Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

Alation

MAY 24, 2022

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. For instance, via lineage, analysts can understand if upstream data dependencies have reliable data quality.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

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

Supervised learning Supervised learning techniques use real-world input and output data to detect anomalies. These types of anomaly detection systems require a data analyst to label data points as either normal or abnormal to be used as training data.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Precisely

MARCH 2, 2023

Data analysts also use contextual data enrichment to help monitor the spread of infectious diseases. The Unique Power of Location Intelligence Location data is a powerful way to add context. The net result is difficulty in delivering trusted insights and driving meaningful, measurable business value.

Data Quality 52

Data Quality Data Analyst Data Observability Data Governance 52

IBM Journey to AI blog

JULY 13, 2023

Data quality monitoring Maintaining good data quality requires continuous data quality management. Data quality monitoring is the practice of revisiting previously scored datasets and reevaluating them based on the six dimensions of data quality.

Data Quality 74

Data Quality Data Profiling Data Governance Analytics 74

IBM Journey to AI blog

DECEMBER 16, 2022

It seamlessly integrates with IBM’s data integration, data observability, and data virtualization products as well as with other IBM technologies that analysts and data scientists use to create business intelligence reports, conduct analyses and build AI models.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52