Data trust and the evolution of enterprise analytics in the age of AI

In “Prioritizing AI investments: Balancing short-term gains with long-term vision,” I addressed the foundational role of data trust in crafting a viable AI investment strategy. The foundational tenet remains the same: Untrusted data is unusable data and the risks associated with making business-critical decisions are profound whether your organization plans to make them with AI or enterprise analytics.

Like most, your enterprise business decision-makers very likely make decisions informed by analytics. For many, the level of sophistication can easily range from more sophisticated solutions like Power BI, Tableau, SAP Analytics or IBM Cognos to mid-tier solutions like Domo, Qlik or the tried and true “elder statesman” for all business analytics consumers, Excel. Business intelligence platforms and clients in some form are pervasive for large, midsize and even smaller enterprise customers.

According to Fortune Business Insights approximately 67% of the global workforce has access to business intelligence (BI) tools, and 75% has access to data analytics software. Further, a study by Dresner Advisory Services found that 84% of surveyed organizations considered BI to be “critical” or “very important” to their business operations.  So why would any organization that considers a decision critical use business intelligence data to make that decision?  

The truth is more disturbing than any practice that uses (unwittingly or otherwise) untrusted data to make important decisions: While most use the data and recognize the tools as important, more trust their own intuition and instincts. Why? Ultimately, they trust “gut feel” over Power BI dashboards. In fact, a study by BARC (Business Application Research Center) found that 58% of respondents reported their companies base at least half of their regular business decisions on gut feel or experience rather than data and information.

If we dig deeper, we find that two factors are really at work:

1. Causal data versus correlated data
2. Data maturity as it relates to business outcomes.

One of the most fundamental tenets of statistical methods in the last century has focused on correlation to determine causation. For example, an analytics dashboard that correlates shipping data gaps in a logistics view could be correlated to quantities released for distribution in a warehouse. Still, the correlated relationship is not necessarily causal. 2011 Turing Award winner Judea Pearl’s landmark work “The Book of Why” (2020) explains it well when he states that “correlation is not causation” and “you are smarter than your data. Data do not understand causes and effects; humans do.” Until we can connect data to the nuances of the business through active governance and trusted context with semantic models that mirror the business, our gut instincts will take priority. Maturity and better business outcomes come through active governance and data stewardship and according to IDC data-mature organizations see over three times improvement in revenue along with shorter time to market and greater profit. 

Revisiting the foundation: Data trust and governance in enterprise analytics

Despite broad adoption of analytics tools, the impact of these platforms remains tied to data quality and governance. According to McKinsey, organizations with mature governance frameworks are 2.5 times more likely to report successful analytics initiatives compared to those with ad hoc approaches. This happens because proper governance creates the environment for analytics success, including data quality assurance, standardized definitions, clear ownership and documented lineage.

Absent governance and trust, the risks are higher as organizations adopt increasingly sophisticated analytics. Without rock-solid data foundations, even the most advanced ML models merely provide artful analysis. Getting the right data governance significantly affects operational efficiency and risk as well.  According to Gartner, lack of data management practices and rigor around governance can introduce risk and significantly impede data and analytics strategic readiness and ultimately AI readiness. 

The rise of machine learning in enterprise analytics 

As an enterprise architect in consumer goods, I experienced how machine learning captures the nuance of business semantics through pattern matching and it ultimately helped everyone in our product organization realize that no single source of truth existed for product data. What did exist were facts about the product and everything else was contextualized enrichment data that reflected the semantics of the business that used it.

Machine Learning (ML) is the game-changer in business analytics, helping organizations to extract nuanced insights from vast and complicated datasets. As a subset of artificial intelligence, ML uses algorithms trained on large datasets to recognize trends and identify patterns without explicit programming. This capability has become increasingly more critical as organizations incorporate more unstructured data into their data warehouses.

The quantitative models that make ML-enhanced analytics possible analyze business issues through statistical, mathematical and computational techniques. Business analysts collect and examine numerical data to identify trends, patterns and relationships that can inform strategic business decisions while being sensitive to the nuanced meaning of the consuming business user. These approaches influence decision-making by providing business leaders with evidence-based foundations rather than just intuition. According to ResearchGate, leaders leveraging quantitative analysis can forecast future trends, optimize operations, improve product offerings and increase customer satisfaction with greater reliability.

