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 joe@caserta.com. 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

In this podcast, Wayne Eckerson and James Serra discuss myths of modern data management. Some of the myths discussed include 'all you need is a data lake', 'the data warehouse is dead', 'we don’t need OLAP cubes anymore', 'cloud is too expensive and latency is too slow', 'you should always use a NoSQL product over a RDBMS.' Serra is big data and data warehousing solutions architect at Microsoft with over thirty years of IT experience. He is a popular blogger and speaker and has presented at dozens of Microsoft PASS and other events. Prior to Microsoft, Serra was an independent data warehousing and business intelligence architect and developer.

In this podcast, Henry Eckerson interviews Dave Wells on the current health and future of the data warehouse. Wells acknowledges that data warehouses are struggling, but argues they are still necessary and cannot be replaced by data lakes. He then explains what the role of the modern data warehouse should be, practical steps forward for evolving the data warehouse, and much more. Wells is an advisory consultant, educator, and industry analyst dedicated to building meaningful connections throughout the path from data to business value. He works at the intersection of information management and business management, driving business impact through analytics, business intelligence, and active data management. More than forty years of information systems experience combined with over ten years of business management give him a unique perspective about the connections among business, information, data, and technology. Knowledge sharing and skill building are Dave’s passions, carried out through consulting, speaking, teaching, and writing. He is now the practice director of data management at Eckerson Group, cofounder and director of education at eLearningCurve, and a faculty member at The Data Warehousing Institute.

Explore the self-service data platform model discussed by Jeff Magnusson, VP of Data Platform at Stitch Fix. Learn about the integration of data engineering and data science roles, empowering data scientists with Flotilla API for batch job execution, and the technology stack deployed on AWS including Spark and Presto.

In this episode, Wayne Eckerson and Mike Masciandaro discuss keys to creating successful reports that users will love and use. Masciandaro provides in-depth explanations of each key (easy, drill, monitor, accurate, relevant, timely, responsive, and secure). Masciandaro is a veteran business intelligence practitioner who recently retired from an illustrious career at Dow Chemical as director of BI. During that time, Mike saw and did just about everything there is to do in the world of BI, data, and analytics. He is now intent on sharing his hard-won knowledge with others.

In this episode, Wayne Eckerson and Lenin Gali discuss the past and future of the cloud and big data. Gali is a data analytics practitioner who has always been on the leading edge of where business and technology intersect. He was one of the first to move data analytics to the cloud when he was BI director at ShareThis, a social media based services provider. He was instrumental in defining an enterprise analytics strategy, developing a data platform that brought games and business data together to enable thousands of data users to build better games and services by using Hadoop & Teradata while at Ubisoft. He is now spearheading the creation of a Hadoop-based data analytics platform at Quotient, a digital marketing technology firm in the retail industry.

In this podcast, Henry Eckerson and Stephen Smith discuss the movement to operationalize data science. Smith is a well-respected expert in the fields of data science, predictive analytics and their application in the education, pharmaceutical, healthcare, telecom and finance industries. He co-founded and served as CEO of G7 Research LLC and the Optas Corporation which provided the leading CRM / Marketing Automation solution in the pharmaceutical and healthcare industries. Smith has published journal articles in the fields of data mining, machine learning, parallel supercomputing, text understanding, and simulated evolution. He has published two books through McGraw-Hill on big data and analytics and holds several patents in the fields of educational technology, big data analytics, and machine learning. He holds a BS in Electrical Engineering from MIT and an MS in Applied Sciences from Harvard University. He is currently the research director of data science at Eckerson Group.