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Stop Feeding AI Junk: A Systematic Approach to Unstructured Data Ingestion
It’s go-time for enterprise AI. A PagerDuty global survey of 1,000 IT and business executives found that 62% of companies using agentic AI expect a return of 171% on average. But getting to ROI is no easy task. Recent surveys show mixed results on efforts thus far, with “getting strategies right” and “making data ‘AI-ready’” […]


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Author: Kumar Goswami

The Good AI: Data Contracts for AI Transparency
“AI is only as trustworthy as the data that fuels it.”  This statement has never been more relevant. AI systems now power decisions, affecting credit approvals, medical diagnoses, fraud detection, and countless other critical areas. Yet without transparency into data sources, quality, and lineage, AI can quickly become a black box — opaque, unpredictable, and […]


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Author: Subasini Periyakaruppan

Strengthening Compliance with Better Data
Compliance today isn’t just about keeping pace with rules and regulations; it’s about keeping pace with culture. Globalization, geopolitical uncertainty, and rapid shifts in technology mean the risks companies face are more complex than ever. Yet too many organizations are still relying on legacy systems, outdated processes, and once-a-year, check-a-box training to protect their people […]


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Author: Ty Francis

Building a Data-First Culture
Technology is not what powers a data-first culture, but people, operating models, and disciplined delivery. Most organizations already possess more tools and data than they can effectively utilize. What differentiates the leaders is that they tie analytics to real business results, productize effective data, govern for speed and security, and, most importantly, rewire decisions. While […]


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Author: Chirag Agrawal

The Data Danger of Agentic AI


Agentic AI represents a significant evolution beyond traditional rule-based AI systems and generative AI, offering unprecedented autonomy and transformative potential across various sectors. These sophisticated systems can plan, decide, and act independently, promising remarkable advances in efficiency and decision-making.  However, this high degree of autonomy, when combined with poorly governed or flawed data, can lead […]

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Author: Samuel Bocetta

Data Speaks for Itself: Is Your Data Quality Management Practice Ready for AI?
While everyone is asking if their data is ready for AI, I want to ask a somewhat different question: Is your data quality management (DQM) program ready for AI?  In my opinion, you need to be able to answer yes to the following four questions before you can have any assurance you are ready to […]


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Author: Dr. John Talburt

External Data Strategy: From Vision to Vendor Selection (Part 1)


In today’s data-driven business environment, the ability to leverage external information sources has become a critical differentiator between market leaders and laggards. Organizations that successfully harness external data don’t just gather more information – they transform how they understand their customers, anticipate market shifts, and identify growth opportunities. However, the path from recognizing the need for […]

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Author: Subasini Periyakaruppan

Improving Data Quality Using AI and ML


In our fast-paced, interconnected digital world, data is truly the heartbeat of how organizations make decisions. However, the rapid explosion of data in terms of volume, speed, and diversity has brought about significant challenges in keeping that data reliable and high-quality. Relying on traditional manual methods for data governance just doesn’t cut it anymore; in […]

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Author: Udaya Veeramreddygari

Mind the Gap: AI-Driven Data and Analytics Disruption


We are at the threshold of the most significant changes in information management, data governance, and analytics since the inventions of the relational database and SQL. Most advances over the past 30 years have been the result of Moore’s Law: faster processing, denser storage, and greater bandwidth. At the core, though, little has changed. The basic […]

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Author: Mark Cooper

Is the Scope of Data Governance Enough?
Data governance has long been the backbone of responsible data management, ensuring that organizations maintain high standards in data quality, security, and compliance. According to Jonathan Reichental in “Data Governance for Dummies,” the scope of governance extends well beyond data ownership and stewardship. It encompasses metadata, data architecture, master and reference data management, storage, integration, […]


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Author: Myles Suer

DGIQ + AIGov Conference: Takeaways and Trending Topics in Data Quality


In this series of blog posts, I aim to share some key takeaways from the DGIQ + AIGov Conference 2024 held by DATAVERSITY. These takeaways include my overall professional impressions and a high-level review of the most prominent topics discussed in the conference’s core subject areas: data governance, data quality, and AI governance.  In the first blog post of […]

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Author: Irina Steenbeck

Unlocking AI Success: Creating a Winning Data Strategy


AI has the power to transform industries by analyzing massive datasets and automating complex processes. However, AI’s effectiveness is directly tied to the integrity of the data fueling it. Data governance is required to drive accountability around privacy and ethics, while poor quality data results in inaccurate AI outcomes, leading to customer dissatisfaction, delayed decisions, […]

