Graphwise’s Sumit Pal offers commentary on the hidden reason AI fails and how knowledge graphs can fix it. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The competitive edge in AI today is not about the next model on the leaderboard. Achieving a successful journey from paper to production is the most critical cog in the Data-AI-Flywheel. However, it relies on something less glamorous: a strong data foundation that includes a data strategy and data infrastructure. For enterprises seeking to unlock the powers of AI, it’s not enough to just have data. The most critical cog is establishing a robust data culture and an understanding of how data is created, managed, shared, trusted, and used.

In fact, Deloitte found 91 percent of companies expect to address data challenges in the next year, showing the criticality of data readiness for powering AI solutions. To ensure a successful AI development and deployment in enterprises, organizations should consider the following approaches to address five key challenges:

Address Data Quality

Today data debt is most prominent in the form of data quality with missing, incomplete, incoherent, and incompatible data. As organizations ingest heterogeneous data from internal and external sources, data teams encounter challenges with inconsistent formats, duplicate records, incomplete fields, outdated entries, and inaccurate data. These arise due to fragmented data systems, lack of standardization, manual errors, and insufficient governance around data and business processes. Poor data quality disrupts business operations and leads to flawed analytics, unreliable insights, and misguided strategic decisions. Additionally, it erodes stakeholder trust and increases costs due to repeated cleansing and reconciliation efforts impacting customer experience, regulatory compliance, and competitive advantage.

Organizations are increasingly leveraging knowledge graph-powered platforms to overcome the persistent data quality challenges that hinder advanced analytics and AI initiatives. Knowledge graphs connect disparate data sources into a unified semantic layer which enables enterprises to automatically detect inconsistencies, eliminate duplicates, and enrich incomplete information through intelligent context linking. It also ensures data relationships are explicitly modeled and maintained, improving accuracy, traceability, and governance across systems. Data and knowledge platforms enhance data cleansing, entity resolution, and metadata management, providing continuous validation and insight generation. As a result, organizations can transform fragmented, unreliable data into trusted, interconnected knowledge assets—fueling more accurate analytics, explainable AI models, and faster, data-driven decision-making.

Eliminate Data Silos

In modern enterprises data, content, metadata, and knowledge silos represent one of the most critical barriers to achieving true digital intelligence and agility. This fragmentation leads to duplication, inconsistent taxonomies, and disconnected insights, making it difficult for teams to get a unified view of data. Metadata silos further exacerbate the problem by obscuring context and lineage, limiting discoverability and trust in the data. Similarly, knowledge silos prevent the flow of institutional expertise across teams, slowing innovation and decision-making. The result is a significant drag on productivity, poor collaboration, and a missed opportunity for leveraging enterprise-wide intelligence. Breaking down these silos requires a connected data foundation that unifies structured and unstructured information, harmonizes metadata, and enables knowledge to flow seamlessly across systems and stakeholders.

Knowledge graphs enable organizations to break down the silos that fragment enterprise intelligence by connecting disparate systems and unifying structured and unstructured data with a semantic framework. Knowledge-powered platforms provide a holistic, interconnected view of the enterprise’s information landscape – capturing relationships and context across data sources, enriching content with metadata, and linking business concepts to create a dynamic network of knowledge. This interconnected foundation allows advanced AI and analytics tools to access trusted, contextualized data, improving model accuracy, discoverability, and explainability. A data knowledge management-powered AI platform unifies, transforms fragmented data and knowledge islands into a cohesive intelligence fabric, empowering organizations to make faster, more informed, and more strategic decisions.

 Create Context and Semantics

Context and semantics are the necessary ingredients for modern data and AI platforms. As data proliferates across silos, it takes on different meanings leading to ambiguity and lack of trust, which creates downstream integration challenges. In most enterprises, data is rife with ambiguities and impreciseness, which makes it difficult to use effectively for building AI solutions. For data to be useful, it needs to be presented intuitively with contextual enrichment to end users. Context is the critical element for surfacing insights from data. Consider the word “Paris” and how to distinguish if it refers to the French city or Paris Hilton. Humans readily understand context, but machines require semantic structure to disambiguate. Reliable facts with precise semantics become especially important when implementing Generative AI. A semantic model grounds Generative AI systems, mitigating hallucinations and leveraging proprietary data.

A knowledge management platform elegantly handles heterogeneity of enterprise data integration. Providing a unified view across all data and metadata silos with a semantic layer, it is based on context and semantics enriched with metadata and domain specific ontologies, taxonomies and conceptual relationships. This semantic foundation enables GraphRAG —or Graph-based Retrieval-Augmented Generation—to go beyond traditional RAG. Instead of retrieving unstructured text chunks, GraphRAG connects queries to a trusted, context-rich knowledge graph that represents how data points relate to one another. This allows the system to retrieve reliable, explainable, and traceable information. This allows GraphRAG pattern to combine retrieval augmented generation with the semantic layer to retrieve reliable and explainable data for decision-making. This empowers end-users with accurate and traceable responses governed by semantic principles with actionable insights, while creating a foundation for advanced AI applications that require contextual understanding.

Integrate Structured and Unstructured Data

It is critical for modern enterprises to effectively leverage both structured and unstructured data for building powerful and accurate machine learning and AI solutions. Structured data provides the foundation for quantitative analysis and model training. However, the majority of enterprise data is unstructured, residing in emails, documents, chat logs, videos, social media, and other textual or multimedia formats. Ignoring this wealth of unstructured information leads to incomplete insights and biased AI outcomes. The challenge lies in integrating these diverse data types, which differ in format, quality, and accessibility, into a unified analytical framework. Without proper integration and contextual understanding, enterprises risk developing AI models that lack depth, accuracy, and real-world relevance. Successfully combining structured and unstructured data allows organizations to capture the full spectrum of intelligence, which enables richer predictions, more human-like AI interactions, and truly data-driven outcomes.

Knowledge graphs based on the Resource Description Framework (RDF) graph model, empowers organizations to build a unified semantic layer that seamlessly integrates structured and unstructured data. RDF-powered graph models can easily leverage semantic web standards that semantically integrate data from relational databases, documents, APIs, and content repositories mapped to a common, machine-interpretable format. This preserves the meaning, context, and relationships across diverse data sources, allowing AI and analytics systems to reason over information rather than simply process it. This is possible through intelligent entity linking, ontology management, and metadata enrichment, transforming fragmented datasets into a connected knowledge ecosystem. This enhances discoverability and interoperability and powers explainable and context-aware AI solutions.

Establish Data Governance and Explainability

Strong data governance and explainability are essential pillars in building trustworthy, compliant, and effective machine learning and AI solutions. As organizations increasingly rely on data-driven algorithms to automate decisions and derive insights, the lack of proper governance can lead to biased models, inconsistent data usage, and compliance with ever-evolving regulations. Without clear lineage, accountability, and oversight, it becomes difficult to ensure that data feeding AI systems is accurate, ethical, and secure.

Black-box models erode stakeholder trust and hinder adoption, especially in regulated industries like finance, healthcare, and insurance. Explainability, which is the ability to understand and articulate how AI models arrive at their predictions or recommendations, is a critical cog for enterprises to achieve responsible AI. Doing so not only mitigates risk but also enhances confidence in AI-driven decisions, enabling organizations to deploy accountable AI solutions.

Knowledge graph-powered platforms also enable organizations to have visibility into data lineage, provenance, and quality across disparate sources. This ensures that every dataset feeding a machine learning model is traceable, validated, and compliant with governance policies. Additionally, the semantic context and AI-driven insights make model behavior interpretable, supporting explainability and transparency in decision-making processes. By integrating governance, metadata management, and knowledge relationships into a single ecosystem, enterprises can develop trustworthy, auditable, and responsible AI solutions while accelerating the creation of reliable data products that drive informed business outcomes.

