CIO Business Intelligence

APRIL 29, 2022

Despite soundings on this from leading thinkers such as Andrew Ng , the AI community remains largely oblivious to the important data management capabilities, practices, and – importantly – the tools that ensure the success of AI development and deployment. Further, data management activities don’t end once the AI model has been developed.

Data Governance 145

Data Governance IT Risk Data Lake 145

erwin

MAY 2, 2022

Data modeling is a serious scientific method with many rules and best practices. One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. Look again at Figure 7, what is the difference between an insured and a beneficiary? What is an entity? Aren’t they just both people?

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face.

Data Warehouse 67

Data Warehouse Data Lake Cost-Benefit Modeling 67

Ontotext

SEPTEMBER 2, 2020

Instead, it creates a unified way, sometimes called a data fabric, of accessing an organization’s data as well as 3rd party or global data in a seamless manner. Data is represented in a holistic, human-friendly and meaningful way. For efficient drug discovery, linked data is key.

Insurance 72

Insurance Metadata Publishing Unstructured Data 72

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

Jet Global

JUNE 17, 2021

Managing Data Integrity. Before rolling the new process out, the company needed to address data integrity, a normal stage in any new software implementation project. Following the data integrity phase, the company focused on setting up the correct processes and on rightsizing the project.

Risk 98

Risk Recreation/Entertainment Forecasting Manufacturing 98

O'Reilly on Data

MARCH 20, 2019

So, whatever the commercial application of your model is, the attacker could dependably benefit from your model’s predictions—for example, by altering labels so your model learns to award large loans, large discounts, or small insurance premiums to people like themselves. Sometimes also known as an “exploratory integrity” attack.)

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

IBM Big Data Hub

AUGUST 7, 2023

Transparency throughout the data lifecycle and the ability to demonstrate data integrity and consistency are critical factors for improvement. The ledger delivers tamper evidence, enabling the detection of any modifications made to the data, even if carried out by privileged users.

Data Governance 66

Data Governance Manufacturing Interactive IoT 66

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.)

Cost-Benefit 80

Cost-Benefit Metadata Data Governance Modeling 80

Alation

FEBRUARY 10, 2022

Let’s discuss what data classification is, the processes for classifying data, data types, and the steps to follow for data classification: What is Data Classification? Either completed manually or using automation, the data classification process is based on the data’s context, content, and user discretion.

Data Governance 52

Data Governance Risk Insurance Strategy 52