Many machine learning projects fail because data scientists don’t have the right data. Techniques such as synthetic data is a novel algorithmic approach to address algorithmic risks.
Published at:
https://www.eckerson.com/articles/synthetic-data-for-ai-definition-risks-and-strategies

Synthetic Data for AI: Definition, Risks, and Strategies - Audio Blog

Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.

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This blog defines conversational BI, why companies should consider it, and how their power and casual users can best get the desired results.
Published at:
https://www.eckerson.com/articles/driving-results-with-conversational-bi-best-practices-for-power-and-casual-users

Driving Results with Conversational BI: Best Practices for Power and Casual Users - Audio Blog

Data engineering is now considered a crucial job in IT as Generative AI, the hottest technology of this decade, relies on data engineers to provide accurate inputs.
Published at:
https://www.eckerson.com/articles/data-engineering-for-genai-how-to-optimize-data-pipelines-and-governance

Data Engineering for GenAI: How to Optimize Data, Pipelines, and Governance - Audio Blog

Data engineers and data scientists must manage pipelines for unstructured data to ensure healthy inputs for language models.
Published at:
https://www.eckerson.com/articles/why-and-how-data-engineers-will-enable-the-next-phase-of-generative-ai

Why and How Data Engineers Will Enable the Next Phase of Generative AI - Audio Blog

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

DataOps for Generative AI Data Pipelines, Part II: Must-Have Characteristics - Audio Blog

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.

Synthetic Data for AI: Definition, Risks, and Strategies - Audio Blog

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