Data integration, governance, and consumption play a pivotal role in the machine learning lifecycle. New offerings from Informatica illustrate the types of tools data science teams need to handle data integration, governance, and consumption. Published at: https://www.eckerson.com/articles/integrating-governing-and-consuming-data-for-the-machine-learning-lifecycle

In the physical world, you can see a bridge rusting or a building facade crumbling and know you have to intervene to prevent the infrastructure from collapsing. But when all you have is bits and bytes - digital stuff, like software and data ---how can you tell if your customer-facing digital interactions or data-driven analytics and models are about to go up in smoke?

Observability is a new term that describes what we used to call IT monitoring. The new moniker is fitting given all the technology changes that have happened in the past decade. The cloud, big data, microservices, containers, cloud applications, machine learning, and artificial intelligence have created a dramatically complex IT and data environment that is harder than ever to manage. And the stakes are higher as organizations move their operations online to compete with digital natives. Today, you can't run digital or data operations without observability tools.

Kevin Petrie is one of the industry's foremost experts on observability. He is vice president of research at Eckerson Group where he leads a team of distinguished analysts. He recently wrote an article titled "The Five Shades of Observability" that describes five types of observability tools. In this podcast, we discuss what observability is, why you need it, and the types of available tools. We also speculate on the future of this technology and recommend how to select an appropriate observability product.

Every December, Eckerson Group fulfills its industry obligation to summon its collective knowledge and insights about data and analytics and speculate about what might happen in the coming year. The diversity of predictions from our research analysts and consultants exemplifies the breadth of their research and consulting experiences and the depth of their thinking. Predictions from Kevin Petrie, Joe Hilleary, Dave Wells, Andrew Sohn, and Sean Hewitt range from data and privacy governance to artificial intelligence with stops along the way for DataOps, data observability, data ethics, cloud platforms, and intelligent robotic automation.

Continuous Intelligence (CI) integrates historical and real-time analytics to automatically monitor and update various types of systems, including supply chains, telecommunications networks and e-commerce sites. CI encompasses data ingestion, transformation and analytics, as well as operational “triggers” that recommend or initiate specific real-time actions.

CI casts a wider net than traditional analytics because it includes contextual data, for example related to market behavior, weather patterns or social media trends, that help enterprises operate the core systems more intelligently.

In this episode, our VP of Research Kevin Petrie interviews Simon Crosby, CTO at Swim.ai, a continuous intelligence software vendor that focuses on edge-based learning for fast-data. He co-founded security vendor Bromium in 2010, later sold to HP Inc in 2019.

This blog is about Graphics Processing Units (GPU) and how much of the focus and industry growth in the use of GPUs has come from their suitability for machine learning, especially neural networks, more commonly known as deep learning.

Originally published at: https://www.eckerson.com/articles/gpu-databases-getting-more-value-from-your-machine-learning-infrastructure

This blog is about the challenge of data sprawl and how the combination of AI-based entity matching, schema matching, and enhanced knowledge graphing is moving us ever closer to the vision of self-driving data.

Originally published at: https://www.eckerson.com/articles/using-data-knowledge-to-conquer-data-sprawl

The rise of machine learning has placed a premium on finding new sources of data to fuel predictive models. But acquiring external data is often expensive and many data sets are rife with errors and difficult to combine with internal data. But that’s going to change in 2020.

To help us understand the scale, scope, and dimensions of emerging data marketplaces is Justin Langseth, one of the visionaries in our space. Justin is a VP at Snowflake responsible for the Snowflake Data Exchange.  Prior to Snowflake, Justin was the technical founder and CEO/CTO of 5 data technology startups: Claraview (sold to Teradata), Zoomdata (sold to Logi Analytics), Clarabridge, Strategy.com, and Augaroo. He has 25 years of experience in business intelligence, natural language processing, big data, and AI.

With the growing popularity of machine learning and artificial intelligence, creating a data science program is a key initiative at most companies today. However, it’s not always clear to executives how they can deliver a return on investments in data science. To explain this, we invited an expert who has spent most of his career in the data science trenches and has a clear-minded perspective on how to deliver ROI with data science.

Alan Jacobson is the chief data and analytics officer (CDAO) of Alteryx, driving key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson leads the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains, responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.

Prior to joining Alteryx, Alan held a variety of leadership roles at Ford Motor Company across engineering, marketing, sales and new business development; most recently leading a team of data scientists to drive digital transformation across the enterprise. As an Alteryx evangelist at Ford, Alan spent many years leveraging the Alteryx Platform across the company and witnessed first-hand the impact a culture of analytics can have on the bottom line and what it takes to succeed as a data-driven enterprise.

Although dashboards will never disappear, they will be radically transformed by artificial intelligence. They will become more intelligent, predictive, timely, and conversational.

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.

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.

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).

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 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.

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.