Key Takeaways

As enterprises increasingly rely on AI and machine learning to drive innovation, the persistent challenges of poor data quality, fragmented silos, and the absence of standardized semantics and robust governance threaten the reliability and trustworthiness of these solutions.

AI applications are evolving from simple prompt based systems to being powered by autonomous, contextually enriched autonomous multi-agents. Enterprise-scale knowledge management is becoming increasingly imperative to power these next generation AI systems. In the race to become AI-driven, incorporating architectural principles of knowledge graphs for semantics and data management for context engineering, is a facet organizations cannot afford to ignore.

AI success increasingly depends on how effectively organizations connect and contextualize their data. Knowledge-driven architectures, anchored by semantic layers and governed relationships, provide the structure needed to transform raw data into insight, and insight into confident decisions. These foundations make AI not only more accurate, but also explainable, traceable, and compliant by design.

The next generation of AI systems will not be defined by larger models, but by smarter data. By weaving semantics, structure, and governance into the heart of enterprise intelligence, organizations can move beyond experimentation to operational excellence. Better yet, they will build AI that learns responsibly, reasons transparently, and earns lasting trust.

The post The Hidden Reason AI Fails & How Knowledge Graphs Can Fix Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Quantum’s Skip Levens offers commentary on turning data hoarding into a strategic advantage. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

In the past, data hoarding was often viewed in a similar light to physical hoarding—a costly and inefficient practice that cluttered storage systems with outdated and irrelevant information. Organizations that held onto data far beyond its perceived usefulness or beyond compliance requirements were often criticized for wasting valuable storage resources, which were expensive to maintain. With no thought for its future value, the focus was on keeping only the most relevant and recent data–anything beyond that deemed unnecessary and subject to deletion.

However, the landscape has shifted dramatically in recent years due to two major developments: the rise of cloud storage and the advent of artificial intelligence (AI). Cloud storage, both private and public, has made it easier and more cost-effective for organizations to store vast amounts of data as data objects. Meanwhile, AI has emerged as a game-changer, with its potential to learn and improve from every piece of data it processes. As a result, organizations that were once criticized for their data-hoarding practices now find themselves at a significant advantage if they can implement a data management lifecycle strategy that leverages their data for insights and business value.

AI’s Insatiable Appetite for Data

Today, the most valuable asset in any organization is not just data itself, but the AI models that can be trained and refined using that unique data. The narrative has shifted from questioning the value of retaining all data to recognizing its critical role in AI development. While many assume that AI success is all about investing in powerful GPUs, the reality is that the availability of extensive, diverse datasets is equally important.

However, organizations are realizing that even with vast data stores, it’s still not enough to fully train AI models. The demand for high-quality data has led to the rise of synthetic data, where AI models generate additional datasets to fill gaps. AI researchers now leverage synthetic data as a way to create entirely new training sets, augment real-world data, and reduce biases. This shift highlights just how valuable data has become—not just for internal use, but also as a tradeable asset. Organizations are now renting or loaning their datasets to partners to fuel AI initiatives, recognizing that even proprietary datasets might not be enough to keep up with AI’s growing needs. But it’s not enough to retain all the data, you also have to have a way to organize the data so it can be easily searched, accessible and useful to the business.

What Does Data Hoarding Look Like?

Data hoarding, at its core, is the practice and mindset of retaining every piece of data an organization generates, guided by a “just in case” mentality. As data flows throughout your organization, this data should be protected and managed. While this may seem straightforward, the types of data that organizations generate are diverse. Some common categories of data that organizations should consider retaining include:

• Customer Support Records and Transaction Histories: Organizations often keep detailed records of customer interactions and transactions, sometimes dating back many years, to analyze trends, improve customer service, or refine marketing strategies.
• Internal Communications: Emails, shared documents, call transcripts, and other forms of internal communication amongst employees are often stored, providing a rich resource for understanding organizational dynamics and decision-making processes.
• Research and Development Data: Whether generated internally or sourced externally, R&D data is invaluable for innovation and product development. Retaining this data allows organizations to revisit past ideas and leverage them in new ways.
• Backup Redundancies and Obsolete Software Versions: While these may seem like outdated remnants of the past, retaining backups and old software versions can be crucial for troubleshooting, compliance, and reference.

Data hoarding has been happening in other forms for centuries. Consider the Library of Congress, which has an overarching mission to protect a nation’s cultural legacy and so preserves documents dating back to the founding of the United States, or European museums and universities that maintain archives spanning hundreds or even thousands of years. The Vatican, for example, holds documents that are millennia old. These institutions preserve such documents for the same reason modern organizations should retain their data: for potential reference, analysis, and use in the future.

AI Use Cases and the Growing Importance of Data

Data fuels AI, and as AI adoption grows, so do its use cases. AI is now playing a critical role in various sectors, including:

• Surveillance and Security: AI is transforming surveillance through applications like line detection, crowd control, facial recognition, and integrating watchlists like the FBI’s Most Wanted list. AI-driven video analytics enhance real-time threat detection and public safety.
• Healthcare: AI models trained on vast medical datasets are accelerating drug discovery, improving diagnostics, and personalizing treatment plans.
• Financial Services: Banks and financial institutions use AI to detect fraudulent transactions, assess creditworthiness, and automate risk management.
• Retail and Customer Experience: AI-driven recommendation engines analyze past purchase behavior and browsing history to deliver personalized shopping experiences.
• Autonomous Vehicles: Self-driving technology relies on massive datasets to improve navigation, obstacle detection, and traffic pattern predictions.

Making Use of the Data

To successfully transform volumes of data into a valuable, competitive asset that drives innovation and business insights, organizations must implement a data lifecycle management strategy.

Many organizations today don’t have a complete lifecycle strategy. There are three key areas to a data lifecycle strategy: a working area, where data is actively worked on, cleansed, and mined for value; an area where that data is then backed up and protected; and finally, an archive area where all data is collected and retained for future AI model training and analytics.

Most importantly, as part of their data lifecycle strategy, organizations need to understand what data they have and the value in that data. Often, they don’t have a way to organize, tag, index, and catalog it, and therefore can’t understand the potential value their data presents to their business. Just like a card catalog in a physical library, your data “library” needs to be organized so it can be searched and accessed to be useful to the organization. Having an automated workflow solution in place that automatically organizes and categorizes your data to make it AI-ready is critical.

Turning Data Hoarding into a Strategic Advantage

Data hoarding, once considered a wasteful practice, has now become an essential strategy for organizations aiming to succeed in the age of AI and gain a competitive edge. The reality is that organizations need to start retaining all of their data—not because they will use it immediately, but because they cannot afford to lose the potential value that data may offer in the future.

However, simply hoarding data is not enough. Organizations must also ensure that their data is stored and managed, organized, tagged, and enriched in a way that delivers performance while being affordable and accessible. By doing so, organizations can position themselves to leverage their data for innovation and a competitive advantage and thrive in an increasingly data-driven world.

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LRN Corporation’s Ty Francis offers commentary on information as a tool and safety as a culture. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Companies can no longer afford to treat data governance, compliance, and culture as administrative afterthoughts. As workforces evolve, technologies disrupt, and regulatory demands intensify, organizations must move beyond static compliance frameworks toward strategic, data-driven risk management that fuels sustainable growth.

When data governance is elevated and employee culture measurement is prioritized, organizations gain the visibility to identify and mitigate risks faster than ever. Compliance stops being a checklist exercise and becomes a shared capability powered by insight, automation, and accountability. When quality data leads, organizations can monitor in real time, analyze behavioral patterns, and benchmark integrity across their enterprise.

