datapine

MAY 31, 2022

Like many other branches of technology, security is a pressing concern in the world of cloud-based computing, as you are unable to see the exact location where your data is stored or being processed. This increases the risks that can arise during the implementation or management process. Cost management and containment. Compliance.

Risk 237

Risk Cost-Benefit Business Intelligence Data-driven 237

O'Reilly on Data

OCTOBER 22, 2020

banking, insurance, etc.), It’s also a good indirect measure of training data quality: a team that does not know where their data originated is likely to not know other important details about the data as well. .” Why it’s useful: How can you truly know what is good without also knowing what is bad?

Consulting 303

Consulting Risk Cost-Benefit Modeling 303

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. Industries such as banking and credit, insurance, healthcare and biomedicine, hiring and employment, and housing are often tightly regulated. Meeting Regulatory Expectations.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

Cloudera

SEPTEMBER 7, 2023

With AI, financial institutions and insurance companies now have the ability to automate or augment complex decision-making processes, deliver highly personalized client experiences, create individualized customer education materials, and match the appropriate financial and investment products to each customer’s needs.

Insurance 83

Insurance Risk Data-driven Finance 83

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Reshape and aggregate data as necessary. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Alation

OCTOBER 28, 2021

Leaders are asking how they might use data to drive smarter decision making to support this new model and improve medical treatments that lead to better outcomes. Yet this is not without risks. To make good on this potential, healthcare organizations need to understand their data and how they can use it.

Data Governance 52

Data Governance Measurement Metrics Data Quality 52

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.

Metrics 49

Metrics Statistics Data Quality Measurement 49

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources.

Cost-Benefit 77

Cost-Benefit Metadata Data Governance Modeling 77

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

AWS Big Data

AUGUST 14, 2023

At the same time, unstructured approaches to data mesh management that don’t have a vision for what types of products should exist and how to ensure they are developed are at high risk of creating the same effect through simple neglect. When do new data products get created, and who is allowed to create them?

Management 80

Management Risk Measurement Optimization 80

Alation

AUGUST 26, 2021

Data intelligence has thus evolved to answer these questions, and today supports a range of use cases. Examples of Data Intelligence use cases include: Data governance. Cloud Data Migration. Privacy, Risk and Compliance. Let’s take a closer look at the role of DI in the use case of data governance.

Metadata 64

Metadata Data Governance Dashboards Software 64

Andrew White

JANUARY 11, 2021

As such banking, finance, insurance and media are good examples of information-based industries compared to manufacturing, retail, and so on. But we also know not all data is equal, and not all data is equally valuable. Some data is more a risk than valuable. Would really like to explore this one in debate.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52