Octopai

APRIL 19, 2021

BI teams will have a better handle on their data’s history, its current status, and any changes it may have undergone. Without organized metadata management, the validity of a company’s data is compromised and they won’t achieve adequate compliance, data governance, or generate correct insights. Donna Burbank.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

Cloudera

DECEMBER 1, 2022

Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. RI is a global leader in the design and deployment of large-scale, production-level modern data platforms for the world’s largest enterprises.

Metrics 84

Metrics Data Quality Data Lake Statistics 84

Alation

NOVEMBER 2, 2022

We had not seen that in the broader intelligence & data governance market.”. Right now, it’s probably not a secret that the amount and the pace of financings – if you compare 2022 to 2021 – is night and day,” he continues. “It The lakehouse] helps businesses really harness the power of data and analytics and AI.

Data Governance 98

Data Governance Marketing Finance Data Lake 98

AWS Big Data

APRIL 19, 2023

The goal of a data product is to solve the long-standing issue of data silos and data quality. Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. We focus on the former.

Technology 82

Technology Data-driven Machine Learning Sales 82

Andrew White

JANUARY 9, 2022

– In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. I would take a look at our Top Trends for Data and Analytics 2021 for additional AI, ML and related trends. We did in fact do so last year for the Leadership Vision for D&A Leaders 2021.

Analytics 64

Analytics Measurement Modeling Data-driven 64

IBM Big Data Hub

APRIL 27, 2022

Data fabric and data mesh are emerging data management concepts that are meant to address the organizational change and complexities of understanding, governing and working with enterprise data in a hybrid multicloud ecosystem. The good news is that both data architecture concepts are complimentary.

Management 80

Management Metadata Data Architecture Data Lake 80