Secrets of Data Analytics Leaders

Before a company hires data science talent, they should understand the role and types of data scientists. Failing to differentiate between research, applied, and citizen data scientist can result in appointing the wrong people on crucial projects. To continue our previous episode's discussion, we invited Alex Vayner for a second time to get an answer to the question: What is a data scientist? Alex Vayner is a Partner and Americas Data & AI Practice Leader for PA Consulting Group, an innovation and transformation consultancy. Alex has spent his entire career in data & analytics, with his last five roles focused on building and running high-performance data science teams and capabilities in consulting and corporate environments. Before joining PA Consulting, Alex ran the NA Data Science & AI practice at Capgemini. He joined Capgemini from Equifax, where he served as VP, Global Data Innovation Leader, building a team responsible for pioneering disruptive data & analytics solutions for clients across all industries.

Explore how data analytics leaders can balance the use of specialists and generalists. Originally published at https://www.eckerson.com/articles/specialists-or-generalists

New challenges to analytics platforms have prompted SAS to create new responses. The software giant responds with automation and decision support tools.

Data science has made immense progress, but companies are still stuck with the question: how do you use data science to deliver real value to the business? They hire dozens of data scientists and invest in state-of-the-art technology, but only a few have delivered ROI and business impact. In this episode, Wayne Eckerson and Alex Vayner discuss what organizations need to do for data science success. Alex Vayner is a Partner and Americas Data & AI Practice Leader for PA Consulting Group, an innovation and transformation consultancy. Alex has spent his entire career in data & analytics, with his last five roles focused on building and running high-performance data science teams and capabilities in consulting and corporate environments. Before joining PA Consulting, Alex ran the NA Data Science & AI practice at Capgemini. He joined Capgemini from Equifax, where he served as VP, Global Data Innovation Leader, building a team responsible for pioneering disruptive data & analytics solutions for clients across all industries.

IoT has created a tidal wave that data savvy organizations can turn into profitable business solutions. Most IoT data comes from sensors, which are now attached to almost every device imaginable, from factory floor machines and agricultural fields to your cell phone and toothbrush. But IoT is forcing companies to rethink their data architectures to ingest, process, and analyze streaming data in real-time. To help us understand the impact of IoT on data architectures, we invited Dan Graham to our show for a second time. Dan is a former product marketing manager at both IBM and Teradata, renowned for combining deep technical knowledge with industry marketing savvy. During his tenure at those companies, he was responsible for MPP data management systems, data warehouses, and data lakes, and most recently, the Internet of Things.

Just-in-time design is the practice of designing working software in small increments that support a business-defined need or story. Just-in-time design, as well as just-in-time testing, is an integral part of the agile software methodology. In fact, you can’t really do agile without just-in-time design. To help us understand the nuances of just-in-time design, we invited Aaron Fuller, a long-time data architect and member of Eckerson Group’s consulting network. Across an 11-year career as the enterprise data architect for an insurance company, he modeled data, created technical designs for a broad range of systems, established governance and stewardship, and led the establishment of their enterprise data warehousing, business intelligence, and enterprise architecture programs. As principal consultant and owner of Superior Data Strategies since 2010, he leads a team of highly skilled data professionals who are uniquely capable of planning and executing agile data projects.

Last month, I attended Domo’s annual user conference for the first time. I came a skeptic, but left a believer. Domo has invested large sums of money to create a comprehensive data and analytics platform that scales to run small and medium-size businesses, and possibly large ones. Most importantly, it has a cadre of highly satisfied brand-name customers who want to extend the platform to support all business users and their analytic applications. Originally published at: https://www.eckerson.com/articles/catching-the-domo-spirit

Processing continuous data streams is becoming increasingly important. However, traditional analytics architectures were often not built for real-time scenarios. This article will illustrate challenges and discuss how streaming-first approaches can change the way we think about analytics architectures. Originally published at: https://www.eckerson.com/articles/streams-everywhere-towards-streaming-first-architectures

This second article in a series on modern data architectures. It focuses on what drives customers to want a modern data architecture (i.e., fear and opportunity) in the first place. It then lists ten requirements that customers desire for a modern data architecture, ranging from “cloud-first” and “streaming-first” to “best of breed” and “predictable pricing”. Originally published at: https://www.eckerson.com/articles/ten-things-companies-want-from-a-modern-data-architecture

Data virtualization has been around for decades and has always been controversial. In the 1990s, it was called virtual data warehousing or VDW-- or as some skeptics liked to say, "voodoo and witchcraft”. It’s also been known as query federation and more recently, data services. The idea is that business users don't need to know the location of the data; they merely need to log into the data service and all data appears as if it’s local to their server, modeled in a fashion that makes sense to them. Andrew Sohn is the Global Head of Data and Analytics at Crawford & Company, a $1B+ service provider to the insurance and risk management industry, where he designed and leads its data and digital transformation strategy and program. With more than 25 years in the industry, Andrew has managed a broad range of infrastructure and application technologies. He’s a strong advocate of data virtualization technology and believes it is an integral part of a modern, agile data ecosystem.