Mike Burke | 4/18/2025
The key insights gained from the Gartner 2025 Data & Analytics Conference by Analytica’s team confirmed what we are seeing across numerous client engagements implementing Artificial Intelligence (AI) in government and large enterprises: AI isn’t the future – it’s now. But, a critical component in successfully implementing AI and AI Agents into enterprise production environments still remains: clean, well governed, and actionable data. Without a strong AI & data governance framework and a well-defined AI & Data Strategy, AI won’t deliver the business impact organizations expect.
This year’s event focused on real-world challenges: scaling AI, enabling decision intelligence, and modernizing data platforms. Here are our biggest takeaways from the conference and insights into what they mean for the future.
AI Without Trust is a Liability
During the opening keynote, Gareth Hersche and Carlie Idoine, Vice President Analysts at Gartner, emphasized a clear message: AI is only as reliable as the data it’s trained on. Even with strong governance there can still be bias, misinformation, and security risks, leading to costly mistakes. The key theme? The essential ingredients of trust must be built into AI systems and solutions from the start, not added as an afterthought.
Key insights from the sessions:
To cultivate trust in AI systems, organizations must implement practices that increase transparency and accountability:
- Foster transparency by including stakeholders from across your organization in model feedback loops to align AI development with business goals.
- Leverage inherently interpretable algorithms where possible to simplify understanding and decision-making.
- Apply model-agnostic explanatory methods to explain complex models without relying on internal logic alone.
- Validate outcomes using human-in-the-loop frameworks to reinforce accuracy and accountability.
Why this matters:
Agencies need to move beyond one-off AI projects and embed governance directly into AI workflows. This ensures AI-driven decisions are explainable, secure, and aligned with business goals.
- AI trust models should be use-case-driven, with clear validation processes.
- Metadata and lineage tracking are essential for AI transparency and compliance.
- AI governance isn’t just about policies – automation and continuous monitoring are critical.
Decision Intelligence: Moving Beyond BI Dashboards
Static dashboards aren’t enough anymore. In his session “Emerging Practices for Decision Intelligence,” Erick Brethenoux, Gartner’s Chief of AI Research and Distinguished VP Analyst, emphasized the growing importance of Decision Intelligence (DI) as the next leap in analytics. He described DI as a way to connect analytics with decision-making, transforming data into timely, actionable outcomes.
DI was one of the biggest topics at the conference, highlighting a shift from passive reporting to active, AI-driven recommendations that help organizations make better, faster decisions.
What is Decision Intelligence?
Decision Intelligence is the practice of combining data, AI, and context to improve how decisions are made, automated, and optimized across an organization. Instead of just showing you what happened, DI tools help answer What should I do next?
What’s replacing static dashboards?
Rather than relying solely on traditional dashboards, agencies are moving toward:
- AI-assisted insights embedded directly in apps or workflows
- Decision playbooks – reusable frameworks that recommend and guide best practices
- Trigger-based alerts that prompt action when thresholds or patterns are detected
- Automated decision loops that continuously learn and improve over time.
As Rob Gordon, Director of Data Science at Analytica, put it: “What decision will you make based on what you see on the dashboard?” If the answer isn’t clear, that’s your cue to redesign the experience or to automate the decision entirely.
Key themes from the sessions:
- Decision playbooks help standardize, scale, and accelerate repeatable decisions.
- AI can refine decision-making through continuous learning and feedback loops.
- Successful DI adoption requires close alignment between data teams and business stakeholders.
What agencies should do:
- If your organization still leans heavily on static, historical BI reporting:
- Start identifying decisions that can be automated or supported by AI.
- Test key scenarios through proofs of concept (POCs).
- Look for ways to embed decision-making into business workflows – not just dashboards.
- Build or adapt playbooks that document high-impact decisions, inputs, logic, and outcomes.
The Rise of the Data Product Economy
Traditional data management is being replaced by a more modern, business-aligned model: data products. Rather than treating data as a byproduct, organizations are now packaging data into reusable, governed, and purpose-built assets that support analytics, AI, and operational decision-making.
“Data products are the next wave of sustained solutions to reduce business-IT friction,” explained Michele Launi, Sr. Principal Analyst at Gartner, during “How You Should Build, Manage and Sustain Data Products.” He emphasized the importance of marketplaces, governance, and contracts to manage and scale these assets effectively.
In the session “How Verizon is Building a Thriving Data Product Economy,” Syed Latheef, Executive Director at Verizon, highlighted how the company modernized its data ecosystem to enhance self-service and responsible AI. Steve Wooledge of Alation reinforced the importance of metadata management, stating that “cataloging, tagging, and curating business information is critical to improving GenAI accuracy.”
Key takeaways from the Data Product Economy session:
- Balance speed and trust: Decide when centralized governance vs. domain-level ownership makes sense.
- Scalability and reuse are essential: Data products must be durable assets, not one-off solutions.
- Discovery matters: Tools like data catalogs and metadata layers make products easier to find, use, and govern.
Why this shift is happening:
A well-structured data product strategy reduces time-to-insight, lowers costs, and improves data quality, ultimately making AI and analytics more effective.
Data Strategy is a Living Process, Not a Document
Too often, organizations treat their data strategy as a one-time deliverable – something to check off a list. But according to Saul Judah, VP Analyst at Gartner, this mindset is a major reason strategies fail. In his session “How to Create a Data & Analytics Strategy for Real Business Results,” Judah emphasized that strategy must be dynamic, adapting continuously to business needs and market conditions.
“Strategy is about navigation – your operating model is how you execute,” Judah explained. A successful strategy is not about selecting the right tools; it’s about clarifying the drivers, desired outcomes, and organizational capabilities required to reach them.
Key insights from the strategy session:
- Strategy defines direction; operating models define execution.
- Good strategy connects business goals to the data and analytics needed to achieve them.
- Treat your strategy as a living framework: Evaluate and evolve it over time to maintain relevance and impact
What this means for agencies:
If your data strategy hasn’t changed in the last 12 months, it’s probably outdated. Regular reviews and realignment with business priorities, emerging technologies, and organizational capabilities are essential.
Data Governance is the Key to Scaling AI & Analytics
Data governance is no longer just a compliance checkbox – it’s a strategic enabler. In the session “Unified Governance for Data and AI: Best Practices from a Bank of New York Data Leader,” Paul Carey (Bank of New York) and Murthy Mathiprakasam (Collibra) showcased how modern governance frameworks are critical to delivering trusted, scalable analytics and AI.
“To scale AI, you need to know what data you have, where it lives, how it flows, and why it matters,” noted Carey. This means moving beyond static inventories toward dynamic, federated models that embed governance directly into data and analytics workflows.
Key takeaways from governance sessions:
- Governance should be proactive, automated, and embedded within analytics
- Metadata is critical for AI, decision intelligence, and self-service analytics.
- Agencies should transition from manual policies to automated enforcement.
Why this matters:
Without unified governance, organizations risk data silos, compliance failures, and untrustworthy AI. A strong governance foundation accelerates time to insight, improves data trust, and allows teams to innovate confidently at scale.
Final Thoughts: AI & Data Strategy Need a Strong Foundation
Gartner 2025 reinforced a critical truth: AI and analytics success isn’t about adopting the latest tools – it’s about building the right strategy, governance, and decision-making frameworks. Agencies that prioritize trust, automation, and structured data models will be best positioned to scale AI responsibly and drive real business impact.
At Analytica, we help agencies build scalable, governed, and AI-driven analytics strategies. If you’re looking to modernize your AI & Data Strategy approach for data governance, decision intelligence, or AI adoption, let’s talk.
