Posts Tagged ‘Data governance’
It’s Probably Your Data
When an artificial intelligence (AI) initiative underperforms, the model is often the first thing blamed. Maybe the algorithm isn’t sophisticated enough. Maybe the platform is wrong. Maybe the organization needs a newer, more powerful model. This interesting and important topic came to us from CIO in their article, “Your AI model isn’t the problem. Your…
Read MoreWho Is Governing Whom? Data Governance in the Age of AI
For years, data governance has been about control: organizations establish policies for how data is collected, stored, accessed, shared, protected and eventually retired. People create the rules, technology follows them, and governance provides the guardrails. Artificial intelligence (AI) complicates that relationship. As AI systems become embedded in search, analytics, content creation, decision support and everyday…
Read MoreWhy AI Makes a Data Strategy More Important Than Ever
Artificial intelligence (AI) may be changing what organizations can do with data, but it hasn’t changed one fundamental truth: AI is only as effective as the data behind it. That makes a strong data strategy more important than ever. This interesting topic came to us from Dataversity in their article, “Your Data Strategy Isn’t Ready…
Read MoreFrom Information to Action: Turning Data into Better Decisions
Organizations today have access to more data than ever before. Customer behavior, operational performance, financial trends, market activity and employee insights can all be measured and analyzed. But having data and making good decisions with it are two very different things. This interesting topic came to us from IDB in their article, “How Can Governments…
Read MoreAI Agents Need Data Governance and Data Governance Needs to Keep Up
Artificial intelligence (AI) agents are changing how organizations interact with data. Unlike traditional AI tools that primarily analyze information or generate responses, agents can take action: retrieving data, making decisions, initiating workflows and interacting with other systems with limited human intervention. This topic came to us from CIO in their article, “Why AI agents will…
Read MoreAI Isn’t the Data Problem. It’s the Spotlight.
Artificial intelligence (AI) may be dominating technology conversations, but underneath the excitement, urgency and evolving tools lies a very familiar challenge: data. AI did not create the data problem. It simply made it much harder to ignore. For years, organizations have struggled with fragmented information, inconsistent terminology, disconnected systems, weak metadata and poor governance. Those…
Read MoreWhen the Data Has to Hold Up in Court
Artificial intelligence (AI) is rapidly changing the legal profession. From contract review and e-discovery to legal research, case analysis, document drafting — the list goes on and on – AI can process enormous amounts of information far faster than a human team. This pertinent topic came to us from Wolters Kluwer in their article, “Would…
Read MoreWhen AI Learns the Wrong Lessons
Data bias is one of the most significant challenges facing organizations that rely on artificial intelligence (AI) and machine learning. AI systems learn from the data they are given, which means that when the data contains historical biases, gaps or imbalances, those problems can become embedded in the technology’s results. This interesting and important article…
Read MoreWhy Data Governance Matters More Than Ever
Artificial intelligence (AI) is transforming healthcare. AI systems are helping clinicians identify diseases earlier, predict patient risks, optimize workflows and personalize treatment plans. Yet despite its potential, AI adoption in healthcare faces a significant obstacle: data governance. Healthcare IT News brought this topic to us in their article, “Before AI can deliver, healthcare orgs must…
Read MoreData Qualityis the Foundation of Successful AI Adoption
Artificial intelligence (AI) has enormous potential to transform organizations, but its success depends on one very critical factor: data quality. AI systems learn patterns, identify relationships and generate insights based entirely on the information they receive. If that information is inaccurate, incomplete or outdated, the results will be flawed. This important and timely topic came…
Read More