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 104

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 104

AWS Big Data

FEBRUARY 1, 2023

AWS Glue for ETL To meet customer demand while supporting the scale of new businesses’ data sources, it was critical for us to have a high degree of agility, scalability, and responsiveness in querying various data sources. Clients access this data store with an API’s.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Jet Global

OCTOBER 4, 2023

Finance teams are under pressure to slash costs while playing a key role in data strategy, yet they are still bogged down by manual tasks, overreliance on IT, and low visibility on company data. Addressing these challenges often requires investing in data integration solutions or third-party data integration tools.

Finance 52

Finance Reporting Cost-Benefit Management 52

Ontotext

MARCH 8, 2023

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. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

Data Quality 85

Data Quality Data Warehouse Data-driven Reporting 85

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

IBM Big Data Hub

AUGUST 4, 2023

Data fabric Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. By recognizing data as a product, it creates greater incentive to properly manage data.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62