CIO Business Intelligence

FEBRUARY 16, 2023

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

Unstructured Data 100

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 100

Alation

OCTOBER 28, 2021

Storing the same data in multiple places can lead to: Human error: mistakes when transcribing data reduce its quality and integrity. Multiple data structures: different departments use distinct technologies and data structures. Data governance is the solution to these challenges.

Data Governance 52

Data Governance Measurement Metrics Data Quality 52

AWS Big Data

FEBRUARY 22, 2023

Data analytic challenges As an ecommerce company, Ruparupa produces a lot of data from their ecommerce website, their inventory systems, and distribution and finance applications. The data can be structured data from existing systems, and can also be unstructured or semi-structured data from their customer interactions.

Data Lake 52

Data Lake Dashboards Cost-Benefit Metadata 52

Ontotext

APRIL 25, 2019

The SPARQL query is a way to search, access and retrieve structured data by pulling together information from diverse data sources. The SPARQL query language, designed and endorsed by the W3C, is the standard for querying data, stored in RDF or mapped to RDF. Normalizing data values (if needed).

Cost-Benefit 75

Cost-Benefit Enterprise Structured Data Data Architecture 75

Ontotext

APRIL 25, 2019

The SPARQL query is a way to search, access and retrieve structured data by pulling together information from diverse data sources. The SPARQL query language, designed and endorsed by the W3C, is the standard for querying data, stored in RDF or mapped to RDF. Normalizing data values (if needed).

Cost-Benefit 40

Cost-Benefit Enterprise Structured Data Data Architecture 40

O'Reilly on Data

DECEMBER 19, 2018

If you suddenly see unexpected patterns in your social data, that may mean adversaries are attempting to poison your data sources. Anomaly detection may have originated in finance, but it is becoming a part of every data scientist’s toolkit. Automating model building is just one component of automating machine learning.

Machine Learning 266

Machine Learning Software Metadata Testing 266

Jet Global

MAY 9, 2024

Batch processing pipelines are designed to decrease workloads by handling large volumes of data efficiently and can be useful for tasks such as data transformation, data aggregation, data integration , and data loading into a destination system. What is the difference between ETL and data pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52