- ✓A BI strategy should begin with a clear articulation of the business outcomes it is intended to support, expressed in terms that connect to organisational priorities rather than in terms of the technology to be deployed.
- ✓Successful BI strategies identify the key decisions that need to be better informed by data, the data required to inform them, the gaps between current data availability and what is needed, and the roadmap for closing those gaps.
- ✓Governance is a critical component of any BI strategy: defining who owns data, who is responsible for quality, who can access what information and how data issues are resolved must be agreed before tools are selected or dashboards are built.
- ✓Change management is as important in BI implementation as technical execution: building the culture and skills needed for data-driven decision-making requires sustained investment in communication, training and leadership by example.
- ✓Measuring the value of BI investments requires connecting BI activity to business outcomes, such as demonstrating that a specific decision informed by BI data led to a measurable improvement in a business metric.
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Start learning →Alex: Welcome back to The Study Podcast. We're closing out Unit 9 today by thinking about BI strategy: how you align an organisation's data capability with its strategic goals. Sam, this is where the unit takes a really senior perspective.
Sam: It does, and it's worth noting that the ability to think strategically about data and BI, rather than just technically, is what distinguishes senior data professionals from those operating at an individual contributor level. This is the kind of thinking that drives career progression in this field.
Alex: What makes a BI strategy genuinely strategic rather than just a list of tools and dashboards?
Sam: A genuine BI strategy starts from the business strategy, not from the technology. What are the organisation's most important strategic goals? What decisions need to be made better to achieve those goals? What information would enable better versions of those decisions? That chain of reasoning, from strategic goal to decision to information need, is what makes a BI strategy genuinely strategic rather than a technology wish list dressed up as strategy.
Alex: What are the components of a BI strategy document?
Sam: A well-structured BI strategy typically covers the business context and strategic goals it's designed to support, the key analytical questions and decisions that will be addressed, the data assets required and an assessment of their current quality and accessibility, the technology architecture needed to make the data available and usable, the governance framework that will ensure data quality, security and compliant use, the organisational capabilities that need to be developed, and the roadmap and investment plan for delivering the capability over time.
Alex: Data governance seems to come up as a critical enabler. How do you build it?
Sam: Governance requires both structures and culture. The structural elements include a data governance framework document that defines roles, responsibilities and processes, a data catalogue, data quality standards and metrics, data stewards who own specific data assets and are accountable for their quality, and a governance forum where data issues are escalated and resolved. But the cultural elements matter just as much: data governance only works if people believe in it, if leaders model data-informed behaviour and if accountability for data quality is genuine rather than nominal.
Alex: And how do you make the case for investment in BI? Because it can be hard to quantify the return.
Sam: The approach I find most compelling is to identify specific, valuable decisions that the organisation needs to make better, estimate the value of making those decisions better and use that to justify the investment. Quantifying the value of a decision improvement requires some creativity but it's possible: if better stock management could reduce write-offs by a certain percentage, that's a concrete financial benefit. If better customer churn prediction could retain an additional fraction of revenue, that's quantifiable. The case becomes much more compelling when it's grounded in specific business outcomes rather than general claims about the value of data.
Alex: Excellent close to Unit 9. We'll move into Unit 10 on the Internet of Things next. Thanks, Sam.