Information Overload

The amount of information any business needs to operate is staggering. From small businesses to global leaders, how data is gathered and utilized is a key indicator of overall success and performance. Yet as businesses scale, legacy systems, fragmented tech stacks, and post-merger redundancies often slow responses and bury critical intelligence in silos. And when companies begin to store data on platforms that don’t directly communicate with one another, they risk losing time searching for the correct data points, hindering decision-making processes and leadership action. Strengthening governance begins with rebuilding the data foundation updating technology, aligning platforms, and integrating systems so insights flow freely. With the right tools, compliance stops reacting to risk and starts anticipating it.

In our 2025 Program Effectiveness Report, we found that organizations are still grappling with many of the same systemic challenges as in previous years, chief among them outdated internal systems (64%) and increasingly complex regulatory environments (59%). These barriers not only slow the modernization of ethics and compliance (E&C) programs but also limit their ability to shape culture and anticipate emerging risks. The report highlights a widening performance gap between high and medium impact programs, while high-impact programs are nearly twice as likely to use benchmarking data and automation to inform decisions, many others remain constrained by fragmented data and legacy technology. Addressing these limitations requires more than incremental fixes.

It calls for renewed investment in data governance, cross-platform integration, and benchmarking tools that allow compliance leaders to act on insights in real time. By modernizing their systems and aligning technology with cultural goals, organizations can ensure that compliance data becomes a source of strategic foresight rather than an administrative burden.

Empowering Compliance

The rise of artificial intelligence has revolutionized how industries approach problem-solving within a compliance model. As organizations grapple with growing volumes of data and documentation, many are turning to AI to manage and analyze information more efficiently. Among its most valuable strengths, AI excels at automating repetitive, time-consuming tasks that are often vulnerable to human error. AI systems can monitor regulatory updates in real time, flag inconsistencies, and even interpret new legal language as rules evolve. Yet LRN’s 2025 Global Study on E&C Program Maturity shows that more than a third of organizations still manage investigations in spreadsheets, and fewer than 30% use cross-functional teams. The gap isn’t technological, it’s cultural.

Additionally, AI automates labor-intensive tasks such as document review, audit logging, risk assessment, and transaction monitoring, reducing the likelihood of mistakes caused by human error. With natural language processing, AI systems can interpret new legal language to keep compliance protocols current as laws change. Many of these platforms include consolidated dashboards that deliver visual analytics enabling governance teams to quickly identify weaknesses, evaluate control effectiveness, and continuously monitor risk exposure. Equipped with these advanced tools, compliance teams gain faster access to actionable insights and can leverage data that might have otherwise remained untapped. When equipped with integrated data and predictive insight, compliance teams can focus less on chasing paperwork and more on shaping the culture that prevents issues before they occur.

Protecting Data

Over the past four years, the average number of weekly cyberattacks per organization has more than doubled, rising from 818 in the second quarter of 2021 to almost 2,000 during the same period this year. Cybersecurity has emerged as a significant and growing threat to operational stability as compliance has shifted from the back office to the strategic sphere. Breaches can cost a business thousands of dollars in updating operations and damage its reputation. For many companies, unclear guidelines surrounding accessing, storing, and collecting private information leave them vulnerable to cyberattacks.

It is not enough to view security as a technical challenge. Rather, approaching security as a shared cultural responsibility requires companies to instill proactive risk awareness throughout their workforce. As cyber threats grow in volume and sophistication, organizations turn to more innovative technologies that empower teams to build resilience from the inside out. Rather than relying solely on technical defenses, many are adopting solutions that focus on strengthening human awareness and behavior, protocols, and training surrounding data security. This proactive approach helps employees feel prepared and in control, empowering a security culture, rather than a checklist.

Phishing remains one of the most pervasive and effective tactics used by attackers. Organizations are implementing phishing simulations and training platforms to help employees recognize and respond to suspicious scenarios, such as LRN’s Catalyst Phishing. Tools like these offer libraries of realistic phishing templates based on current threats and adaptive training modules tailored to user behavior. Administrators can customize simulations, segment users into targeted groups, and access detailed reporting to measure campaign and individual performance. This kind of data-driven approach helps identify areas of vulnerability and foster a culture of shared responsibility regarding cybersecurity.

A Culture of Protection

Organizations that invest in data governance, leverage AI, and embed cybersecurity into everyday conduct build a foundation of trust that endures beyond any single regulation. These technologies enhance visibility and efficiency, but resilience depends on how leaders use insight to drive ethical behavior and accountability at every level.

 However, technology alone is not the answer. The true differentiator is leadership that uses these powerful insights to embed ethics into every decision and demand accountability at every level. Forward thinking and evolved Boards, regulators, and investors no longer accept routine, check-the-box compliance. They expect a culture of integrity backed by clear metrics and decisive action. Those organizations that recognize this shift and invest early in more innovative technology, deeper insights, and stronger ethical foundations will stay ahead of risk and gain a competitive edge, empowering trust and long-term success. Ultimately, the ability to leverage quality data and proactive governance will define which organizations thrive in a landscape of increasing complexity and scrutiny.

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Solutions Review Executive Editor Tim King offers this commentary on the rise of the logical data strategy in this AI moment.

In the age of AI, the effectiveness of an organization’s data strategy no longer depends on how much data it controls, but how intelligently that data is connected, governed, and delivered. As generative AI and self-service analytics accelerate, enterprises are discovering that traditional architectures—designed for centralized control—are struggling to keep pace with the new demands for agility, context, and trust.

According to The Rise of Logical Data Management by O’Reilly author Christopher Gardner, the industry’s reliance on lakehouses and sprawling repositories has created a paradox. While these centralized models promise simplicity, they often introduce new layers of complexity and inertia. They can slow down innovation, obscure lineage, and make it harder for AI initiatives to find the high-quality, context-rich data they require.

In this emerging reality, a new concept is gaining ground: the logical data strategy. It emphasizes connectivity over consolidation, semantic consistency over duplication, and governance as an enabler—not an obstacle—of AI. This transformation is at the heart of an upcoming discussion moderated by Kevin Petrie, VP of Research at BARC, featuring Pablo Alvarez, Global VP of Product Management at Denodo, and Samir Sharma, CEO of datazuum. Together, they’re exploring what it really means to build an AI-ready enterprise.

The New Pressures on Data Strategy in the Age of AI

Artificial intelligence is both the ultimate consumer and the ultimate critic of enterprise data. Models are only as good as the data pipelines, governance frameworks, and metadata systems that feed them. The generative AI boom has intensified scrutiny on these systems, forcing leaders to ask whether their data foundations are agile enough to support scalable intelligence.

Kevin Petrie of BARC has observed that many data strategies remain trapped in analytics-first thinking—built for dashboards, not dialogue. AI doesn’t just analyze data, but it also  interrogates it. And that exposes every weakness in how data is managed, cataloged, and trusted.

At the same time, line-of-business teams are demanding greater autonomy. They expect real-time access, self-service analytics, and contextualized insights without waiting in IT queues. The result is a widening gap between centralized data control and distributed business need. AI magnifies that tension: it thrives on broad data access but crumbles without coherence.

This is where a logical approach begins to show its strength. Rather than forcing all data into a single platform, it connects distributed assets through a unified semantic and governance layer. It enables agility without sacrificing oversight—an essential balance in the era of AI democratization.

Beyond the Lakehouse: The Rise of Logical Data Management

The traditional wisdom of “collect everything in one place” is giving way to a more nuanced understanding of how enterprises actually work. Data now lives everywhere—across cloud and on-premises systems, SaaS applications, and APIs. Centralizing it all is often neither practical nor necessary.

