Secrets of Data Analytics Leaders

Exploring the evolution of data analyst tools from manual spreadsheets to modern self-service technologies. Importance of integrating data catalog, preparation, and analysis functions for improved productivity. Role of data virtualization tools in enabling versatile data access and integration in analyst workbenches.

In this episode, Wayne Eckerson and Shakeeb Ahkter dive into DataOps. They discuss what DataOps is, the goals and principles of DataOps, and reasons to adopt a DataOps strategy. Shakeeb also reveals the benefits gained from DataOps and what tools he uses. He is the Director of Enterprise Data Warehouse at Northwestern Medicine and is responsible for direction and oversight of data management, data engineering, and analytics.

A classic management practice dictates that a newly-appointed leader must accomplish certain things in their first 90 days. While some of this is general knowledge, there are specifics when it comes to Data Management and Analytics If you have been recently named to head any group that has to manage or facilitate the use of data, at any level in the organization, then this audio blog post is for you. Originally published at https://www.eckerson.com/articles/what-do-you-do-first-after-being-hired-as-a-bi-analytics-data-engineering-director

In this episode, Wayne Eckerson and Rich Fox discuss what differentiates data science from analytics, why and how data science addresses business needs, why balance scorecards are relevant, and why Excel is a problem. Throughout the podcast, Fox shares many real-life examples and personal experiences. Fox is vice president of Data Science and Analytics at Apex Parks Group, one of the largest entertainment center companies in the United States, which operates amusement parks, water parks, and family entertainment centers.

With all the hype and attention around big data and huge data platforms, there can sometimes be some data envy. There are still organizations and companies that don’t have big data: are they not poised for analytics too? Can they not get insights as well? The BI Pharaoh gives tips on how to work with your little data just like the big boys. Originally published at https://www.eckerson.com/articles/little-data-needs-love-too

We’re at the dawn of a new era in decision making made possible by the intersection of business intelligence and artificial intelligence. Rather than replace BI, AI will make BI more pervasive. AI-infused BI tools will be easier to use, generate more useful insights, and make business users more productive. Rather than replace human decision makers, AI will free them to focus on value-added activities and make decisions with data rather than rely solely on gut instinct. Originally published at https://www.eckerson.com/articles/the-impact-of-ai-on-analytics-machine-generated-intelligence

In this episode, Wayne Eckerson and Rich Galan discuss the obstacles to delivering timely analysis, the problems that large volumes of data create, solutions to those issues, and where BI is headed in the near future. Rich is a veteran data analytics leader with 20 years of experience in a variety of data-driven organizations.

Discover how power user networks are the key to successful self-service analytics, providing data analysts with the training and support they need. Learn about the role of peer-based learning and collaboration in enhancing skills and satisfaction. Explore the benefits of tool standardization and data catalogs in improving data access and knowledge sharing among analysts.

In this episode, Wayne Eckerson and Steve Dine discuss modernizing data architectures. They cover the definition of a modern data architecture, telltale signs you need to modernize, best practices to modernize, and much more. Dine is the President of Datasource Consulting, an EXL company. He has extensive experience designing, delivering, and managing successful, highly scalable and maintainable modern data architecture solutions.

Data pipelines become chaotic with pressures of agile, democratization, self-service, and organizational “pockets” of analytics. From enterprise BI to self-service analysis, data pipeline management should ensure analysis results are traceable, reproducible, and of production strength. Robust data pipelines rely on eight critical components. Originally published at https://www.eckerson.com/articles/the-complexities-of-modern-data-pipelines