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What to Expect in AI Data Governance: 2025 Predictions


In 2025, preventing risks from both cyber criminals and AI use will be top mandates for most CIOs. Ransomware in particular continues to vex enterprises, and unstructured data is a vast, largely unprotected asset. AI solutions have moved from experimental to mainstream, with all the major tech companies and cloud providers making significant investments in […]

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Author: Krishna Subramanian

AI Predictions for 2025: Embracing the Future of Human and Machine Collaboration


Predictions are funny things. They often seem like a bold gamble, almost like trying to peer into the future with the confidence we inherently lack as humans. Technology’s rapid advancement surprises even the most seasoned experts, especially when it progresses exponentially, as it often does. As physicist Albert A. Bartlett famously said, “The greatest shortcoming […]

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Author: Philip Miller

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

Delivering Personalized Recommendations Without Sacrificing User Privacy


In today’s fast-paced digital landscape, we all love a little bit of personalization. Whether it’s Netflix suggesting our next binge-worthy show or Spotify curating our playlists, these tailored experiences make us feel understood and valued. But with growing concerns around user privacy, how can companies achieve this level of personalization without compromising our personal data? […]

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Author: Ganapathy Subramanian Ramachandran

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

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

Synthetic Data Generation: Addressing Data Scarcity and Bias in ML Models


There is no doubt that machine learning (ML) is transforming industries across the board, but its effectiveness depends on the data it’s trained on. The ML models traditionally rely on real-world datasets to power the recommendation algorithms, image analysis, chatbots, and other innovative applications that make it so transformative.  However, using actual data creates two significant challenges […]

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Author: Anshu Raj

A Financial Approach to Evaluating Data, Analytics, and AI Investments


Extracting tangible business benefits from data and analytics projects, including those involving AI, has proven challenging for most enterprises. In 2019, VentureBeat reported that 87% of data and analytics (D&A) projects failed to reach production. In 2022, Gartner found that only 20% of insights derived from analytics translated into business outcomes. Despite various reasons for […]

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Author: Prashanth Southekal, Varun Vemula, and Zain Raza Nayani

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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Author: Todd Fisher

Crafting a Secure Future: Integrating an AI-First Security Posture


It is now well understood that integrating AI into an organization’s digital infrastructure will unlock real-time insights for decision-makers, streamline internal workflows by automating repetitive tasks, and enhance customer service interactions with AI-powered assistants. This technology will accelerate everything from purchases to personalized service requests. These benefits have made AI an essential component of modern […]

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Author: Donnchadh Casey

AI Advancement Elevates the Need for Cloud


The widespread adoption of artificial intelligence (AI) and machine learning (ML) simultaneously drives the need for cloud computing services. Enterprises aiming to effectively train huge datasets and navigate advanced neural networks will need to rely on more flexible and dependable solutions for managing challenging computing tasks. That is why organizations should look to hybrid solutions […]

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Author: Richard Copeland

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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Author: Bryan Eckle

Why GenAI Won’t Change the Role of Data Professionals


The recent rise of GenAI has sparked numerous discussions across industries, with many predicting revolutionary changes across a broad range of professional landscapes. While the processes data professionals use and the volume of work they can sustain will change because of GenAI, it will not fundamentally change their roles. Instead, it will enhance their abilities, […]

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Author: Itamar Ben Hemo

How to Make Generative AI Work for Natural Language Queries


As businesses increasingly rely on data-driven decision-making, the volume and complexity of data within enterprises have grown exponentially. However, processing this data and deriving actionable insights remains challenging due to the reliance on human analysts. The explosion in interest surrounding text-to-SQL (T2SQL) solutions and the capabilities of large language models (LLMs) like ChatGPT have presented […]

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Author: David Mariani

The AI Chasm: Bridging the Divide Between Research and Real-World Applications


Imagine a world where AI can accurately predict earthquakes, giving us precious time to save lives. Yet, the same technology struggles to understand essential voice commands through your home assistant during a noisy family dinner. This striking dichotomy between the potential of cutting-edge AI research and its often underwhelming real-world applications underscores a significant yet […]

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Author: Srinivasa Rao Bogireddy

Data Leader’s Playbook for Data Mapping
I’ve been thinking a lot about data mapping lately. I know, weird, right? With analytics, AI, cloud, etc., why would someone do that? What’s even stranger is that I’ve been thinking about its impact on data leaders. For clarity’s sake, I’m not talking about geographic maps with data points, I’m referring to the process of […]


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Author: John Wills

From Instincts to Data-Driven Success: The AI-Powered Path to Product-Led Growth


Have you noticed the way that businesses grow is changing? We are moving away from standard sales-driven models to more innovative product-led tactics. And what is fueling this shift? You guessed it: AI and predictive analytics. These tools are not just fancy jargon; they are transforming how we understand customer needs, customize experiences, and upgrade […]

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Author: Lohith Kumar Paripati

Data Visualization in the Era of AI/ML


How will data visualization evolve in the era of AI/ML? While AI is rapidly evolving, it is ironic that business users are still using “dumb” dashboards. The challenge is to move beyond these unintelligent dashboards to a genuinely transformative visual analytics solution that harnesses the power of AI/ML. While some vendors offer a ChatGPT-like querying […]

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Author: Chaitanya Indukuri

Putting Threat Modeling into Practice: A Guide for Business Leaders


Recognizing the value of threat modeling – a process that helps identify potential risks and threats to a business’s applications, systems, and other resources – is easy enough. By providing comprehensive insight into how cyberattacks might pan out before they occur, threat modeling helps organizations prepare proactively and reduce the risk of experiencing a successful […]

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Author: Scott Wheeler and Jason Nelson

The Trouble with AI and Identity


Artificial Intelligence (AI) has earned a reputation as a silver bullet solution to a myriad of modern business challenges across industries. From improving diagnostic care to revolutionizing the customer experience, many industries and organizations have experienced the true transformational power of AI.  However, that’s not the case for the masses. Organizations that view AI as […]

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Author: Jackson Shaw

Combat Governance Dilution: The CGO Solution
The term “governance” has become so widely used that it has lost much of its impact and precision. Originally, governance referred to the frameworks and processes for formalized and effective organizational control and accountability. However, its frequent and broad application across various contexts — ranging from IT to corporate, data, information, and AI governance — […]


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Author: Robert S. Seiner

Legal Issues for Data Professionals: Current Leading U.S. AI Laws
There is no nationwide federal law in the U.S. that specifically regulates the development, deployment, and use of AI in the private sector. (This contrasts with AI use in U.S. federal agencies, as discussed below.) This absence of such a federal law contrasts with the recently enacted AU AI law.  Instead, in the U.S., there […]


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Author: William A. Tanenbaum

How Data Is Used in Fraud Detection Techniques in Fintech Business


In the rapidly changing world of financial technology (fintech), fraud is a developing area seething with vigor. Digital banking and online financial services are booming every day, bringing with them new techniques for thieves to ply their trade – not ones that can be easily dismissed. Fintech firms must now relentlessly deploy data and artificial intelligence […]

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Author: Harsh Daiya

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