Logical data management offers an alternative. It allows organizations to manage data where it resides, applying consistent policies and semantics through abstraction technologies like data virtualization, metadata intelligence, and semantic layers.

Pablo Alvarez of Denodo explains that this approach doesn’t discard the lakehouse; it complements it. In sum, a logical data strategy doesn’t start by moving data. It starts by understanding it, governing it, and connecting it in meaningful ways.

By using virtualization and semantic unification, organizations can make distributed data act as though it’s centralized—without the costs, delays, or duplication that come from constant physical integration. This enables a single source of truth across multiple systems, providing the agility to adapt as new AI models, tools, and governance requirements emerge.

The result is not only faster time-to-insight but also a foundation that evolves in step with business change. Logical data management turns the data landscape into a dynamic ecosystem, rather than a static warehouse.

Data Products & the Business Participation Revolution

Perhaps the most profound shift of all is cultural. The age of AI demands that business and technical teams work together around shared data objectives. The concept of data products is central to this shift.

In a data-product model, business domains take ownership of specific datasets—treating them as assets that can be packaged, maintained, and reused across the enterprise. These products have clear definitions, service-level agreements, and semantic descriptions that make them easily consumable by both humans and machines.

This approach empowers line-of-business experts to shape the data they use every day, closing the historical gap between IT gatekeepers and business consumers. It also enables self-service without chaos: each product is governed, versioned, and discoverable within a federated system.

Pablo Alvarez points out that involving domain experts doesn’t mean sacrificing control—it means enhancing it. “When people closest to the data’s meaning participate in its design, the entire organization benefits,” he says. “That’s what turns governance from a burden into a source of value.”

For AI initiatives, this is critical. Data products provide the semantic richness and contextual metadata that allow models to reason more accurately. They also make it possible to ground generative outputs in authoritative, up-to-date information—an essential step in preventing hallucinations and misinformation.

Governance Reimagined: From Bottleneck to AI Enabler

For decades, data governance was viewed as a necessary evil—a series of guardrails imposed by compliance teams. Today, it has become the cornerstone of AI enablement. As organizations deploy large language models and autonomous systems, they must be able to verify data lineage, enforce access controls, and trace decision logic.

Governance is no longer about restriction. It’s about empowerment. The enterprises that will thrive with AI are those that make governance a core part of their innovation strategy.

Modern governance leverages metadata intelligence, automation, and federated policy enforcement. Instead of manual cataloging or static rules, organizations now apply dynamic controls that adapt to context.

In this model, semantic unification plays a pivotal role. By defining consistent business concepts across domains, the enterprise ensures that every model, dashboard, and query operates from the same playbook. Whether it’s sales forecasting or customer sentiment analysis, the underlying semantics stay aligned.

The unexpected rise of governance as an AI enabler marks a turning point. Where once it slowed progress, it now fuels trust—and trust is the real currency of intelligent systems.

The Semantic Layer: AI Translation Engine

The semantic layer has emerged as the connective tissue between human understanding and machine intelligence. It translates complex data relationships into business-friendly language, enabling both self-service analytics and AI integration at scale.

In practical terms, the semantic layer allows large language models (LLMs) to query enterprise data with context and precision. It anchors generative outputs in real business logic, ensuring that AI answers reflect accurate definitions rather than guesswork.

For example, when an LLM is asked, “What were last quarter’s new customer acquisitions in EMEA?”, the semantic layer ensures it applies the company’s definition of “new customer,” the correct regional filters, and the official revenue recognition rules. Without that layer, the AI’s output is just an approximation.

Denodo and others in this space have pioneered ways to make the semantic layer actionable, bridging virtualized data access with business semantics. This not only supports RAG (retrieval-augmented generation) use cases but also extends traditional BI and analytics frameworks, giving every data consumer—human or AI—a consistent, governed experience.

The result: faster innovation, fewer data silos, and far greater confidence in AI-driven decisions.

Organizational Shifts: Culture, Skills & Collaboration

No data strategy succeeds on architecture alone. The rise of AI demands new roles, new governance models, and new mindsets. Enterprises that once relied solely on centralized IT now find themselves coordinating a web of domain-specific teams, data product owners, AI governance leads, and semantic modelers.

This is where the “human layer” becomes as important as the semantic one. BARC’s research highlights that successful organizations establish data empathy—an understanding of how data serves real business goals and the people behind them.

Culturally, this means bridging the gap between technologists and business stakeholders. Skills in metadata management, prompt engineering, and semantic modeling are blending with strategic roles in data ethics, compliance, and AI stewardship.

The AI-ready enterprise isn’t just a technical construct; it’s an organizational philosophy that prioritizes collaboration, transparency, and adaptability.

Designing for Agility, Trust, and AI Readiness

Enterprises are at a crossroads. They can continue to layer AI capabilities atop brittle architectures—or they can rethink their data strategy from the ground up. The latter approach demands a mindset shift: from data hoarding to data harmonization, from control to collaboration, from static models to logical ecosystems.

An AI-ready data strategy rests on four pillars:

1. Logical connectivity: Managing data where it lives, without constant duplication.

2. Semantic unification: Ensuring every system and model shares a consistent vocabulary.

3. Governance as enablement: Embedding trust and lineage into every data interaction.

4. Business participation: Empowering domain experts to co-create and curate data products.

These principles transform data from an asset to an advantage. They allow AI systems to operate on a foundation of truth, while giving humans the confidence to make better, faster decisions.

For even more on re-thinking your data strategy in the age of AI and self-service, consult the experts via Solutions Review’s Industry Trends Session:

Note: These insights were informed through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to extract targeted knowledge and optimize content for relevance and utility.

The post The Rise of the Logical Data Strategy: On Building AI-Ready Enterprise appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review Executive Editor Tim King Highlights the most important data governance tools to consider when evaluating commercial solutions.

Data is the most valuable enterprise asset — but only if it is properly managed. With volumes of information expanding at exponential rates, enterprises are under constant pressure to ensure their data is accurate, accessible, secure, and compliant. Data management sits at the heart of this challenge, providing the structures and systems that turn data from a liability into a strategic resource. From ensuring consistent data quality to enforcing governance and protecting sensitive information, data management tools are what allow organizations to build trust in their analytics and make informed business decisions.

The complexity of enterprise data environments makes management more critical than ever. Businesses operate across hybrid and multi-cloud infrastructures, ingest data from countless applications and devices, and must comply with increasingly strict regulations on privacy and security. Without the right tools, organizations risk fragmented systems, poor data quality, and exposure to compliance failures. Modern data management solutions are designed to tackle these challenges head-on, offering features such as metadata management, master data management (MDM), data catalogs, governance frameworks, and security controls. They also provide the scalability and automation enterprises need to manage millions — if not billions — of data points seamlessly.

This article highlights the most important data governance tools for enterprises today — commercial platforms that stand out for their ability to unify, govern, and protect data across the enterprise. These are the tools that CIOs, chief data officers, and IT leaders trust to deliver consistent, reliable data that fuels analytics, supports regulatory compliance, and drives digital transformation. By surfacing solutions that combine governance, quality, and accessibility, this guide provides a roadmap for decision-makers navigating a crowded vendor landscape. Whether the goal is consolidating customer data, ensuring regulatory readiness, or enabling data democratization across teams, these platforms represent the enterprise standard for managing data as a true strategic asset.

