without it. An executive sponsor is responsible for the program's overall success and continuously ensures alignment with corporate strategy and delivery of value.

A Solid BI Strategy Should Plan for Change and Adoption

Change Management - Embrace an “iterate to success” approach. 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
• Strong executive leadership support
• Top-down and bottom-up stakeholder engagement
• Plan for ongoing communications and value assurance

Promotion & Adoption—Building awareness throughout the organization of the analytics organization’s goals and capabilities and how those goals and capabilities will potentially benefit the organization will create “champions” and “data stewards” for analytics. These evangelists will be key when it comes time to request feedback. Pay attention to feedback and be prepared to explain (repeatedly) how the analytics team supports 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.

Execution Focused

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

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

One of the 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 strategy.

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

The Goals of Self-Service

• Enable timely and informed business decisions
• The ability to ask and answer new questions
• Efficiently use resources
• Connect data analysis with decisions
• Limit or eliminate information bottlenecks
• Agility to move with the business
• Foster innovation

The Goals of Governance

• Better communication through a common language
• Faster data analysis through reusable content
• Protect the company’s secrets
• Increase margins through efficiency
• Internal transparency
• Ability to make decisions with confidence
• The single version of the truth!

Related Content: Take the BI Maturity Assessment

BI Project Pitfalls and How to Avoid Them

It is common for BI projects to fail or get hung up due to a lack of business intelligence strategy, lack of training/education, use of unstructured data, and, most commonly, lack of adoption. We recommend treating BI as a business process improvement initiative rather than an IT-centric undertaking, focusing 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.

Getting Started with a Robust BI Strategy

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 or rolling out the analytics program to one business unit or functional area (e.g., customer operations). Remember to track, measure, and celebrate your successes.