This blog offers five guiding principles to help CIOs, CDOs, and team leaders optimize hybrid data environments. Published at: https://www.eckerson.com/articles/balancing-act-five-principles-to-optimize-hybrid-cloud-environments

This blog, the second in a series, explores the mix of infrastructure types that support modern AI. Published at: https://www.eckerson.com/articles/cloud-on-prem-hybrid-oh-my-where-ai-adopters-host-their-projects-and-why

As cloud adoption accelerates, not all analytics workloads are heading in the same direction. This blog explores three strategic options for data and IT leaders. Published at: https://www.eckerson.com/articles/are-you-cloud-bound-the-case-for-migration-repatriation-or-keeping-your-analytics-projects-on-premises

As organizations grapple with data spread across various storage locations, solutions like Coginiti Hybrid Query offer a much-needed alternative to fragmented tools. Published at: https://www.eckerson.com/articles/a-novel-approach-for-reducing-cloud-data-warehouse-expenses-from-coginiti

Dan O'Brien and Kevin Petrie discuss FinOps, which is a cost governance discipline for cloud-based analytics and operational projects.

The modern data stack is a loose collection of technologies, often cloud-based, that collaboratively process and store data to support modern analytics. It must be automated, low code/no code, AI-assisted, graph-enabled, multimodal, streaming, distributed, meshy, converged, polyglot, open, and governed. Published at: https://www.eckerson.com/articles/twelve-must-have-characteristics-of-a-modern-data-stack

We enter 2023 in a haze of uncertainty. Enterprises must rationalize analytics projects, shift to lower-risk use cases, and control cloud costs. They also must measure the ROI of analytics projects and use data governance to reduce business risk. Published at: https://www.eckerson.com/articles/analyzing-a-downturn-five-principles-for-data-analytics-in-2023

As enterprises grow more dependent on the cloud and as the economy convulses, FinOps will soon become mandatory. Published at: https://www.eckerson.com/articles/the-rise-of-finops-cost-governance-for-cloud-based-analytics

Data orchestration uses caching, APIs, and centralized metadata to help compute engines access data in hybrid or multi-cloud environments. Data platform engineers can use data orchestration to gain simple, flexible, and high-speed access to distributed data for modern analytics and AI projects. Published at: https://www.eckerson.com/articles/data-orchestration-simplifying-data-access-for-analytics

This audio blog, the third and final in a series, recommends five guiding principles for success. Published at: https://www.eckerson.com/articles/the-customer-360-data-program-and-cloud-connectors-guiding-principles-for-success

This blog recommends criteria for enterprises to evaluate cloud connectors and ensure the benefits outweigh the costs. Published at: https://www.eckerson.com/articles/evaluating-cloud-connectors-for-the-customer-360-data-program

Enterprise data teams embrace DataOps to achieve new levels of efficiency and effectiveness in delivering data-driven solutions. Roche shows what’s possible when you combine a state-of-the-art cloud data platform with a data mesh architecture and DataOps solution. Published at: https://www.eckerson.com/articles/what-can-dataops-do-for-you-ask-roche

Today’s data architecture discussions are heavily biased toward managing data for analytics, with attention to big data, scalability, cloud, and cross-platform data management. We need to acknowledge analytics bias and address management of operational data. Ignoring operational data architecture is a sure path to technical debt and future data management pain. Published at: https://www.eckerson.com/articles/the-yin-and-yang-of-data-architecture

Knowledge graphs are a new, human-friendly way of organizing and navigating data that makes it easy to infer relationships that aren't explicitly defined. Knowledge graphs now power many applications in the cloud, including Google Search, data fabrics, and data catalogs. They make it easy to glean insights that aren't manually baked into the model. This is why people say knowledge graphs provide a rich, semantic user experience.

Joe Hilleary, a senior research analyst at Eckerson Group, has been exploring knowledge graphs for the past 12 months. He has written several excellent blogs that explain knowledge graphs in a way that makes sense even for a modeling simpleton like me! We've combined his blogs into an e-Book called "Getting Started with Knowledge Graphs" which will publish shortly on our site. Listen to this podcast and then read the eBook if you want to understand the ins and outs of knowledge graphs.

The advent of big data, self-service analytics, and cloud applications has created a need for new ways to manage data access. New data access governance tools promise to simplify and standardize data access and authorization across an enterprise. Data management expert, Sanjeev Mohan, provides an industry perspective on this emerging technology and what it means for data analytics teams.

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.

The COVID shock forces enterprises in every market to accelerate and reshape their data analytics strategies. This trend is likely to continue. “Data Elite” enterprises survived this year through a mix of agility, efficiency, and intelligence. They met these requirements of survival as they accelerated their digital transformations, adopted cloud data platforms and embraced advanced analytics. As these data leaders continue their momentum in 2021, the data laggards will strive to catch up.

In this episode, Kevin Petrie, VP of Research at Eckerson Group, interviews Sumeet Agrawal, VP of Product Management at Informatica, to discuss the impact of COVID on enterprises. Sumeet talks about the trends of adoption during the onslaught of COVID and how enterprises are navigating in the post-pandemic era.

This is an audio blog on BI on the Cloud Data Lake and how to improve the productivity of data engineers. We'll dive deeper into the question; what’s the best measure of success for data pipeline efficiency? This is part 2 of a two part blog.

Originally published at: https://www.eckerson.com/articles/business-intelligence-on-the-cloud-data-lake-part-2-improving-the-productivity-of-data-engineers

This audio blog is about business intelligence on the cloud data lake and why it arose and how to architect for it. This is Part 1 of a two part blog series.

Originally published at: https://www.eckerson.com/articles/business-intelligence-on-the-cloud-data-lake-part-1-why-it-arose-and-how-to-architect-for-it