Data engineers are often responsible for numerous types of data tests: unit tests, integration/component tests, performance tests, and end-to-end tests. Their best hope is to find the automated data testing tools that work for their technologies and try them. Published at: https://www.eckerson.com/articles/testing-capabilities-and-tools-for-data-engineers-part-2

Data integration, governance, and consumption play a pivotal role in the machine learning lifecycle. New offerings from Informatica illustrate the types of tools data science teams need to handle data integration, governance, and consumption. Published at: https://www.eckerson.com/articles/integrating-governing-and-consuming-data-for-the-machine-learning-lifecycle

Data architecture-as-a-service or DAaaS is a metadata-driven approach that injects SQL guardrails into no-code data development tools so business users can develop their own data pipelines without creating data silos. This article defines 10 criteria for evaluating DAaaS-based products. Published at: https://www.eckerson.com/articles/data-architecture-as-a-service-how-to-evaluate-products

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

This audio blog, the first in a series, presents the fundamentals of location intelligence. It will explore how location intelligence has evolved in recent years and the kinds of insights it can provide. Published at: https://www.eckerson.com/articles/location-intelligence-part-i-leveraging-geospatial-data-to-drive-your-organization

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

We in the West have watched Russia's invasion of Ukraine with disbelief and horror. How could this happen to a European country in the 21st century? Is there any justifiable rationale for the wanton destruction of people and property there? As we ponder these questions, our data colleagues in Ukraine have experienced the war firsthand. To help us get a handle on Ukraine's role in the data economy and how teams based there are coping with Russia's military onslaught, Wayne interviews two software executives today who share how the war has affected their companies and how they are adapting to the evolving situation. Dragos Georgescu is vice president and chief technology officer of DataClarity, an innovative data analytics vendor with a development shop in Lviv, Ukraine. Bogdan Steblyanko is CEO of CHI Software, a software development company based in Ukraine with more than 500 employees spread across four development centers, including hard-hit Kharkiv in the east, which is the company's headquarters.

COVID, inflation, broken supply chains, and not-so-distant war make this a turbulent time for the modern consumer. During times like these, families tend to their nests, which leads to lots of home-improvement projects…which means lots of painting. Today we explore the case study of a Fortune 500 producer of the paints and stains that coat many households, consumer products, and even mechanical vehicles. While business expands, this company needs to carefully align the records that track hundreds of suppliers, thousands of storefronts, and millions of customers. Business expansion and complex supply chains make it particularly important—and challenging—for enterprises such as this paint producer, which we’ll call Bright Colors, to accurately describe the entities that make up their business. They need to be governed, validated data to describe entities such as their products, locations, and customers. Master data management, also known as MDM, streamlines operations and assists data governance by reconciling disparate data records into golden records and ideally a single source of truth. We’re excited to share our conversation with an industry expert that helps Bright Colors and other Fortune 2000 enterprises navigate turbulent times with effective strategies for MDM and data governance. Dave Wilkinson is chief technology officer with D3Clarity, a global strategy and implementation services firm that seeks to ensure digital certainty, security, and trust. D3Clarity 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.

The number of women entering data professions is growing, and men need to adapt. This podcast is designed to enlighten men about the role of women in the data field. Our guests are all executives at data and analytics software companies who have held positions in other sectors of our field: Prukalpa Sankar, Cindi Howson, Debika Sharma.

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. Key Takeaways: - What is the essence of a data mesh? - How does it relate to the data fabric? - Does the data mesh require a cultural transformation? - Does the data mesh obviate the need for a data warehouse? - How does data architecture as a service fit with the data mesh? - What is the best way to roll out a data mesh? - What's the role of a data catalog? - What is a suitable roadmap for full implementation?