BI-Survey

FEBRUARY 17, 2020

With the growing interconnectedness of people, companies and devices, we are now accumulating increasing amounts of data from a growing variety of channels. New data (or combinations of data) enable innovative use cases and assist in optimizing internal processes. This is where data governance comes in. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

TDAN

AUGUST 15, 2023

I just attended the 17th Annual Chief Data Officer and Information Quality Symposium in July, and there, I heard many creative suggestions for renaming data governance. Calling it data enablement, data trust, data utilization, and many other names to try and avoid the […]

Data Governance 52

Data Governance Data Enablement Data Quality IT 52

Grooper

AUGUST 19, 2019

Choosing the best analytics and BI platform for solving business problems requires non-technical workers to “speak data.”. A baseline understanding of data enables the proper communication required to “be on the same page” with data scientists and engineers. From here on out, I’ll refer to ML and data science as just AI.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

DataKitchen

JUNE 24, 2021

In May 2021 at the CDO & Data Leaders Global Summit, DataKitchen sat down with the following data leaders to learn how to use DataOps to drive agility and business value. Kurt Zimmer, Head of Data Engineering for Data Enablement at AstraZeneca. Jim Tyo, Chief Data Officer, Invesco.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

Alation

NOVEMBER 11, 2021

It’s the one thing that can save data teams from the risk of processing data from their own circular references, as this framework is a credible check-and-balance. Data Sovereignty and Cross?Border International data sharing is essential for many businesses. and simply sharing data across borders is not permitted.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

CIO Business Intelligence

MARCH 27, 2025

The consequences of getting identity wrong are substantial: Poor data quality = missed insights, operational inefficiencies, and wasted marketing spend. Slow digital adoption = inability to activate customer data reliably at scale. [i] We share three common mistakes that hinder data strategies and how they can be fixed.

Data Strategy 71

Data Strategy Strategy Digital Transformation Data-driven 71