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Author: Cameron Ogden

Data Quality Metrics Best Practices


The amount of data we deal with has increased rapidly (close to 50TB, even for a small company), whereas 75% of leaders don’t trust their data for business decision-making. Though these are two different stats, the common denominator playing a role could be data quality. With new data flowing from almost every direction, there must be a yardstick or […]

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Author: Suresh P

The Art of Lean Governance: Moving Beyond Governance Buzzwords and Bling
This column will expand on a Systems Thinking approach to Data Governance and focus on process control. The vendors of myriad governance tools focus on metadata, dictionaries, and quality metrics. Their marketing is a sea of buzzwords and bling — bells and whistles. Yet, where is the evidence of adding actual business value, defined as […]


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Author: Steve Zagoudis

Data Speaks for Itself: Data Quality Management in the Age of Language Models
Unsurprisingly, my last two columns discussed artificial intelligence (AI), specifically the impact of language models (LMs) on data curation. My August 2024 column, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality,” and my November 2024 column, “Data Validation, the Data Accuracy Imposter or Assistant?” addressed some of the […]


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Author: Dr. John Talburt

Empowering Organizations Through Data Literacy, Governance, and Business Literacy
In my journey as a data management professional, I’ve come to believe that the road to becoming a truly data-centric organization is paved with more than just tools and policies — it’s about creating a culture where data literacy and business literacy thrive.  Data governance, long regarded as a compliance-driven function, is now the backbone […]


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Author: Gopi Maren

Identifying and Addressing Data Overload
Increased data generation requires modern businesses to manage vast volumes of information. All this data holds immense potential for insights and informed decision-making, but its value depends on effective utilization. Without the right tools, frameworks, and strategies, even established companies risk being overwhelmed by data overload.  Let’s take a closer look at data overload and […]


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Author: Irfan Gowani

The Challenges of Data Migration: Ensuring Smooth Transitions Between Systems
Data migration — the process of transferring data from one system to another — is a critical undertaking for organizations striving to upgrade infrastructure, consolidate systems, or adopt new technologies.   However, data migration challenges can be very complex, especially when doing large-scale data migration projects.   Duplicate or missing data, system compatibility issues, data security problems, […]


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Author: Ainsley Lawrence

From Input to Insight: How Quality Data Drives AI and Automation


More and more enterprises are looking to automation and AI to deliver new efficiencies and give their organizations an edge in the market. Data is the engine that powers both automation and AI. But data must be clean and user-friendly for these systems to work effectively and deliver on their promise.  Lots of organizations are […]

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Author: Amol Dalvi

Beyond Ownership: Scaling AI with Optimized First-Party Data


Brands, publishers, MarTech vendors, and beyond recently gathered in NYC for Advertising Week and swapped ideas on the future of marketing and advertising. The overarching message from many brands was one we’ve heard before: First-party data is like gold, especially for personalization. But it takes more than “owning” the data to make it valuable. Scale and accuracy […]

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Author: Tara DeZao

Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database


You never know what’s going to happen when you click on a LinkedIn job posting button. I’m always on the lookout for interesting and impactful projects, and one in particular caught my attention: “Far North Enterprises, a global fabrication and distribution establishment, is looking to modernize a very old data environment.” I clicked the button […]

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Author: Mark Cooper

5 Data Management Tool and Technology Trends to Watch in 2025


The market surrounding data management tools and technologies is quite mature. After all, the typical business has been making extensive use of data to help streamline its operations and decision-making for years, and many companies have long had data management tools in place. But that doesn’t mean that little is happening in the world of […]

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Author: Matheus Dellagnelo

Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?
I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, I’ve seen a trend — more and more emphasis on AI.   I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]


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Author: Randall Gordon

Data Insights Ensure Quality Data and Confident Decisions
Every business (large or small) creates and depends upon data. One hundred years ago, businesses looked to leaders and experts to strategize and to create operational goals. Decisions were based on opinion, guesswork, and a complicated mixture of notes and records reflecting historical results that may or may not be relevant to the future.  Today, […]


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Author: Kartik Patel

Technical and Strategic Best Practices for Building Robust Data Platforms


In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks. A successful data strategy outlines best practices and establishes a clear vision for data architecture, […]

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Author: Alok Abhishek