Unlocking Business Value with Data & Analytics: The Power of Value Chain Mapping
Chinaza Imala, M.B.A
IT Leadership Professional || ServiceNow Certified || Data & Analytics (Diagnostic, Prescriptive, Descriptive, Predictive) || BI Developer || Data Analyst || Cautious AI Adoption Advocate & Trainer || Podcast Producer
— Part 1 of my new series focused on exploring an innovative strategy and tool that helps organizations maximize their D&A investments and drive meaningful business outcomes —
Inspired by "Gartner’s 2025 Leadership Vision for Data & Analytics" by Rita Sallam, Chief of Research, Data, Analytics and AI at Gartner
In today’s AI-driven landscape, Data & Analytics are no longer just buzzwords—they’re the engines powering innovation, growth, and competitive advantage.
Inspired by Gartner’s 2025 Leadership Vision for Data & Analytics, as presented by Rita Sallam, Chief of Research, Data, Analytics and AI at Gartner, I’d be diving into a strategy that bridges the gap between technology and tangible business impact.
If you’re a Data & Analytics (D&A) leader, this might hit close to home. Gartner reveals that 65% of Chief Data & Analytics Officers (CDAOs) struggle to articulate the impact of their initiatives, while 67% of CFOs say digital investments aren’t delivering the returns they expected.
The culprit? A disconnect between technical efforts and measurable business value. This disconnect isn’t just about numbers—it’s about missed opportunities for growth, efficiency, and customer satisfaction.
That's where Value Chain Mapping comes in—a strategic tool for bridging the gap between technical D&A efforts and the business results that need to be delivered.
What Is Value Chain Mapping?
At its core, value chain mapping connects three critical pieces:
Technical Enablers: AI models, data infrastructure, and analytics tools.
Operational Processes: The workflows that turn data into action.
Business Outcomes: Tangible wins like revenue growth, cost savings, or better customer experiences.
Without value chain mapping, even the best tech can falter. For example:
Route optimization can reduce cycle times, but mismanaged data leads to flight delays, passenger refunds, and revenue losses.
Predictive maintenance boosts equipment uptime, but only when seamless data integration prevents parts shortages or skyrocketing costs.
The Value Chain Mapping Framework makes this connection crystal clear, helping to visualize how every data effort drives strategic goals.
Key Takeaways:
Tech is only half the battle: Strong technical foundations are crucial, but they need effective operational processes to truly shine.
Clear Business Alignment: Every technical component should directly contribute to at least one strategic business outcome.
Balanced Investment: Over-investing in technology without parallel operational enhancements can hinder performance.
Holistic Approach: This approach ensures projects fuel big-picture goals like growth or efficiency.
Value chain is about aligning D&A projects with an organization's top priorities – revenue growth, cost optimization, and delivering exceptional customer experiences.
#DataAnalytics #AI #ValueChain #BusinessStrategy #GartnerInsights #Leadership #DataDriven #BusinessValue
Erasmus Mundus Masters in Decentralised smart ENergy SYStems | BEng Electrical/Electronics Engineering | Aspiring Energy Professional | SDG4, 7 & 9
2moGreat work!
I help global organizations turn challenges into growth by bridging strategy and execution. Digital transformation, AI strategy, agile product management, and innovation for measurable results. | ESSEC & Mannheim EMBA
2moGreat insights, Chinaza Imala, M.B.A! The finding that around 67% of CDAOs struggle to demonstrate the business impact of D&A initiatives, and a similar percentage of CFOs report disappointment in digital investment returns, is well documented in recent industry research. The most common reasons for these setbacks are poor data quality, weak alignment with business objectives, and a lack of strong governance. Failure in these initiatives often looks like missed deadlines, budget overruns, slow time-to-market, low user adoption, or falling short of key business goals—whether financial, operational, or strategic. Data quality issues and solutions that don’t address real business needs are also frequent stumbling blocks. Approaches like value chain mapping and robust Data & AI Governance can help close these gaps and turn investments into real impact.