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 105

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 105

IBM Big Data Hub

APRIL 25, 2023

Relational databases were adapted to accommodate the demands of new workloads, such as the data engineering tasks associated with structured and semi-structured data, and for building machine learning models. To effectively use raw data, it often needs to be curated within a data warehouse.

Optimization 57

Optimization Strategy Data Warehouse Cost-Benefit 57

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. As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Sisense

MAY 11, 2021

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021! Discover why.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

JUNE 21, 2023

A typical ask for this data may be to identify sales trends as well as sales growth on a yearly, monthly, or even daily basis. A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. This is achieved by partitioning the data.

Data Warehouse 76

Data Warehouse Data Lake OLAP Cost-Benefit 76