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 […]
Ask a Data Ethicist: How Do Technical Data Choices in ML Lead to Ethical Issues?
A lot of times, ethical issues in AI systems arise from the most mundane types of decisions made about data such as how it is processed and prepared for machine learning (ML) projects. I’ve been reading Designing Machine Learning Systems by Chip Huyen, which is filled with practical advice about design choices in machine learning […]
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 […]
Why and How to Unlock Proprietary Data to Drive AI Success
These days, virtually every company is using AI – and in most cases, they’re using it through off-the-shelf AI technologies, like Copilot, that offer the same capabilities to every customer. This begs the question: How can a business actually stand out in the age of AI? Rather than just adopting AI as a way of keeping […]
Unlocking Unstructured Data: Fueling AI with Insights
IDC reports that around 90% of the data in the digital world is unstructured. This encompasses data like PDFs, PowerPoints, emails and images, all containing valuable information that traditional structured databases can’t gather. As artificial intelligence (AI) becomes more widespread, the importance of unstructured data grows. Businesses now face the challenge of organizing and utilizing these diverse data sources so AI models can fully leverage their potential, which is much easier said than done. […]
Protecting Machine Learning Systems in the GenAI Era
As GenAI and machine learning (ML) become more widespread across industries, their high levels of adoption have created a major challenge: security. While every organization and IT team has its own security protocols and frameworks, many are starting to realize that traditional approaches aren’t enough when it comes to protecting themselves from the potential threats […]
Data Governance: Building a Strong Foundation
The first time I was introduced to data governance, someone handed me a thick binder labeled “Data Policies.” It sat on my desk untouched for months. No one referred to it. No one followed it. It was a perfect symbol of what goes wrong when governance is reduced to paperwork without purpose. True data governance […]
Ask a Data Ethicist: Is Consent the Wrong Approach for Modern Data Regulation?
Asking for consent to collect and use someone’s data as the basis for legitimate processing of that data is key to our data privacy regulations and is also ethical. However, many people agree that consent is broken and that we don’t really have meaningful consent given the power imbalances between big tech organizations and the […]
How Data Accessibility Shapes Business Intelligence
Business intelligence (BI) ensures organizations and enterprises make measured decisions. And teams are aware of its importance. However, many analytics teams in businesses struggle with slow, fragmented, or downright counterproductive BI systems. What’s interesting is that these challenges do not emerge due to a lack of sophisticated technologies or adequately trained employees. The main culprit […]
Harnessing Data Lineage to Enhance Data Governance Frameworks
At a time when even the smallest organizations have data from multiple sources undergoing various transformations, ensuring data accuracy, security, and compliance has never been more critical. This reality has led to a proliferation of robust data governance frameworks, i.e., the systems, policies, and procedures that manage data as a strategic asset. One vital ingredient […]