The Most Important Data Governance Tools

Alation

Platform: Alation Data Catalog

Description: Alation Data Catalog helps you find, understand, and govern all enterprise data through a single pane of glass. The product uses machine learning to index and make discoverable a wide variety of data sources including relational databases, cloud data lakes, and file systems. Alation democratizes data to deliver quick access alongside metadata to guide compliant, intelligent data usage with vital context. Conversations and wiki-like articles capture knowledge and guide newcomers to the appropriate subject-matter expert. The intelligent SQL editor empowers users to query in natural language, surfacing recommendations, compliance flags, and relevant policies as users query.

ASG Technologies

Platform: ASG Enterprise Data Intelligence

Description: ASG Technologies offers a data intelligence platform that can discover data from more than 220 traditional and big data sources. The tool features automated data tagging by pattern matching, integration of reference data, and enriched metrics. Automated business linage allows users to better understand their data, and governance capabilities include those for tracing data in the data lake and traditional sources. ASG’s EDI product offers an impressive capabilities portfolio, with reference customers touting the vendor’s support for a variety of business use cases.

Ataccama

Platform: Ataccama ONE

Description: Ataccama ONE is a comprehensive data management and governance platform that also includes master data management and data quality capabilities. The solution touts a machine learning-centric user interface, as well as a data processing engine that is responsible for data transformations, evaluating business rules, and matching and merging rules. The platform supports any data, domain, and a variety of integrations.

Atlan

Platform: Atlan

Description: Atlan’s data workspace platform offers capabilities in four key areas, including data cataloging and discovery, data quality and profiling, data lineage and governance, and data exploration and integration. The product features a Google-like Search interface, automatic data profiling, and a searchable business glossary for generating a common understanding of data. Users can also manage data usage and adoption across an ecosystem via granular governance and access controls, no matter where your data goes.

Collibra

Platform: Collibra Platform

Related products: Collibra Catalog, Collibra Privacy & Risk

Description: Collibra’s Data Dictionary documents an organization’s technical metadata and how it is used. It describes the structure of a piece of data, its relationship to other data, and its origin, format, and use. The solution serves as a searchable repository for users who need to understand how and where data is stored and how it can be used. Users can also document roles and responsibilities and utilize workflows to define and map data. Collibra is unique because the product was built with business end-users in mind.

https://www.youtube.com/watch?v=i8W8mgy_FRI

Claravine

Platform: Claravine

Description: Claravine’s Data Standards Cloud lets customers drive internal alignment with standards across data sets, types, and sources. The product enables the creation of easy-to-follow requirements using referenceable fields and descriptions. Users can also audit, manage and standardize data and automatically verify tag placement and configuration across landing pages to maintain standards. Claravine touts the ability to grant users or groups the right roles and permissions with standard and custom settings, and review, audit, and visualize platform activity with dashboards.

Egnyte

Platform: Egnyte

Description: Egnyte offers a content security, compliance, and collaboration solution that governs an organization’s files regardless of where they reside. The product features a variety of user access capabilities, lifecycle management, data security, compliance, business process management, and API integration via a unified solution. Information governance functionality includes locating valuable and sensitive data, compliance automation, and more. Egnyte also touts granular policy controls for remote work and modernizing file systems.

erwin

Platform: erwin Data Governance

Related products: erwin Data Intelligence Suite, erwin Data Catalog, erwin Data Literacy, erwin EDGE Portfolio

Description: erwin offers a unified software platform for combining data governance, enterprise architecture, business process, and data modeling. The product is delivered as a managed service that allows users to discover and harvest data, as well as structure and deploy data sources by connecting physical metadata to specific business terms and definitions. erwin imports metadata from data integration tools, as well as cloud-based platforms, and can evaluate complex lineages across systems and use cases.

Immuta

Platform: Immuta

Description: Immuta’s automated data governance platform lets users discover and access data through a dedicated data catalog. The product features an intuitive policy builder that provides author policies in plain English, without code so security leaders can write policies across any data. Immuta also enables compliant collaboration via projects, controlled workspaces where users can share data. When users switch projects, they assume the right permissions and controls. Immuta runs as a containerized solution on-prem, in the cloud or via a hybrid model.

IBM

Platform: IBM InfoSphere Information Governance Catalog

Description: IBM has data management products for virtually every enterprise use case. Its products can be deployed in any environment, and partnerships with some of the other top names in the marketplace make it an even more intriguing option for organizations with large workloads and expansive data jobs. IBM also offers its Informix database that can integrate SQL, NoSQL/JSON, time series and spatial data.

https://www.youtube.com/watch?v=UGvaNxD0_4E

Informatica

Platform: Axon Data Governance

Related products: Informatica Product 360, Informatica Customer 360, Informatica Supplier 360

Description: Informatica Axon Data Governance is an integrated and automated data governance solution that enables quick access to curated data. The product ensures teams can find, accessed and understand the data they need via a curated marketplace. Axon also enables data dictionary development for a consistent source of business context across multiple tools. Users can visualize data lineage, automatically measure data quality, and ensure data privacy with this solution as well.

Oracle

Platform: Oracle Enterprise Metadata Management

Related products: Oracle Cloud Infrastructure Data Catalog

Description: Oracle Enterprise Metadata Management is a metadata management platform that can harvest and catalog metadata from any provider. The product allows for interactive searching and browsing of the metadata as well as providing data lineage, impact analysis, semantic definition and semantic usage analysis for any metadata asset within the catalog. Oracle Enterprise Metadata Management also touts advanced algorithms that stitch together metadata assets from each of the providers.

OvalEdge

Platform: OvalEdge

Description: OvalEdge offers an on-prem data catalog and governance toolset that crawls databases, data lakes and back-end systems to create a smart catalog of the information. The product provides a discovery platform that both novice and experienced analysts can use to discover data quickly. OvalEdge includes built-in governance tools that help define a standard business glossary, data assets, PIIs, and limits access by various roles. It also organizes data automatically via machine learning and advanced algorithms.

Precisely

Platform: Precisely Data Integrity Suite

Related products: Precisely Data Governance service, Precisely Data360, Precisely Spectrum Quality, Precisely Trillium

Description: The Data Governance service of the Precisely Data Integrity Suite is one of 7 SaaS services. Its enterprise metadata management capabilities enable customers to automate governance and stewardship tasks and answer essential questions about data meaning, usage, impact, and lineage. Data quality services are available through the Data Quality service, Data Observability service, Data360 DQ+, Spectrum Quality, Trillium Quality, Trillium Discovery, and Data360 Analyze.

SAP

Platform: SAP Master Data Governance

Related products: Master Data Governance on SAP S/4HANA

Description: SAP offers enterprise MDM functionality through its SAP Master Data Governance product. The solution can be deployed on-prem or in the cloud and enables users to consolidate and centrally govern master data. SAP includes support for all master data domains and implementation styles, pre-built data models, business rules, workflow, and user interfaces. Master Data Governance also lets you define, validate, and monitor your established business rules to confirm master data readiness and analyze the performance of data management.

Segment

Platform: Segment

Description: Segment offers a customer data platform (CDP) that collects user events from we band mobile apps and provides a complete data toolkit to the organization. The product is available in three iterations, depending on the user persona (Segment for Marketing Teams, Product Teams or Engineering Teams). Segment works by letting you standardize data collection, unify user records, and route customer data into any system where it’s needed. The solution also touts more than 300 integrations.

Semarchy

Platform: Semarchy xDM

Description: Semarchy offers a master data management solution called xDM. The product utilizes machine learning algorithms to enable stewardship and advanced matching, survivorship, curation, and classification. The tool features a native data model that facilitates transparent lineage, audibility, and governance as well. xDM can integrate any data source via real-time and batch APIs to integrate the data hub with existing applications and business processes. Semarchy offers a 30-day license key free trial of xDM for on-prem and cloud.

