Secrets Of Data Analytics Leaders

This blog explores four use cases for data orchestration and examples of the supporting architectural elements. Published at: https://www.eckerson.com/articles/architecting-data-orchestration-four-use-cases

There are many models for bridging business and technical teams. These models can be more centralized or decentralized in nature, depending on the culture of the organization and nature of the business domain. Each requires a strong enterprise data teams comprised of multiple departments and roles. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-iii-team-composition-and-dynamics

Data observability provides intelligence about data quality and data pipeline performance, contributing to the disciplines of DataOps and FinOps. Vendors such as DataKitchen, DataOps.live, Informatica, and Unravel offer solutions to help enterprises address these overlapping disciplines. Published at: https://www.eckerson.com/articles/the-blending-disciplines-of-data-observability-dataops-and-finops

Hybrid development teams are critical to the success of a data & analytics program. Data leaders must invest time, energy, and thought to the creation of these teams and how best to support them. It’s critical that they allocate staff time to nurture knowledge flows between component groups. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics-part-ii-knowledge-flows

“Metadata is data about data” is a bad definition. It’s vague and recursive. It’s like saying climate is the weather of weather. But if metadata is not data about data, then what is it? Follow my thought process working toward a better understanding of metadata and its role in today’s data landscape. Published at: https://www.eckerson.com/articles/fixing-metadata-s-bad-definition

Data Mesh gets a lot of discussion. Some see it as revolutionary—the first new data architecture thinking in years. Others view it as a dangerous backward slide to the chaos of data silos. The reality lies somewhere between—a big shift in architecture thinking with some inherent risk. A few good architecture practices help to realize the benefits while managing the risks. Published at: https://www.eckerson.com/articles/a-thoughtful-approach-to-data-mesh

Many machine learning projects fail because data scientists don’t have the right data. Techniques such as synthetic data is a novel algorithmic approach to address algorithmic risks. Published at: https://www.eckerson.com/articles/synthetic-data-for-ai-definition-risks-and-strategies

Many machine learning projects fail because data scientists don’t have the right data. Techniques such as synthetic data is a novel algorithmic approach to address algorithmic risks. Published at: https://www.eckerson.com/articles/synthetic-data-for-ai-definition-risks-and-strategies

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

Data access management (DAM) is the process of defining and enforcing policies that control access to application data throughout the enterprise. New approaches to DAM provide greater access and better protection through centrally managed policies that are universally enforced. Published at: https://www.eckerson.com/articles/data-access-management-zero-sum-game-over

This article, the third in a series, dives into the technologies that underpin modern approaches to location intelligence. It explores databases for industrial-scale geospatial applications, advanced business intelligence (BI) tools for exploratory analysis, and simple use-case specific platforms. Published at: https://www.eckerson.com/articles/location-intelligence-part-iii-enabling-technologies

The data mesh makes business domain experts the owners of their data, which they deliver as a “data product” to analytics teams using a self-service data platform and a federated governance framework. Published at: https://www.eckerson.com/articles/why-enterprises-should-implement-the-data-mesh-with-dataops

An operating model for data & analytics is critical for aligning resources across the enterprise and balancing the needs for agility and governance. An effective operating model is critical to data & analytics success and its creation and upkeeping should be the primary focus of a chief data officer. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics

In this article, Lawson Abinati lays out core principles for market positioning that apply across all industries, as well as to Business Intelligence (BI) professionals. Although many business intelligence (BI) managers see themselves as technologists first, unless they understand the soft skills of sales, marketing, and communication, they won't succeed professionally or make good on their organization's investments in BI. Published at: https://www.eckerson.com/articles/what-is-positioning-and-why-is-it-important

This article summarizes the major characteristics of a modern data architecture and serves as a guide for organizations that are in the midst of developing a new data strategy for the modern age. Published at: https://www.eckerson.com/articles/ten-characteristics-of-a-modern-data-architecture

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

Design patterns have proven valuable in many endeavors. Can data pipeline design patterns help to break the data engineering logjam? Published at: https://www.eckerson.com/articles/data-pipeline-design-patterns

The need for adaptable data management architecture has never been more pressing. Yet getting there seems to be more confusing than ever. The field is rampant with buzzwords: data lake, data lakehouse, data fabric, data mesh, data hub, data as a network. Making sense of the confusion begins with sorting out the buzzwords. Published at: https://www.eckerson.com/articles/data-architecture-complex-vs-complicated

Data mesh is an evolutionary concept that’s gained a lot of traction in the software engineering world. It’s both exciting and unnerving—exciting because it represents a sea change in engineering attitudes toward data; unnerving because of its potential to create organizational confusion. Published at: https://www.eckerson.com/articles/the-exciting-unnerving-vision-of-data-mesh

This article, the second in a series, shares cutting-edge examples of location intelligence applications from the real world to help readers understand what’s possible with geospatial data. Published at: https://www.eckerson.com/articles/location-intelligence-part-ii-real-world-examples-from-businesses-that-use-geospatial-data