Data collection sits at the foundation of clinical trials, historically gathered from meticulous in-person visits to clinical trial sites. To improve efficiency, clinical trial research has significantly transitioned in recent years. The expansion of technological data collection methods, from wearable sensors to smartphone applications, has facilitated many aspects of clinical trial research, allowing patients to share […]
AI Technologies and the Data Governance Framework: Navigating Legal Implications
Artificial intelligence (AI) is revolutionizing how organizations use data, and these big changes are providing capabilities for improved decision-making and predictive insights. However, as AI becomes more integrated into business and daily life, it also introduces legal complexities that require careful oversight. Issues like intellectual property rights, bias, privacy, and liability are central concerns that […]
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 […]
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 […]
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 […]
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, […]
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 […]
The Impact of Advanced Data Lineage on Governance
In today’s data-driven business landscape, data quality – the availability of usable and business-ready information – remains a significant and worsening challenge for many organizations. To mitigate these effects, businesses need swift resolution of data issues with transparent and trustworthy information. However, in our fast-paced digital environment, complex data architectures with more system variables make it difficult to understand the problems. […]
AI Data Governance Spotlights Privacy and Quality
The emergence of artificial intelligence (AI) brings data governance into sharp focus because grounding large language models (LLMs) with secure, trusted data is the only way to ensure accurate responses. So, what exactly is AI data governance? Let’s define “AI data governance” as the process of managing the data product lifecycle within AI systems. To keep it […]
It’s Essential – Verifying the Results of Data Transformations (Part 1)
Today’s data pipelines use transformations to convert raw data into meaningful insights. Yet, ensuring the accuracy and reliability of these transformations is no small feat – tools and methods to test the variety of data and transformation can be daunting. Transformations generally involve changing raw data that has been cleansed and validated for use by […]