Talend

Platform: Talend Data Catalog

Related products: Talend Open Studio, Talend Data Fabric, Talend Data Management Platform, Talend Data Preparation, Talend Big Data Platform, Talend Data Services Platform, Talend Integration Cloud, Talend Stitch Data Loader

Description: Talend Data Catalog automatically crawls, profiles, organizes, links, and enriches metadata. Up to 80 percent of information associated with the data is documented automatically and kept up-to-date through smart relationships and machine learning. Data Catalog key features include faceted search, data sampling, semantic discovery. categorization, and auto-profiling. The tool also includes social curation and data relationship discovery and certification, as well as a suite of design and productivity tools.

The post The Most Important Data Governance Tools to Consider for 2026 appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Precisely’s Cam Ogden offers commentary on the state of data observability and how organizations are preparing for agentic AI. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The agentic AI revolution is beginning to take shape. AI and ML models are transitioning from being merely generative, like answering user questions or creating content, to agentic, where the AI model can make complex, autonomous decisions without additional user input. A data integrity strategy, including a robust approach to data governance, quality, and observability, is the only way agentic AI can do this accurately, consistently, and at scale.

Observability is our window into how our AI/ML models perform. Data observability provides continuous insight into the quality and reliability of data pipelines, while AI observability focuses on monitoring model health, behavior, and performance over time. Together, these concepts help teams understand how data is stored, processed, and influence model outputs – information that is essential for delivering relevant, trustworthy AI outcomes.

Until now, most observability processes have focused on structured data. But agentic AI models require richer, more contextual information to make intelligent decisions. That context often lives in unstructured data, which comes from a variety of internal and external sources, such as emails, videos, audio files, PDFs, and much more.

As the volume and variety of both structured and unstructured continue to grow, many organizations are struggling to manage, interpret, and extract meaningful insights from the information flowing through their systems. Tracking data from these disparate sources is extremely difficult without modern observability tools and processes that centralize insights and unify visibility across systems.

Precisely partnered with BARC, a leading technology analyst firm in Europe, and surveyed a qualified panel of IT, management, and other tech professionals to learn more about the current state of AI and data observability and how organizations are tackling this critical challenge. Here’s what we discovered.

Organizations are Building a Solid Foundation, but There is Room to Grow

Many organizations are making progress in observing data, pipelines, and models to support AI and ML initiatives. Over two-thirds have formalized, implemented, or optimized observability programs for each of these disciplines, and a similar amount (68 percent) rely on quantitative and/or qualitative metrics to measure the impact of those programs. Modern analytics tools are starting to take hold, too, with one-third of respondents using tools like predictive machine learning and real-time analytics to gather necessary observability data. We’re also starting to see organizational buy-in, with nearly 50 percent of business process owners overseeing data quality initiatives.

While this is a fantastic start, there’s still significant room for improvement. Right now, the number one obstacle for observability is training and skills gaps, with over half of all respondents citing this as a primary concern.

Key takeaway: Close the skills gap and implement comprehensive data observability training programs for IT professionals and key stakeholders. This process will help to solidify observability as a crucial component for improving data governance and quality at a foundational level, informing future decisions regarding your agentic AI models.

A rising demand for unstructured data requires a renewed focus on observability

Only 59 percent of respondents trust the inputs and outputs of the AI/ML models they rely on. Training teams to write more effective prompts can help, but that alone isn’t enough. To improve model performance – particularly for agentic use cases – organizations need to integrate unstructured data sources that offer additional context.

62 percent of organizations are exploring semi-structured data, and 28 percent are already using it. Meanwhile, 60 percent are evaluating unstructured documents. This trend underscores the growing importance of observability across diverse data types.

40 percent say that observing and governing unstructured data is now vital to their workflows – suggesting a growing gap between those with robust observability and those at risk of blind spots.

Key takeaway: Unstructured data is becoming increasingly important for improving both generative and agentic AI capabilities. Investing in metadata management and quality metrics will help improve visibility and trust in how this data is used.

Organizations Rely on Legacy Solutions Rather than Dedicated Observability Tools

Many organizations rely on a combination of tools and technologies to provide insight into the disparate elements of their AI/ML infrastructure. Currently, 69 percent of respondents use their data warehouse or lakehouse tools, 67 percent use a business intelligence or analytics tool, and 45 percent rely on data integration tools.

Meanwhile, only 8 percent of respondents report using a dedicated observability tool to oversee operations. While these legacy systems offer limited visibility, they often fall short of delivering a full picture.

For example, a data warehouse may show you the health of your stored data, but not how that data flows through the pipeline or influences model performance. In contrast, dedicated data observability solutions provide full-lifecycle monitoring, anomaly detection, and drift alerts – capabilities that will become increasingly vital as models grow more complex and autonomous.

Key takeaway: Shift reliance from basic data-gathering and monitoring tools to dedicated AI observability solutions and integrate them into your AI governance strategy. You’ll get deeper and more comprehensive insight into what your data is doing, leading to more informed decisions about how to improve the health and performance of your AI/ML models.

By taking a proactive, holistic approach to observability, organizations can lay the groundwork for building high-integrity, secure, and reliable AI/ML models. That foundation will be essential as agentic AI moves from possibility to business-critical reality.

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Archive360’s George Tziahanas offers commentary on unlocking hidden value and the evolution of enterprise data archives in the age of AI. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

For decades, enterprise data archives have occupied an understated position within organizational IT infrastructure. These vast repositories of information were treated as necessary for meeting legal and regulatory obligations, but only modestly accessed once data was safely stored away. The prevailing approach was simple: keep costs low, ensure compliance, and preserve the data until a court order or regulatory investigation demanded its retrieval.

The emergence of AI and advanced analytics has fundamentally changed the game. What was once viewed as a regulatory cost center has transformed into a potential treasure trove of business intelligence. Organizations are beginning to recognize that their archives contain rich datasets that could provide crucial insights into customer behavior, market trends, operational efficiency, and meet the voracious data needs for training AI models, if they can access and use this information effectively.

The Compliance Imperative

Beyond the promise of business insights, evolving regulatory requirements are accelerating the transformation of enterprise archives. Modern privacy regulations like the European Union’s General Data Protection Regulation (GDPR) and similar regulations now in place with many U.S. states, have introduced complex requirements for how organizations handle personally identifiable information (PII) throughout its lifecycle. These regulations demand not just secure management, but active governance and the ability to quickly locate, assess, and potentially delete specific data elements.

Traditional archive systems and enterprises’ large portfolios of legacy applications make compliance with these sophisticated regulations extremely challenging. Organizations must be able to demonstrate precisely what data they hold, where it’s stored, how it’s protected,  who has access to it, and how it can be deleted. This level of visibility and control is extremely difficult to achieve when data is fragmented across disconnected systems never designed for such levels of governance.

The Access Challenge

The fundamental obstacle preventing organizations from leveraging their archive data lies in accessibility and governance. Before feeding archived information to AI systems or analytics platforms, IT teams must first visibility into their data holdings. This requires understanding not just what data exists, but also its format, quality, sensitivity level, and legal status.

The challenge extends beyond mere access. Even when organizations can retrieve their archived data, it often requires significant preparation before it can be effectively utilized by modern AI and analytics systems.  Legacy archives keep data in proprietary or other formats that require significant cleaning, formatting, classifying and structuring. Sensitive information must be identified and appropriately masked or removed. Data pipelines must be established to ensure efficient and secure transfer to analytics platforms. In contrast, modern archiving architectures address much of this work when they bring data into their platforms, so they can provide AI-ready data.

The stakes are particularly high in regulated industries where improper handling of archived data can result in substantial fines and reputational damage. Organizations must balance the desire to extract value from their archives with the need to maintain strict compliance with data protection regulations.

