Secrets of Data Analytics Leaders

In this episode, Wayne Eckerson and Jen Underwood explore a new era of analytics. Data volumes and complexity have exceeded the limits of current manual drag-and-drop analytics solutions. Data moves at the speed of light while speed-to-insight lags farther and farther behind. It is time to explore intelligent, next generation, machine-powered analytics to retain your competitive edge. It is time to combine the best of the human mind and machine.

Underwood is an analytics expert and founder of Impact Analytic. She is a former product manager at Microsoft who spearheaded the design and development of the reinvigorated version of Power BI, which has since become a market leading BI tool. Underwood is an IBM Analytics Insider, SAS contributor, former Tableau Zen Master, Top 10 Women Influencer and active analytics community member. She is keenly interested in the intersection of data visualization and data science and writes and speaks persuasively about these topics.

In this podcast, Carl Gerber and Wayne Eckerson discuss Gerber’s top five data governance best practices: Motivation, Assessment, Data Assets Catalog, CxO Alliance, and Data Quality.

Gerber is a long-time chief data officer and data leader at several large, diverse financial services and manufacturing firms, who is now an independent consultant and an Eckerson Group partner.

He helps large organizations develop data strategies, modernize analytics, and establish enterprise data governance programs that ensure data quality, operational efficiency, regulatory compliance, and business outcomes. He also mentors and coaches Chief Data Officers and fills that role on an interim basis.

With today’s unprecedented velocity of change in data and technologies, the single version of the truth (SVOT) is sometimes looked upon as a nice to have. But SVOT is not optional. Despite the difficulties in constructing a single version of the truth, it is now needed more than ever to keep CEOs happy and to stay compliant with new regulations like the GDPR.

Originally published at https://www.eckerson.com/articles/single-version-of-the-truth-not-optional

Ethics is challenging because right and good are not always clear. More data, more kinds of data, and advanced analysis of data often conflict with concerns of data privacy, security, anonymity, and ownership. Resolving these conflicts requires acknowledgment, discussion, and the hard work of defining ethics-based policies and creating a culture of ethical conduct.

Originally published at https://www.eckerson.com/articles/data-ethics-the-new-data-governance-challenge

In this episode, Wayne Eckerson and Jeff Magnusson discuss the data architecture Stitch Fix created to support its data science workloads, as well as the need to balance man and machine and art and science.

Magnusson is the vice president of data platform at Stitch Fix. He leads a team responsible for building the data platform that supports the company's team of 80+ data scientists, as well as other business users. That platform is designed to facilitate self-service among data scientists and promote velocity and innovation that differentiate Stitch Fix in the marketplace. Before Stitch Fix, Magnusson managed the data platform architecture team at Netflix where he helped design and open source many of the components of the Hadoop-based infrastructure and big data platform.

To survive and thrive in today’s information economy, organizations need to establish a compelling data strategy that advances the company’s key objectives and goals. This is not a job for the chief information officer or even chief data officer; it’s a task that the CEO must own and champion and assemble his best and brightest to execute. This audio blog examines the ten step methodology that Eckerson Group uses to help organizations create a data strategy.

Originally published at https://www.eckerson.com/articles/how-to-create-a-data-strategy-part-1-overview

To survive and thrive in today’s information economy, organizations need to establish a compelling data strategy that advances the company’s key objectives and goals. This is not a job for the chief information officer or even chief data officer; it’s a task that the CEO must own and champion and assemble his best and brightest to execute. This audio blog examines the ten step methodology that Eckerson Group uses to help organizations create a data strategy.

Originally published at https://www.eckerson.com/articles/how-to-create-a-data-strategy-part-1-overview

Data engineering is one of the hottest and most difficult jobs to fill in the field of analytics. Breadth and depth of required skills limits the number of people qualified to work as data engineers. If you’re seeking to hire data engineers, consider the 24 skill areas identified here as guidance to shape job descriptions and to screen candidates. If you’re seeking to become a data engineer, take the skills assessment to highlight your strengths and identify your gaps.

Originally published at https://www.eckerson.com/articles/data-engineering-coming-of-age

In this podcast, Wayne Eckerson and Joe Caserta discuss data migration, compare cloud offerings from Amazon, Google, and Microsoft, and define and explain artificial intelligence.

You can contact Caserta by visiting caserta.com or by sending him an email to [email protected]. Follow him on Twitter @joe_caserta.

Caserta is President of a New York City-based consulting firm he founded in 2001 and a longtime data guy. In 2004, Joe teamed up with data warehousing legend, Ralph Kimball to write to write the book The Data Warehouse ETL Toolkit. Today he’s now one of the leading authorities on big data implementations. This makes Joe one of the few individuals with in-the-trenches experience on both sides of the data divide, traditional data warehousing on relational databases and big data implementations on Hadoop and the cloud.

Every organization that manufactures data for decision making is rethinking its data architecture. Compared to five years ago, there is a treasure trove of new technologies and techniques that promise to transform the way organizations compete and serve customers. This audio blog summarizes the major characteristics of a modern data architecture and serves a high-level guide for organizations that are in the midst of developing a new data strategy for the modern age.

Originally published at https://www.eckerson.com/articles/ten-characteristics-of-a-modern-data-architecture