Secrets of Data Analytics Leaders

Treating data assets as products helps businesses increase internal data consumption and become more data driven. It also creates opportunities for monetization. But to succeed in either scenario requires product-market fit. This article presents a process for finding it. Published at: https://www.eckerson.com/articles/7-steps-for-building-a-valuable-data-product

It’s tempting to dismiss observability as another overused buzzword. But this emerging discipline offers substantive methods for enterprises to monitor and optimize business metrics, IT operations, data pipelines, machine learning models, and data quality. Published at: https://www.eckerson.com/articles/the-five-shades-of-observability-business-operations-pipelines-models-and-data-quality

An analytics center of excellence is the cornerstone of every data strategy, yet few data leaders know how to design one that works effectively. The key is to embrace federated techniques that balance standards and speed, agility, and governance. This article explains the core components of an analytics center of excellence. Published at: https://www.eckerson.com/articles/how-to-design-an-analytics-center-of-excellence

Companies are investing in new solutions—such as data fabric, data access governance, and data observability—to keep pace with expanding business appetite for data. Pervasive use of metadata to solve data management problems means that metadata is itself a valuable data asset that we must proactively manage. Published at: https://www.eckerson.com/articles/metadata-is-data-so-manage-it-like-data

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