Most enterprises suffer from spotty deployment and management of artificial intelligence (AI) initiatives. As different parts of the organization experiment with AI in silos, they waste both resources and the opportunity to learn from the experience of others. When my company commissioned an independent third-party survey of more than 2,500 AI practitioners across industries and geographies, […]
Three Causes of Cloud Migration Failure in Large Enterprises
In the ever-evolving landscape of the modern business world, generative artificial intelligence (GenAI) is taking the industry by storm. Companies increasingly recognize the transformative potential of GenAI and machine learning. To harness the full power of these technologies, it is critical for organizations to embrace cloud computing. Cloud migration is not a mere option but […]
Machine Learning: Challenges and Opportunities for Modern Data Executives
The transformational promise of artificial intelligence (AI) and machine learning (ML) for enterprises has fueled enormous excitement and massive investment by data executives. One estimate predicts that AI’s contribution to the global economy could reach an extraordinary $15.7 trillion by 2030. That’s more than the current combined economic output of China and India. Yet, there seems […]
Unlocking the Full Potential of Data Collaboration Through PETs
What’s the future of data collaboration? It’s a question that should be on the lips of every C-suite executive in global organizations right now. The unrealized potential of consumer data is immense for businesses wanting to forge deeper connections with their customers and unlock new opportunities, but many are unsure how to proceed when faced […]
No Database Is Perfect: Applying CAP Theorem to Database Choice
Since its introduction to the marketplace in 2000, the consistency, availability, and partition theorem, or CAP theorem, has been a guiding principle in database management. Computer scientist Eric Brewer presented the CAP theorem in a talk about distributed systems that provide web services. Two MIT professors later proved the theorem. It states that a database can be […]
Choosing Tools for Data Pipeline Test Automation (Part 1)
Those who want to design universal data pipelines and ETL testing tools face a tough challenge because of the vastness and variety of technologies: Each data pipeline platform embodies a unique philosophy, architectural design, and set of operations. Some platforms are centered around batch processing, while others are centered around real-time streaming. While the nuances […]
Maximizing IT Investments and Enhancing End-User Experience with Data
In an age defined by data-driven decision-making, where 91.9% of organizations have already leveraged analytics to enhance their operations, a question remains: What if there were even more sources of untapped data, capable of helping businesses increase the quality of the end-user experience and elevating the functionality of systems, applications, and cloud investments within their business? This is […]
Building a Strong Community for Women in Data Management and Governance
In September, I had the privilege of co-hosting a new special interest group (SIG), Women in Data Management and Governance, alongside DATAVERSITY’s Shannon Kempe, at a pre-conference Enterprise Data World (EDW) event. I’m so honored to be part of building this community and to better serve a critical and growing constituency in our industry. Supporting the growth of […]
Managing Missing Data in Analytics
Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data […]
Data Governors, First Govern Yourselves
Data Governance, as currently practiced, is failing. There have been some successes, but by and large, even these efforts have fallen short. Worse, many of those tasked with contributing to Data Governance find the effort painful. We have enormous sympathy for data governors. (We use the term “data governors” – DGs – as the most […]