Secrets of Data Analytics Leaders

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...

This audio blog probes the business value in analytics by examining the concept of Action Distance. Originally published at https://www.eckerson.com/articles/finding-value-in-analytics-action-distance

One of the hardest parts of running a data analytics program inside a large organization is governing data and reports. It’s simply too easy for the definition of core data elements and metrics to get out of sync and reports to contain conflicting information. Angie Davis has straddled both the business and IT worlds for more than 20 years. She served as a business analyst in several 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.

Learn how to achieve the DataOps objectives of improved efficiency and data quality by migrating to a streaming architecture based on Apache Kafka.

Companies that excel at advanced analytics and data science maximize the value of their data. They unearth hidden opportunities and become innovators in the industry. Although each organization has different goals, the underlying processes and tools to become successful at analytics remain somewhat the same. In this episode, Alan Jacobson explains them one by one and finishes off with his top thre...

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 h...

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

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...

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...

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 re...

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...

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 a...

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-...

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.e...

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 servic...

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...

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, reva...

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 democ...

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 Gra...