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. Maximize the usability of your data.

Modeling 90

Modeling Knowledge Discovery Data Quality Visualization 90

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

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Linked Data, subscriptions, purchased datasets, etc.).

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 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 Machine Learning 78

Ontotext

JULY 29, 2021

We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. Ontotext was founded in 2000 with the Semantic Web in its genes and we had the chance to be part of the community of its pioneers. We can’t imagine looking at the Semantic Web as an artifact.

Enterprise 124

Enterprise Metadata Knowledge Discovery Modeling 124

Domino Data Lab

JUNE 15, 2021

Advanced data wrangling and preprocessing pipelines A Java UDF can use a wider range of techniques for data cleansing, feature engineering, and mode advanced preprocessing, compared to what is available in SQL. 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

Ontotext

AUGUST 21, 2024

Capturing data, converting it into the right insights, and integrating those insights quickly and efficiently into business decisions and processes is generating a significant competitive advantage for those who do it right.

Knowledge Discovery 52

Knowledge Discovery Cost-Benefit Data-driven Metadata 52