Descriptive analytics supplies the foundation of this approach, providing insight into past business performance by analyzing historical records. These models uncover meaningful patterns in data that can be displayed through summary statistics and visualization techniques, serving as a starting point for more advanced forms of analysis like predictive and prescriptive analytics. 

The GenAI revolution in enterprise analytics

In 2025, generative AI is profoundly reshaping the analytics landscape. GenAI technologies are advancing time-to-market through insights by automating traditionally manual processes in the research workflow. For example, tasks that once required hours of manual effort—sifting through existing research, synthesizing findings and generating hypotheses—can now be performed in a fraction of the time. This acceleration gain allows researchers to allocate more time to high-value analysis and insight generation.

Perhaps most significantly, GenAI democratizes access to data insights across organizational hierarchies. By breaking down technical barriers through intuitive interfaces powered by Large Language Models (LLMs), GenAI has given non-technical users the means to instantly find answers and extract insights from complex datasets. These conversational systems of interaction with data provide the context to answer questions based not only on what is being asked but by whom. Modern BI dashboards won’t be about correlated data sets. They will combine data points with a rationale based on conversations about the data and with the data that are discovery-based, role-based and context-based. This democratization is driving a seismic shift in data literacy throughout organizations, significantly changing how data is valued across every part of the enterprise.

Organizations are now moving past early GenAI experimentation toward operationalizing AI at scale for business impact. Deloitte research indicates that focusing on a small number of high impact use cases in proven areas can significantly accelerate ROI with AI implementations. This strategic approach helps organizations navigate real challenges, including how to differentiate in a market where GenAI models and enterprise data platforms are becoming increasingly commoditized, and how to implement AI governance without limiting innovation. 

Enterprise analytics in 2025: AI and analytics convergence and focused utility

In 2025, BI dashboards are dead and AI is moving the user experience from query, response and decision support to agent-based planning and execution with validated accuracy, automated process execution, adaptability and business impact. Enterprises are moving past experimentation, enabling specialized tools and systems of intelligence to address challenges at scale. The melding of AI with enterprise analytics is happening now as enterprise data platform vendors like Snowflake and Databricks recognize that they must differentiate beyond data aggregation and cleansing to systems of intelligence that support systems interaction and engagement.

The industry itself is also shifting away from generalized AI solutions that are now commodities toward focused, utility-based applications that address in specific challenges in specific industries like healthcare, manufacturing, finance and telecommunications with specific solutions.  We are seeing evolve with Agentic AI solutions from SAP, Salesforce and Microsoft to name but a few that will move beyond data as insight to data as action. 

Data and analytics leaders will need to evolve how they view the role of enterprise analytics in the Age of AI.  Every business initiative will expect access to organizational data and this will be problematic if data strategies don’t offer flexible, reliable and governed approaches to accessing information diverse data stores.  Effective governance can clearly drive adoption of intelligent analytics throughout the business. 

Data trust is a mandate, not an option 

As enterprise analytics evolve toward AI-driven, real-time and democratized capabilities, organizations that establish strong foundations of data trust and governance will be in the best position to exploit change for competitive advantage. The impact of data trust on enterprise analytics readiness is clear.  According to a study by Oracle and Seth Stephens-Davidowitz ,72% of executives report making faster decisions when they trust their data. Organizations that put trust and governance at the forefront of their data and analytics strategy have significant competitive advantages, including faster insights, higher-quality decisions, greater agility and sustainable scaling of analytics capabilities.

The gap between organizations with mature governance and those without it is a clear competitive differentiator making governance a priority for progressive enterprises. As we navigate the convergence of traditional analytics with generative AI and machine learning, the organizations that prioritize data trust will be the ones that truly transform business decision-making and secure lasting competitive advantage in the age of AI.

Dion Eusepi is a technology industry veteran focused on practical innovation in the architectural design, development and delivery of enterprise data and AI-ML platforms and intelligent ecosystem solutions for hybrid cloud environments, multi-tier data pipeline aggregation architectures and infrastructure, for on-premises, cloud and edge compute environments. Dion has had the privilege of contributing to multi-industry Fortune 100 and 500 companies including Ford Motor Company, General Motors, Stanley Black & Decker, IBM and Salesforce. His work includes comprehensive platform solutions for cloud, data, integration and AI-led enablement strategy and spans core ERP, CRM and HCM systems, SaaS and digital channel integration, ML ops, IIOT and I4.0 edge compute data distribution that connect broad, deep PLM eco-systems. 

This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects.