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.

The road to AI adoption is far more complex than one can imagine. Building data science models and testing them is only one piece of the puzzle. To understand the roadblocks and best practices, Wayne Eckerson invited Nir Kaldero in our latest episode to learn why organizations need to start paying more attention to people, culture and processes to make data science projects a success and how democratization skills pays off in the long run.

Nir Kaldero is the Head of Data Science, Vice President at Galvanize Inc. and the creator of the GalvanizeU Master’s of Science in Data Science program. A tireless advocate for transforming education and reshaping the field of data science, his vision and mission is to make an impact on a wide variety of communities through education, science, and technology. In addition to his work at some of the world’s largest international corporations, Kaldero serves as a Google expert/mentor and has been named an IBM Analytics Champion 2017 & 2018, a prestigious honor given to leaders in the field of science, technology, engineering, and math (STEM).

In this episode, Daniel Graham dissects the capabilities of data lakes and compares it to data warehouses. He talks about the primary use cases of data lakes and how they are vital for big data ecosystems. He then goes on to explain the role of data warehouses which are still responsible for timely and accurate data but don't have a central role anymore. In the end, both Wayne Eckerson and Dan Graham settle on a common definition for modern data architectures.

Daniel Graham has more than 30 years in IT, consulting, research, and product marketing, with almost 30 years at leading database management companies. Dan was a Strategy Director in IBM’s Global BI Solutions division and General Manager of Teradata’s high-end server divisions. During his tenure as a product marketer, Dan has been responsible for MPP data management systems, data warehouses, and data lakes, and most recently, the Internet of Things and streaming systems.

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.