IBM Data Science in Practice

NOVEMBER 28, 2022

Thus, the earlier in the process that data is cleansed and curated, the more time data consumers can reduce in data preparation and cleansing. This leaves more time for data analysis. Let’s use address data as an example.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

Precisely

FEBRUARY 10, 2023

Nearly two-thirds of data practitioners believe they are expected to make data-driven decisions, yet only 30% believe that their actions are genuinely supported by data analysis. As the drive toward data-driven business decisions continues, most executives are keenly aware of this trust gap.

DataOps 52

DataOps Data Observability ML ML 52

IBM Journey to AI blog

JUNE 5, 2024

That’s why today’s application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) technology to sift through big data, provide valuable business insights and deliver superior data observability. What are application analytics?

Analytics 94

Analytics Analytics AI AI 94

IBM Journey to AI blog

DECEMBER 19, 2023

Machine learning algorithms for unstructured data include: K-means: This algorithm is a data visualization technique that processes data points through a mathematical equation with the intention of clustering similar data points. Isolation forest: This type of anomaly detection algorithm uses unsupervised data.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

AWS Machine Learning Blog

FEBRUARY 15, 2024

When the predicted temperature for that data is similar to the observed temperature in that data, the motor is working normally; a discrepancy will point to an anomaly, such as the cooling system failing or a defect in the motor. The predicted value indicates the expected value for our target metric based on the training data.

ML 125

ML ML Data Science AWS 125

Precisely

SEPTEMBER 11, 2023

” Solution: Intelligent solutions can mine metadata, analyze usage patterns and frequencies, and identify relationships among data elements – all through automation, with minimal human input. Problem: “We face challenges in manually classifying, cataloging, and organizing large volumes of data.”

Data Quality 52

Data Quality AI AI ML 52

Precisely

OCTOBER 10, 2024

When considering data democratization, business leaders need to clearly understand downstream compliance implications. Concerns may also arise around duplication of effort and unintentional misuse of data. In other words, if every department is doing its own work around data analysis, some of that work may be redundant.

Data Quality 52

Data Quality Data Governance Analytics Analytics 52