Modern Archives and their Role in AI

Ironically, the same artificial intelligence technologies driving demand for archived data are also providing solutions to the challenges of accessing and governing it. Modern cloud-based archiving platforms equipped with AI capabilities can automatically ingest data from virtually any source, creating unified repositories that eliminate the problem of scattered data silos.

These archiving platforms can automatically discover and classify data, including sensitive information and applying appropriate governance policies. AI can recognize patterns in data usage and access, helping organizations understand which archived information is most valuable for their analytics initiatives.

Modern archiving platforms automate much of the data preparation process, formatting information appropriately for different analytics platforms and build data pipelines that operate efficiently. This automation significantly reduces the time and effort required to transform archived data into actionable insights, while maintaining high levels of governance and security.

The Strategic Transformation

The integration of AI into enterprise archiving represents more than a technological upgrade. It’s a fundamental shift in how organizations conceptualize their data assets. Archives are evolving from passive storage systems into active, intelligent platforms that can manage, govern, and prepare data for analysis.

This transformation is particularly valuable for organizations that possess massive volumes of data that would be prohibitively expensive to store in traditional data warehouses. Modern archives can serve as cost-effective alternatives for storing large datasets used in machine learning and AI training, while simultaneously maintaining the compliance and governance capabilities required for regulatory adherence.

The evolution of enterprise archives from cost centers to strategic assets reflects broader changes in how organizations approach data management. As AI and analytics become increasingly central to business operations, the ability to efficiently access and utilize archived data will become a significant competitive advantage.

Organizations that successfully transform their archives will be positioned to extract maximum value from their historical data while maintaining the strict governance and compliance standards required in today’s regulatory environment. The archive of the future won’t just store data—it will actively contribute to organizational intelligence and decision-making, turning decades of accumulated information into a powerful driver of business success.

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This exclusive Q&A with Prat Moghe, CEO of Promethium, explores enabling self-service data for AI at scale.

Self-service analytics has promised to democratize data access across the enterprise — but for many organizations, the reality has been a frustrating cycle of unmet expectations and broken workflows. Despite investments in modern BI platforms, semantic layers, and data catalogs, business users still find themselves waiting days or weeks for answers, while data teams drown in ad hoc requests.

According to Prat Moghe, CEO of Promethium, the issue isn’t the technology stack — it’s the design of the workflow itself. “Most self-service implementations assume business users will adapt to how data systems work, rather than making those systems adapt to how decisions actually get made,” Moghe explains. In real-world scenarios, by the time usable data arrives, the decision has already been made based on instinct, not insight.

In this exclusive Q&A, Moghe lays out a new approach to solving this self-service bottleneck — one built on intelligent orchestration and agentic workflows. He explains how Promethium’s Mantra Data Answer Agent delivers governed, contextual insights in minutes, not weeks, and how its architecture works with existing data infrastructure to accelerate results without risky overhauls.

The interview, curated by Solutions Review Executive Editor Tim King, dives into the shortcomings of today’s data stacks, the need for explainable AI in analytics, and why the future of data lies in rethinking how questions become answers.

To hear more from Moghe, check out his appearance on the Insight Jam Podcast, where he expands on AI’s role in data trust, scaling governance, and the future of analyst-augmented decision-making.

Enabling Self-service Data for AI

Question 1: Data leaders are drowning in ad hoc requests despite billions invested in self-service tools. What’s actually broken?

Answer: The fundamental issue isn’t technology, it’s workflow design. Most self-service implementations assume business users will adapt to how data systems work, rather than making those systems adapt to how decisions actually get made.

Here’s what typically happens: A business leader needs to understand customer churn patterns for a product launch next week. They check the existing dashboards — nothing relevant. They submit a request to the data team. Three days later, they get a dataset with cryptic column names like “CUST_STAT_FLG” and no documentation about calculation logic. Several follow-up rounds later, they get the usable insights needed, but the launch decision has already been made based on intuition.

This pattern repeats constantly across organizations. According to recent surveys, over half of data professionals say it takes more than a week to fulfill a typical ad hoc request, and most require multiple iterations to deliver actionable insights. The bottleneck isn’t computing power or storage — it’s the gap between how people ask questions and how systems provide answers.

This is because traditional self-service gives users access to pre-curated dashboards and predefined datasets. What they actually need when exploring new questions is the ability to get complete, contextual answers without waiting for new curation. That requires rethinking the entire workflow from question to decision.

Question 2: Companies have semantic layers, data catalogs, and modern BI platforms. Why isn’t that solving the problem?

Answer: Those tools solve important pieces of the puzzle, but they’re not designed to work together seamlessly when someone asks a new question.

For instance, take this real-world example: “Which of our enterprise customers are most likely to churn in Q4, and what’s driving that risk?” To answer this properly, you need customer data from Salesforce, usage patterns from your product analytics, support ticket volumes from your service database, and contract details from your ERP system. The challenge isn’t just accessing these sources; it’s that the business definitions and metadata needed to combine them properly are fragmented across BI tools, data catalogs, semantic models, and tribal knowledge that exists only in someone’s head.

Current tools excel at predefined scenarios but break down during exploration. A semantic layer can tell you that “customer_tier” means enterprise vs. SMB, but it can’t automatically incorporate the context that enterprise churn calculations should exclude customers in their first 90 days or those currently in contract negotiations.

The missing piece is intelligent orchestration — systems that can interpret intent, reason about what data is relevant across multiple sources, apply the right business logic automatically, and package results so they’re immediately actionable. Most organizations have the raw materials for self-service but lack the intelligence layer to make it work in practice.

Question 3: How does Promethium’s approach differ from traditional self-service platforms?

Answer: Traditional self-service platforms expect business users to navigate dashboards or predefined datasets. But when the question doesn’t fit the mold — which is often — it gets kicked back to the data team, who must stitch together sources, build new pipelines, and deliver the answer manually.

Promethium flips that workflow. Instead of building from scratch, data analysts can now deliver Instant Data Answers that are governed, complete, and explainable in minutes, not weeks.

Here’s how it works:

1. A business stakeholder asks a new question.
2. The analyst poses that question to Promethium.
3. Our agentic architecture — orchestrated by Mantra, the Data Answer Agent interprets intent, identifies relevant sources, applies governance and business logic, and generates a full answer: data, SQL, definitions, and lineage.
4. The analyst reviews, validates, and shares the result — wherever the business needs it: in BI tools like Tableau or Power BI, as a published dataset in Snowflake or Databricks, or via our data marketplace or workflow systems.

The key difference? Promethium empowers analysts to operate at the speed of the business without waiting for engineering or provisioning. It augments the data team’s role rather than bypassing it, putting them in control of trusted, scalable self-service.

Question 4: Trust and governance are major concerns with AI-generated analysis. How do you ensure accuracy and compliance?

Answer: Trust in AI-generated insights requires three things: explainability, validation, and continuous improvement. We address each systematically by combining business definitions, technical metadata, lineage, and usage history into a unified understanding layer we call our 360° Context Engine.

Every data answer includes complete transparency, including the SQL queries generated, the business rules applied, the sources accessed, and the assumptions made. Data teams can review, modify, and approve logic before it’s used for similar future questions, building organizational knowledge over time.

For validation, we maintain human oversight throughout the process. Data teams don’t just review final outputs; they actively shape how the system learns. When an analyst refines a query or corrects business logic, that feedback is incorporated into the platform’s memory, making future answers more accurate and aligned with organizational standards.

The result is AI that augments human expertise rather than replacing it. Speed increases dramatically, but control and accountability remain with the data team, making every data answer traceable and explainable.

Question 5: Most organizations have complex, distributed data architectures. How can they implement this without major infrastructure changes?

