Secrets of Data Analytics Leaders

Companies that adopt DataOps increase the odds of success by making GenAI data pipelines what they should be: modular, scalable, robust, flexible, and governed.

Published: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-ii-must-have-characteristics

Most data leaders want to deliver data products, but few are doing it.

Let's face it: most data teams today function as internal service bureaus that fulfill customer requests that arrive via ticketing systems, email, handwritten notes, or calls from colleagues looking for a favor. Most work double time to keep their request backlogs from ballooning from weeks to months. In this environment, few data leaders have time or capacity to switch from a project management approach to a product management one.

Even if data leaders had time, most wouldn't know how to make this transition. Most have no experience in product management, nor do they have a good idea of a data product. So asking data leaders to deliver data products is like asking them to build a rocket ship that can travel to the moon.

In this episode, Wayne Eckerson interviews Henrik Strandberg, a strong proponent of running data teams using product management principles. Henrik Strandberg is a seasoned data transformation leader who, for the past 25 years, has helped numerous organizations bridge gaps between business and technology. In stints at publishing and gaming companies, Henrik has developed a unique understanding of building and delivering data products at scale that delight customers.

GenAI can help data engineers become more productive, and data engineering can help GenAI drive new levels of innovation.

Published at:

https://www.eckerson.com/articles/achieving-fusion-how-genai-and-data-engineering-help-one-another

Discover how master data management (MDM) provides language models with high-quality enterprise data to improve their response accuracy.

Published at:

https://www.eckerson.com/articles/improving-genai-accuracy-with-master-data-management

Explore our four primary criteria for evaluating conversational BI products.

Published at:

https://www.eckerson.com/articles/genai-driven-analytics-product-evaluation-criteria-for-conversational-bi

The success of Generative AI depends on fundamental disciplines like DataOps.

Published at:

https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-i-what-and-why

With the increasing adoption of Generative AI, learn how data governance will add value to and benefit from Generative AI.

Published at:

https://www.eckerson.com/articles/data-governance-in-the-era-of-generative-ai

"Meet the business where it is." If you're on the data team, that's what you're expected to do to empower stakeholders with data. But how far should you go to meet the business? And shouldn’t the business be expected to move a little toward meeting the data where it is?

Published at:

https://www.eckerson.com/articles/meeting-the-data-where-it-is-time-for-the-business-to-step-up

The European Union recently passed the first of its kind legal framework on the development, use, and governance of artificial intelligence. It lays out rules and standards with the aim of ensuring technologies are safe and transparent, and do not violate the fundamental rights of an individual.

Published at:

https://www.eckerson.com/articles/the-eu-ai-act-and-the-emergence-of-new-global-standards

Most organizations are committed to responsible and ethical use of AI. Yet anticipating unintended consequences before designing and implementing AI can be challenging. This framework and process helps evaluate short-term and long-term impacts across multiple dimensions so you can mitigate AI’s unintended consequences.

Published at:

https://www.eckerson.com/articles/mitigating-ai-s-unintended-consequences