Quantifying and communicating the business value of initiatives can be a struggle for data and analytics (D&A) leaders, who cite it as one of the top three barriers to their team's success.
This highlights the importance of identifying business value metrics (BVMs) that directly link data, analytics and AI operational investments to measurable business outcomes. In this way, D&A leaders can ensure their needs are prioritized within the larger business and secure funding more easily.
Align data, analytics, AI initiatives to outcomes
One of the main reasons that D&A leaders struggle to prove the value of their work is because they tend to focus on technical metrics rather than tangible business outcomes. Instead of reporting data quality or model accuracy in isolation, D&A leaders must link them directly to strategic goals, such as customer churn reduction, to explicitly prove the financial and operational ROI of the program.
Related:Data governance, AI lower the barrier to entry for digital twins
The first step for D&A leaders is to understand what success looks like for their business stakeholders. Stakeholders communicate their priorities through strategic plans, annual reports and ongoing business discussions. From there, leaders should be able to create a clear link between their D&A activities and their specific business outcomes.
Not every D&A initiative will deliver the same level of business impact, so D&A leaders should also assess and prioritize opportunities based on their expected business value. Aligning data, analytics and AI initiatives to business outcomes shifts the conversation from technical performance to tangible business impact, creating a stronger foundation for investment, prioritization and long-term support.
Define the business value metrics
Once the connection between D&A initiatives and business outcomes is established, the next step is for leaders to define their BVMs. The metrics make the impact of the work visible and measurable. Effective BVMs create a clear line of sight between operational improvements, D&A outcomes, and the business results leaders care about most.
To make BVMs credible, D&A leaders should establish baselines and target outcomes. Understanding current performance creates a benchmark against which future improvements can be measured, helping stakeholders assess the true value of an initiative and compare investments across the portfolio. Clear definitions are equally important. Each metric should have a business definition that explains why it matters, as well as an operational definition that outlines how it is calculated, measured and reported.
Related:John Deere harvests data insights with new AI technology
Most importantly, D&A leaders must focus on outcomes, not activity. The real value comes from how time is reinvested to generate revenue, improve customer experiences, reduce risk or advance strategic priorities. The strongest BVMs translate operational improvements into measurable business impacts that stakeholders can easily understand and support.
Refine BVMs when business drivers change
The goal for D&A leaders is to prioritize their investments based on risk assessment. An immediate benefit of identifying, measuring and reporting BVMs is to more effectively manage the readiness or capabilities needed in order to bring to life the expected business outcomes.
Readiness describes the state of capabilities needed to realize the expected data and analytics-enabled improvements in business processes, activities, decisions and data, and products and services. D&A leaders should use BVMs to make defensible portfolio decisions, like determining which data and AI initiatives are funded, scaled, delayed or deprioritized.
By managing the readiness of D&A capabilities to support prioritized business outcomes, leaders can reduce the risk of initiatives falling short of their intended impact.
Related:Report sprawl will undermine your AI strategy
Prioritize initiatives using both value and feasibility metrics. This means evaluating initiatives based on both their expected business value and the organization's ability to actually deliver that value. Initiatives that demonstrate value but lack feasibility should be flagged as calculated risks and carefully prioritized in balance with high-value and high-feasibility initiatives.
Once business outcomes, risks and readiness are assessed, D&A leaders have a defensible basis to prioritize initiatives that balance near-term delivery with long-term strategic impact. This will help leaders ensure that overinvestment in low-value or low-readiness work does not constrain higher-impact opportunities.
Ultimately, showing business value from data and AI will be an ongoing exercise. D&A leaders must consistently align initiatives to business outcomes, define meaningful BVMs, and adapt those metrics as priorities change. This will allow them to more effectively secure funding, strengthen stakeholder confidence, and ensure data and AI investments deliver measurable enterprise value over time.
About the Author
Gartner
Ray Collins is a senior director analyst at Gartner, focused on demonstrating measurable business value by leveraging data and AI initiatives.