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Aug 6 2025

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 […]

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

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AI Data Blogs | Information From Enterprise Leaders Data Education Data Governance & Data Quality | News & Articles Data Governance Blogs data quality DATAVERSITY Machine Learning ml Smart Data Blogs Smart Data News, Articles, & Education

Jun 20 2025

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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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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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, […]

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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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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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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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 […]

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 […]

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 […]

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 […]

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, […]

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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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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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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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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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 […]

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, […]

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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Chatbot Quality Control: Why Data Hygiene Is a Necessity

The rush is on to deploy chatbots. Chatbots rely on data to power their outputs; however, companies that prioritize data quantity over quality risk creating systems that produce unreliable, inappropriate, and simply incorrect responses. Success in this field depends on rigorous data standards and ongoing quality control rather than simply accumulating more training data. When […]

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Data Speaks for Itself: Data Validation – Data Accuracy Imposter or Assistant?

In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM). Because data quality is measured in the degree […]

The Art of Lean Governance: A Systems Thinking Approach to Data Governance

A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data quality approaches not utilizing these loops will fail to achieve the desired results, often worsening the problem. Data Governance is about gaining trust and […]

Data Errors in Financial Services: Addressing the Real Cost of Poor Data Quality

Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Even industry leaders like Charles Schwab and Citibank have been severely impacted by poor data management, revealing the urgent need for more effective data quality processes across the sector. Key Examples of Data Quality Failures — […]

New Tools, New Tech, Same Roadblocks: Data Governance in the Age of AI

Organizations are racing to adopt AI for its promise of efficiency and insights, yet the path to successful AI integration remains fraught with obstacles. Despite advancements in tools like ChatGPT and Google’s Gemini, fundamental issues with data governance – such as high costs, poor data quality, and security concerns – continue to hinder progress. Stop me […]

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