Nothing has galvanized the data community more in recent months than two new architectural paradigms for managing enterprise data. On one side there is the data fabric: a centralized architecture that runs a variety of analytic services and applications on top of a layer of universal connectivity. On the other side, is a data mesh: a decentralized architecture that empowers domain owners to manage their own data according to enterprise standards and make it available to peers as they desire. Most data leaders are still trying to ferret out the implications of both approaches for their own data environments. One of those is Srinivasan Sankar, the enterprise data & analytics leader at Hanover Insurance Group. In this wide-ranging, back-and-forth discussion, Sankar and Eckerson explore the suitability of the data mesh for Hanover, how the Data Fabric might support a Data Mesh, whether a Data Mesh obviates the need for a data warehouse, and practical steps Hanover might to take implement a Data Mesh built on top of a Data Fabric.

Gordon Wong is on a mission. A long-time business intelligence leader who has led data & analytics teams at HubSpot and FitBit, Wong believes BI teams aren’t data-driven enough. He says BI leaders need to think of themselves as small businesses owners and aggressively court and manage customers. He says too many don’t have metrics to track customer engagement and usage. In short, BI teams need to eat their own dog food and build success metrics to guide their activities. If you are a data or analytics leader, do you know the value your team contributes to the business? Do you have KPIs for business intelligence? Can you measure the impact of data and analytics endeavors in terms the business understands and respects? Too often BI and data leaders get caught up in technical details and fail to evaluate how their technical initiatives add value to the business. This wide-ranging interview with a BI veteran will shed light on how to run a successful BI shop.

Fast-casual restaurants offer a fascinating microcosm of the turbulent forces confronting enterprises today—and the pivotal role that data plays in helping them maintain competitive advantage. COVID prompted customers to order their Chipotle burritos, Shake Shack milkshakes, and Bruegger’s Bagels for home delivery, and this trend continues in 2022. Supply-chain disruptions, meanwhile, force fast-casual restaurants to make some fast pivots between suppliers in order to keep their shelves stocked. And the market continues to grow as these companies win customers, add locations, and expand delivery partnerships. These three industry trends—home delivery, supply-chain disruptions, and market expansion—all depend on governed, accurate data to describe entities such as orders, ingredients, and locations. Data quality and master data management therefore play a more pivotal role than ever in the success of fast-casual restaurants. Master data management, also known as MDM, streamlines operations and assists data governance by reconciling disparate data records into a golden record and source of truth. If you’re looking for an ideal case study for how MDM drives enterprise reinvention, agility, and growth, this is it. We’re excited to talk with an industry expert that helps fast-casual restaurants handle these turbulent forces with effective strategies for managing data and especially master data. Matt Zingariello is Vice President of Data Strategy Services with Keyrus, a global consultancy that helps enterprises use data assets to optimize their digital strategies and customer experience. Matt leads a team that provides industry-specific advisory and implementation services to help enterprises address challenges such as data governance and MDM. Keyrus is a partner of Semarchy, whose Intelligent Data Hub software helps enterprises govern and manage master data, reference data, data quality, enrichment, and workflows. Semarchy sponsored this podcast. In our podcast, we'll define data quality and MDM as part of data governance. We’ll explore why enterprises need data quality and MDM, and how they can craft effective data quality and MDM strategies, with a focus on fast-casual restaurants as a case study.

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.

It’s hard to find a data discipline today that is under more pressure than data governance. One on side, the supply of data is exploding. As enterprises transform their business to compete in the 2020s, they digitize myriad events and interactions, which creates mountains of data that they need to control. On the other side, demand for data is exploding. Business owners at all levels of the enterprise need to inform their decisions and drive their operations with data. Under these pressures, data governance teams must ensure business owners access and consume the right, high-quality data. This requires master data management—the reconciliation of disparate data records into a golden record and source of truth—which assists data governance at many modern enterprises. In this episode, our host Kevin Petrie, VP of Research at Eckerson Group talks with our guests Felicia Perez, Managing Director, Information as a Product Program at National Student Clearinghouse, and Patrick O'Halloran, enterprise data scientist as they define what data quality and MDM are, why you need them, and how best to achieve effective data quality and MDM.

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.

In this episode, we explore an area of data analytics that everyone knows they need to improve but no one knows how to do it. That is data literacy. Data literacy ensures that business people have the skills to accurately interpret data represented in charts, tables, and dashboards, as well as the knowledge to use those tools to gather and analyze data on their own. To guide us through the nuances of data literacy and explain how to implement it in an organization, we invited a data literacy expert to share the secrets of his trade. Kirill Makharinsky is the founder of Enki, a San Francisco-based company that provides data-as-a-second language training services. Kirill is a serial entrepreneur, having previously co-founded ETG, one the largest online B2B travel companies in Europe, and Quid, a leading research and analysis tool.

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.