Answer: Our architecture is built on open principles — we connect to data where it lives rather than requiring migration, consolidation or replication. The platform creates a virtual layer that can query across Snowflake, Databricks, cloud warehouses, and SaaS applications simultaneously, applying governance and business logic consistently regardless of where data resides.

Implementation typically follows a crawl-walk-run approach. Organizations start by connecting their primary data warehouse and defining business logic for their most common question types. As the system learns organizational patterns and builds trust, it expands to additional sources and use cases.

The key advantage is immediate value without infrastructure risk. Organizations typically see significant reductions in data team request volume within weeks of initial deployment, using existing security policies and governance frameworks. Teams then expand to additional sources and use cases as the system builds organizational trust and understanding.

We’ve also designed the platform to enhance rather than replace existing tools. Results can be consumed directly through our interface, embedded in Tableau or Power BI, or delivered through existing workflow systems. This preserves investments while dramatically improving capability.

Question 6: Looking ahead, how do you see the relationship between data teams and business users evolving?

Answer: The most successful data teams are moving from service providers to strategic enablers. Instead of spending time translating requests and building one-off analyses, they’re focusing on defining business logic, establishing governance frameworks, and ensuring organizational data literacy.

We’re seeing early adopters implement agent-to-agent workflows where business applications can directly request Data Answers from Promethium’s Mantra without human intervention. This doesn’t eliminate the need for human judgment in defining the underlying logic, but it multiplies the impact of each data professional by automating routine interactions.

Within 2-3 years, we expect routine analysis to be largely automated, with data teams focused on complex investigations, strategic planning, and ensuring AI systems align with business objectives. The most valuable skill will be translating business strategy into data strategy, rather than translating business questions into technical queries.

The end goal isn’t replacing human expertise — it’s amplifying it so that every business decision can be data-informed without overwhelming the people who understand data best. Promethium is built for that re-imagined future — it’s an agentic platform that enables true self-service data at scale. By transforming how questions become answers, we help data teams do more with less and help every decision-maker move faster with confidence.

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Modern Data Company’s Srujan Akula offers commentary on emerging data risks and how CIOs can address them. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI has fundamentally changed how organizations manage and secure data. As someone who regularly works with CIOs, I’m seeing organizations struggle with a new set of challenges that traditional approaches simply can’t handle. These five emerging data risks represent the most significant barriers to realizing value from enterprise data investments.

1. Fragmented Data Management Blocking Business Value

Your marketing team just copied your customer database into ChatGPT. Your data scientists are feeding proprietary algorithms into open-source models. This isn’t just a security issue – it’s a symptom of fragmented data management that prevents organizations from scaling their data initiatives.

When teams can’t easily access the data they need through governed channels, they create workarounds. These shortcuts not only increase risk but fragment your data ecosystem further, making it impossible to build cohesive customer experiences or derive enterprise-wide insights.

This can be addressed by implementing data products as your logical source of truth. Rather than physically moving or centralizing data, data products create unified access through shared metadata and governance layers. Each data product encapsulates the business logic, quality rules, and access controls for a specific domain—like “Customer 360” or “Product Performance”—while the underlying data can remain in its original systems. This approach enables teams to discover, understand, and consume trusted data assets without the complexity and cost of large-scale data movement, creating a federated yet governed data ecosystem that scales with your AI ambitions.

2. Data Quality Issues Undermining AI Initiatives

Poor data quality remains the number one killer of data initiatives. In traditional analytics, humans could spot and mentally correct inconsistencies. With AI, those small errors amplify into dramatically wrong conclusions that can misdirect entire business strategies.

Here’s what makes this particularly challenging: as data volumes grow, traditional quality approaches collapse. You can’t manually review millions of records, and point solutions that address specific quality issues fail to scale across the enterprise.

Data teams we work with tackle this by fundamentally rethinking data architecture. Data products–managed, curated datasets with clear ownership and built-in quality controls – create reliable foundations for analytics and AI. By embedding quality into the data itself rather than treating it as a separate concern, organizations reduce costs while dramatically improving outcomes. These well-designed data products become valuable business assets that teams can confidently build upon.

3. Data Infrastructure Costs Spiraling Out of Control

“Just run it in the cloud” has become the default answer to data infrastructure challenges, but it’s leading to runaway costs that threaten the ROI of data initiatives. Modern data workloads–particularly around AI–require specialized infrastructure that traditional IT departments struggle to optimize.

The symptoms are everywhere: massive cloud bills with minimal business value, data science teams waiting weeks for resources, and C-suite questions about whether these investments are worth continuing.

The most effective solution I’ve seen is a shift toward right-sized, modular data architecture. CIOs who implement intelligent data orchestration can dramatically reduce costs by matching workloads to the appropriate infrastructure–whether on-premises or cloud—while maintaining a unified governance layer. Organizations using this approach typically see 30-50% cost reductions while actually increasing data utilization. The key is building infrastructure that scales with actual usage rather than provisioning for peak capacity.

4. Data Governance That Blocks Rather Than Enables

Traditionally, organizations approached data governance as a compliance checkbox exercise, resulting in policies that create friction rather than value. As regulatory requirements like GDPR, CCPA, and industry-specific mandates multiply, governance teams often default to restrictive data policies that stifle innovation.

I regularly see organizations where accessing data requires weeks of approval processes, resulting in frustrated business users and missed opportunities. Even worse, these friction-heavy processes actually increase risk as users find workarounds that bypass governance entirely.

Forward-thinking CIOs are implementing what I call “governance by design” – embedding compliance requirements directly into data products and platforms rather than layering them on afterward. With this approach, governance becomes an enabler of innovation rather than a blocker. Automated data discovery, lineage tracking, and policy enforcement reduce compliance costs while accelerating appropriate data use. The companies excelling here make governance invisible to users while maintaining comprehensive audit trails.

5. Siloed Data Teams Creating Redundancy and Waste

As data initiatives multiply across organizations, we’re seeing a troubling pattern: duplicate data teams building similar solutions in different departments with no shared foundation. Marketing creates one customer view, sales builds another, and product teams maintain a third–all using different tools and yielding contradictory insights.

This fragmentation creates obvious inefficiencies, but the bigger cost comes from missed opportunities. Without a unified view of customers, products, and operations, organizations make decisions based on partial information that rarely captures the full business context.

The solution requires an outcome-first approach that optimizes operational expenses by design. Rather than building comprehensive datasets “just in case,” focus on understanding what’s actively consumed by business applications or AI models. This LeanAI principle—minimal viable data with maximum business impact—reduces infrastructure costs while accelerating insights. When teams share governance standards and interfaces, they can collaborate without duplicating efforts, creating immediate OpEx savings and faster time-to-value across AI initiatives.

The Path to Modern Data Management

Organizations pulling ahead aren’t just investing in fancy AI tools–they’re evolving their data foundations to address these fundamental challenges. Every CIO I work with who has successfully navigated these waters shares a common approach: treating data as a product rather than a byproduct of business operations.

This product-oriented mindset changes everything. Data becomes a managed asset with clear ownership, defined quality standards, and measurable business value. Platforms replace point solutions, reducing both cost and complexity while improving outcomes. Governance shifts from restricting access to enabling appropriate use.

The competitive advantage is clear. Organizations with business-focused data product approaches respond to market changes faster, derive more value from AI investments, and scale data initiatives more efficiently than their peers. But getting there requires rethinking fundamental assumptions about how we manage, govern, and leverage enterprise data.

For CIOs looking to lead this transformation, the most critical step is building the organizational capabilities and technical foundations that turn data from a cost center into a strategic asset. Those who succeed will not only avoid these five risks but position their organizations to thrive in an increasingly data-driven economy.

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