The future of data and analytics is arriving faster than most companies can adapt. According to Gartner, several critical data and analytics trends will reshape how organizations plan, operate, and compete. From AI-centric operating models to decision intelligence and vertical-specific analytics tools, these trends mark a structural shift in how value is created.
In this article, we break down six essential D&A trends for 2025, and show how leaders can move from dashboards to dynamic, AI-enabled decision-making.
1. AI-Centric Operating Models Will Become the Norm
By 2027, 40% of organizations will leverage AI-centric operating models to support new business strategies.
AI is no longer a supplement, it’s becoming the engine. Companies will increasingly design operations, decision-making, and customer experiences around AI-first processes.
The traditional sequence – strategy first, then AI enablement – is being reversed in many high-performing organizations. In these models, strategy is shaped with AI, not merely supported by it. Whether it’s predictive maintenance in manufacturing or personalized pricing in retail, AI is dictating the rhythm of decisions.
How to Prepare:
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Build cross-functional teams that include both domain experts and AI practitioners.
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Move from pilots to scaled, governed deployments.
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Prioritize interpretability and trust in AI outputs.
2. Decision Intelligence Will Shift Business Planning
By 2026, 30% of organizations will adopt decision intelligence to support structured and signal-based decision-making.
Decision intelligence integrates business logic, analytics, and AI to improve how organizations interpret and act on data. It’s the next step beyond dashboards: not just seeing signals, but being prompted with options.
Leaders are overwhelmed with data, but starved of clarity. Decision intelligence adds the missing link which is context. It identifies patterns, suggests actions, and learns from outcomes, reducing decision fatigue and reactive cycles.
How to Prepare:
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Shift analytics conversations from “reporting” to “decision support.”
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Invest in tools that integrate scenario modeling, business rules, and machine learning.
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Use historical decision data to train models on likely paths and outcomes.
3. Vertical-Specific Analytics Will Outpace Generalized Tools
By 2027, 50% of organizations will adopt vertical-specific analytics solutions.
Generic KPIs and models won’t cut it anymore. Whether you’re in pharma, logistics, or energy, industry-aligned analytics will outperform one-size-fits-all tools.
Domain knowledge is now embedded directly into analytics solutions. Tools trained on retail seasonality or life sciences compliance nuances offer faster time-to-insight and lower risk of misinterpretation.
How to Prepare:
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Evaluate vendors that specialize in your sector.
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Customize metrics to reflect your operational drivers.
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Build analytics teams with strong domain expertise, not just technical skills.
4. Data Stories Will Replace Static Dashboards
By 2026, 70% of organizations will replace dashboards with dynamic data stories that inform decisions faster.
The age of dashboard overload is ending. Instead of endless visualizations, organizations will embrace contextualized, narrative-style insights that guide actions—not just inform them.
Dashboards often require interpretation, and that introduces lag and ambiguity. Data stories, especially when automated, offer pre-analyzed insights with built-in logic, designed for immediate use.
How to Prepare:
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Build pipelines that enable real-time data interpretation.
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Use natural language generation (NLG) to support analytics delivery.
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Focus on user roles, tailoring data stories to the decisions each persona needs to make.
5. By 2025, Half of Analytics Leaders Will Fail to Quantify Data Value
50% of analytics leaders will not be able to demonstrate the business value of their data assets.
Despite all the investments, many teams are unable to tie analytics efforts to tangible outcomes. The result? Budget cuts, project stalls, and strategic misalignment.
As economic pressure increases, every function is being asked to prove ROI. Data teams without a clear value narrative risk marginalization, even if they’re doing technically excellent work.
How to Prepare:
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Define “value” in terms of cost saved, revenue enabled, or risk reduced.
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Build lightweight value-tracking frameworks tied to data product releases.
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Partner closely with finance to ensure alignment on impact metrics.
6. Data Products Will Become Core to Analytics Operating Models
By 2026, 50% of data and analytics teams will be evaluated based on the success of their data products.
Think of data not as an output, but as a product, delivered with purpose, governance, SLAs, and measurable outcomes. Data products can be anything from a churn prediction model to a forecasting API consumed by operations.
This shift creates accountability. Data products must meet real user needs, not just technical requirements. It also unlocks scalability: once a product is built and adopted, its benefits grow with usage.
How to Prepare:
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Establish product management principles in data teams.
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Set adoption and usage metrics alongside technical quality metrics.
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Maintain an internal data product catalog, with clear ownership and lifecycle support.
Last Thoughts: Time to Restructure is Now
These six trends aren’t optional upgrades. They’re structural shifts in how organizations compete, plan, and adapt. At their core is a simple mandate: analytics must move from insight to impact.
As a company that lives at the intersection of planning, analytics, and execution, Centida is helping clients respond to these shifts, not reactively, but strategically. From embedded writeback in Power BI to industry-aligned forecasting models, we’re building planning stacks that match the pace of change.
If you’re still relying on dashboards and quarterly reports, you’re not just behind, you’re invisible to the signals that will define the next competitive cycle.
Let’s fix that. Together.