Ontotext

SEPTEMBER 15, 2020

Consider using data catalogs for this purpose. Clean data to ensure data quality. Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Several factors are driving the adoption of knowledge graphs. Linked Data, subscriptions, purchased datasets, etc.).

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

DECEMBER 1, 2023

In an engaging narrative built on the premise that most organizations are not ready for a knowledge graph, Lance talked about the usual pitfalls when building such a solution. According to him, “failing to ensure data quality in capturing and structuring knowledge, turns any knowledge graph into a piece of abstract art”.

Metadata 78

Metadata Sales Consulting Enterprise 78

Ontotext

FEBRUARY 19, 2020

Clean your data to ensure data quality. Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc. Choose your data storage.

Modeling 90

Modeling Knowledge Discovery Data Quality Visualization 90

Ontotext

JULY 29, 2021

We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. Providing a formal unified conceptual model, ontologies enable unified access to and correct interpretation of diverse information and greatly facilitate analytics, decision making and knowledge re-use.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Domino Data Lab

JUNE 15, 2021

This is the core functionality of the Domino’s Enterprise MLOps platform – a system that enables fast, reproducible, and collaborative work on data products like models, dashboards, and data pipelines. Existing preprocessing, data ingestion, and data quality processes can be converted from Java/Spark into Java UDFs.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69