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

It's not easy being the head of data & analytics at a large organization. You must align a large team across multiple disciplines; you must deal with oodles of legacy systems and tools that hamper innovation; and you must deliver business value fast to keep executives at bay and your job intact. You also need to recruit dynamic managers who can push the envelope while meeting operational objectives. And when you falter--which you inevitably will-you have to rebound fast. No one knows these lessons better than Tiffany Perkins-Munn. She currently runs a 275-person data & analytics team at JP Morgan Chase that consists of data engineers, data scientists, behavioral economists, and business intelligence experts. She thrives on versatility, having earned a Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Building on this foundation, she has accumulated vast experience in the art of managing data & analytics teams during her 23 years in technical and managerial roles in the financial services industry. In this interview, you’ll learn: 1. Tiffany’s secret for aligning a large data & analytics team and keep them from splitting into silos of specialization 2. Her favorite techniques for recruiting the right people to her team. 3. How to wade through the thicket of legacy systems and deliver innovative solutions quickly. 4. The impact of GenAI on her operations and the financial services industry. 5. How to advance your careers in data & analytics.

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

Companies need to invest heavily in teams and people, both at corporate and in the field, if they want to become a data-driven organization. Published at: https://www.eckerson.com/articles/organizing-for-success-part-iii-how-to-organize-and-staff-data-analytics-teams

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

Data leaders must prepare their teams to deliver the timely, accurate, and trustworthy data that GenAI initiatives need to ensure they deliver results. They can do so by modernizing their environments, extending data governance programs, and fostering collaboration with data science teams. Published at: https://www.eckerson.com/articles/the-data-leader-s-guide-to-generative-ai-part-i-models-applications-and-pipelines

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