Secrets of Data Analytics Leaders

Adopting community of practice principles, along with coaching and mentoring, is a practical approach to fostering and cultivating data literacy. Published at: https://www.eckerson.com/articles/a-people-first-approach-to-developing-data-literacy

This blog examines the upcoming trend of domain-specific LLMs and evaluates three different methods of implementation. Published at: https://www.eckerson.com/articles/the-next-wave-of-generative-ai-domain-specific-llms

Many machine learning (ML) use cases center on real-time calculations. This article defines streaming ML and its architectural components. Published at: https://www.eckerson.com/articles/machine-learning-and-streaming-data-pipelines-part-i-definitions-and-architecture

Explore the intricate staffing and organization of data analytics teams, the role of Chief Data Officer, diverse responsibilities within teams, Valuations Analysts, Data Innovation, Security teams impact on data strategy, and investment in data security and analytics roles for success in data-driven environments.

Data management practices have changed substantially since the early 1990s and the dawn of data warehousing. Published at: https://www.eckerson.com/articles/the-continuing-evolution-of-data-management

Conventional data governance conflicts with today’s world of self-service analytics and agile projects. Published at: https://www.eckerson.com/articles/modern-data-governance-problems

Let's reflect on the events of the past year and prognosticate on what may transpire in the months ahead. Published at: https://www.eckerson.com/articles/trends-for-2024-our-team-gazes-into-the-crystal-ball

This podcast explores the introduction of Generative AI and its impact on early adopters, covering the benefits and challenges faced. It discusses the process of fine-tuning a pre-trained Language Model on domain-specific language and facts. It also explores the application of Gen AI in industries such as travel booking, software development, and healthcare. Additionally, it discusses the responsibilities of data teams in managing data pipelines for Generative AI and the importance of preparing teams for successful Gen AI initiatives.

Data modeling is a core skill of data engineering, but it is missing or inadequate in many data engineering teams. These teams focus on moving data with little attention to shaping the data. They engineer processes, not products. Full data engineering is both process and product engineering, and that calls for data modeling. Published at: https://www.eckerson.com/articles/a-fresh-look-at-data-modeling-part-2-rediscovering-the-lost-art-of-data-modeling

The hardest part about implementing data products is fostering a product mindset among the people responsible for defining, governing, building, and shipping data products. It’s also important that an organization establish processes to facilitate the work of the product team and review boards. Published at: https://www.eckerson.com/articles/data-products-part-ii-data-products-require-product-thinking