Most data leaders want to deliver data products, but few are doing it.

Let's face it: most data teams today function as internal service bureaus that fulfill customer requests that arrive via ticketing systems, email, handwritten notes, or calls from colleagues looking for a favor. Most work double time to keep their request backlogs from ballooning from weeks to months. In this environment, few data leaders have time or capacity to switch from a project management approach to a product management one.

Even if data leaders had time, most wouldn't know how to make this transition. Most have no experience in product management, nor do they have a good idea of a data product. So asking data leaders to deliver data products is like asking them to build a rocket ship that can travel to the moon.

In this episode, Wayne Eckerson interviews Henrik Strandberg, a strong proponent of running data teams using product management principles. Henrik Strandberg is a seasoned data transformation leader who, for the past 25 years, has helped numerous organizations bridge gaps between business and technology. In stints at publishing and gaming companies, Henrik has developed a unique understanding of building and delivering data products at scale that delight customers.

Data architecture-as-a-service (DaaS) is a new self-service paradigm that empowers local data owners to create architecturally compliant data repositories. By abstracting data architecture within self-service tools, DaaS solves the problem of data silos, which wreak havoc on enterprise data consistency and trustworthiness. Published at: https://www.eckerson.com/articles/data-architecture-as-a-service-liberation-for-data-users

This audio blog is about the data lakehouse and how it is the latest incantation from a handful of data lake providers to usurp the rapidly changing cloud data warehousing market. It is one of three blogs featured in the data lakehouse series.

Originally published at: https://www.eckerson.com/articles/all-hail-the-data-lakehouse-if-built-on-a-modern-data-warehouse

This is an audio blog about the perplexities of the Data Lakehouse and if it is, indeed, the "paradigm of the decade". To hear more of Eckerson Group perspectives on the data lakehouse be sure to check out the blogs from colleagues, Wayne Eckerson and Kevin Petrie, and the recording of our recent Shop Talk discussion.

Originally published at: https://www.eckerson.com/articles/an-architect-s-view-of-the-data-lakehouse-perplexity-and-perspective

Data leaders who launch self-service analytics programs without knowing their business users risk unleashing chaos. Data leaders need to canvas the organization and understand who produces what information for whom and where.

Originally published at https://www.eckerson.com/articles/succeeding-with-self-service-analytics-know-thy-customer

In this episode, Wayne Eckerson and Matthew Schwartz discuss non-traditional uses of business intelligence tools. Although BI tools have been around for almost three decades, most companies just scratch the surface of what’s possible to do with those tools. Using web layers and APIs, a company can use their imagination to customize and leverage their exiting BI tool-set to monetize data, integrate tribal knowledge and build industry-specific proprietary products.

Matthew Schwartz is the chief technology officer of Sage Hospitality, one of the world's largest hotel operators. Although Matt is responsible for all aspects of Sage’s IT operations, he has a deep fondness for data and analytics, having served as a BI director for several companies, including PetSmart and Staples. Matt firmly believes in the power of BI tools to transform organizations.

This audio blog focuses on the importance of establishing a strong relationship between business and technical teams and describes various business engagement models that sit at the heart of all successful data analytics programs.

How do you organize a data analytics program to maximize value for the organization? Although there is no right or wrong way to do this, several patterns emerge when you examine successful organizations.

Originally published at https://www.eckerson.com/articles/organizing-for-success-part-ii-how-to-organize-a-data-analytics-program

Despite our reliance on reports, the state of reporting in most companies is awful. There are too many reports in too many locations created by too many different people and tools.

The goal of self-service analytics is to empower business people to build their own reports, dashboards, and predictive models. If that happens, does your company still need a corporate business intelligence team?

Originally published at https://www.eckerson.com/articles/organizing-success-part-1-organize-bi-team

Explore how data analytics leaders can balance the use of specialists and generalists.

Originally published at https://www.eckerson.com/articles/specialists-or-generalists

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.

Why is Data Quality still an issue after all these years? To get an answer to the prevalent question, Wayne Eckerson and Jason Beard engage in a dynamic exchange of questions which lead us to the root cause of data quality and data governance problems. Using examples from his past projects, Jason shows the value of business process mapping and how it exposes the hidden problems which go undetected under the standard IT lens.

In his most recent role as Vice President of Process & Data Management at Wiley, a book publisher, he was responsible for master data setup and governance, process optimization, business continuity planning, and change management for new and emerging business models. Jason has led business intelligence, data governance, master data management, Process Improvement, Business Transformation, and ERP projects in a variety of industries, including Scientific and Trade publishing, Educational Technology, Consumer Goods, Banking, Investments, and Insurance.

Being a change agent is hard. It's tough to inspire people and get them motivated to work on a shared vision. To understand the mechanics of digitalization and tactics required to implement them, Wayne Eckerson invited Andrea Ballinger so that she could share her hard-won lessons from her illustrious career as a technology leader.   Andrea is currently leading a transformation program at LSU, revamping the university’s information technology resources across multiple campuses. Prior to that, she served as Interim CEO and President for the University of Illinois  Alumni Association and CTO of Illinois State University. She began her data career at the University of Illinois where she earned a reputation as the foremost data warehousing expert in higher education.

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 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.

BI teams often work extremely hard but have little to show for their effort, and they can never get ahead of a continuous backlog of requests. The business rewards their hard work by reducing their budget and staff. Even if they know what needs to be done, they have little authority to make it happen. To succeed, BI leaders need to partner with the business and deliver quick wins to turn the tide of adoption in their favor.

Originally published at https://www.eckerson.com/articles/why-bi-teams-struggle-the-tipping-point-of-success

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.

A data analyst workbench will inevitably integrate data catalog, data preparation, and data analysis functionality. Data analysts don’t want to jump from tool to tool when executing a workflow that is both linear and iterative. Data analysts with this kind of workbench will be more productive and foster higher levels of reuse and data literacy.

Originally published at https://www.eckerson.com/articles/self-service-triumvirate-the-new-data-analyst-workbench

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.

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.

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