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.

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

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.

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, Wayne Eckerson asks Steve Dine about the approach needed to migrate to the Cloud and architecture required to run analytics in the Cloud. Steve Dine talks extensively about the pitfalls to avoid during Cloud migration and finishes off by saying that even though security is a big issue, most organizations will have part of their architecture in the Cloud during the next two-three years. Steve Dine is a BI and enterprise data consultant and industry thought leader who has extensive experience in designing, delivering and managing highly scalable and maintainable modern data architecture solutions.

In this Episode, Wayne Eckerson asks Charles Reeves about his organization’s Internet of Things and Big Data strategy. Reeves is senior manager of BI and analytics at Graphics Packaging International, a leader in the packaging industry with hundreds of worldwide customers. He has 25 years of professional experience in IT management including nine years in reporting, analytics, and data governance.

In this episode, Wayne Eckerson and Shakeeb Ahkter dive into DataOps. They discuss what DataOps is, the goals and principles of DataOps, and reasons to adopt a DataOps strategy. Shakeeb also reveals the benefits gained from DataOps and what tools he uses. He is the Director of Enterprise Data Warehouse at Northwestern Medicine and is responsible for direction and oversight of data management, data engineering, and analytics.

A classic management practice dictates that a newly-appointed leader must accomplish certain things in their first 90 days. While some of this is general knowledge, there are specifics when it comes to Data Management and Analytics If you have been recently named to head any group that has to manage or facilitate the use of data, at any level in the organization, then this audio blog post is for you.

Originally published at https://www.eckerson.com/articles/what-do-you-do-first-after-being-hired-as-a-bi-analytics-data-engineering-director

In this episode, Wayne Eckerson and Rich Fox discuss what differentiates data science from analytics, why and how data science addresses business needs, why balance scorecards are relevant, and why Excel is a problem. Throughout the podcast, Fox shares many real-life examples and personal experiences.

Fox is vice president of Data Science and Analytics at Apex Parks Group, one of the largest entertainment center companies in the United States, which operates amusement parks, water parks, and family entertainment centers.

With all the hype and attention around big data and huge data platforms, there can sometimes be some data envy. There are still organizations and companies that don’t have big data: are they not poised for analytics too? Can they not get insights as well? The BI Pharaoh gives tips on how to work with your little data just like the big boys.

Originally published at https://www.eckerson.com/articles/little-data-needs-love-too

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 episode, Wayne Eckerson and Rich Galan discuss the obstacles to delivering timely analysis, the problems that large volumes of data create, solutions to those issues, and where BI is headed in the near future. Rich is a veteran data analytics leader with 20 years of experience in a variety of data-driven organizations.

Data analysts who sit in each business function (i.e., sales, marketing, finance) are critical to the success of a self-service analytics strategy. The problem is that most data analysts don’t receive the training and support they need to be proficient with self-service data and analytics tools. The easiest way to improve the skills and satisfaction of most data analysts is simple: bring them together into a power user network.

Originally published at https://www.eckerson.com/articles/power-user-networks-the-key-to-self-service-analytics

Data pipelines become chaotic with pressures of agile, democratization, self-service, and organizational “pockets” of analytics. From enterprise BI to self-service analysis, data pipeline management should ensure analysis results are traceable, reproducible, and of production strength. Robust data pipelines rely on eight critical components.

Originally published at https://www.eckerson.com/articles/the-complexities-of-modern-data-pipelines

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.

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