DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Sanjeev Mohan

FEBRUARY 6, 2019

Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline? Then in the middle of 2017, a realization set in that we were one year away from GDPR and needed to focus on data governance. I ended up writing two documents on data governance. To address client questions about cloud, I wrote a document on GCP.

Data Analytics 49

Data Analytics Analytics Data Governance Experimentation 49

AWS Big Data

NOVEMBER 6, 2023

The following graph describes a simple data quality check pipeline using setup and teardown tasks. To learn more about Setup and Teardown tasks, refer to the Apache Airflow documentation. For a complete list of installed packages and their versions, refer to this MWAA documentation.

Metrics 102

Metrics Metadata Snapshot Management 102

Alation

OCTOBER 5, 2021

However, about 50% of those surveyed also admit that they do not assess, monitor, or measure their data governance systems. Perhaps even more alarming: fewer than 33% expect to exceed their returns on investment for data analytics within the next two years. Data governance and AI.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses. Let’s summarize very quickly.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Ontotext

JUNE 22, 2023

On top of that, data is sometimes unreliable , and inaccurate or missing metadata makes it hard to decide which information to trust. It facilitates finding information and exploring it visually and enables deeper data analytics based on graph and AI algorithms.

Metrics 52

Metrics Statistics Visualization Metadata 52

Domino Data Lab

JULY 21, 2019

The approach is to provide remote access for government analysts and researchers to confidential data in a secure data facility and to build analytical capacity and collaborations through an Applied Data Analytics training program. What am I talking about with the new types of data? Transcript.

Metadata 65

Metadata Statistics Risk Machine Learning 65

Andrew White

MARCH 22, 2019

to weave together the governance and management of master data, application data, and less-widely shared data, and just enough enterprise metadata management. Your Future Requires You to Define Your Real Master Data. Data and analytics (and maybe AI) are getting very tired terms.

Analytics 68

Analytics Metadata Data Governance Management 68

Alation

MARCH 21, 2023

Why is data analytics important for travel organizations? With data analytics , travel organizations can gain real-time insights about customers to make strategic decisions and improve their travel experience. How is data analytics used in the travel industry?

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Smart Data Collective

SEPTEMBER 26, 2021

A NoSQl database can use documents for the storage and retrieval of data. The central concept is the idea of a document. Documents encompass and encode data (or information) in a standard format. A document is susceptible to change. The documents can be in PDF format. Speaking of which.

Metadata 99

Metadata Visualization Unstructured Data Data mining 99

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

Metadata 59

Metadata Data Quality Statistics Data Science 59

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.) This allows for a high degree of transparency and auditability.

Cost-Benefit 83

Cost-Benefit Metadata Data Governance Modeling 83

Cloudera

AUGUST 4, 2021

And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. Security: It must serve data throughout a system.

Data Governance 83

Data Governance Metadata Data-driven Enterprise 83

Andrew White

JANUARY 9, 2022

To drive a successful Data Analytics strategy do you think it is a multidisciplinary activity and if so, what additional roles would you expect to see involved. I would have to admit that there are few documents that talk about all the connections across any set of topics. We write about data and analytics.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Alation

NOVEMBER 22, 2022

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52