Alation
MARCH 29, 2022
Business intelligence has a long history. How will moving to true self-service business intelligence — and achieving data democracy — impact your organization? A Short History of Business Intelligence. Modern business intelligence starts at the dawn of digital computing.
Paul Turley
NOVEMBER 10, 2019
Donald Farmer has been recognized leader in the Business Intelligence and Analytics industry for the past two decades and his thought leadership is even more relevant today. He played a key role in shaping the Microsoft BI toolset as a Principal Program Manager from 2001 when the products we use today were in their infancy.
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CIO Business Intelligence
JANUARY 17, 2024
The company has been on a continuous journey to adapt its internal and external processes to new business needs and opportunities since 2001.” Reporting standardization One of Ipsos’ latest digital transformation-related projects is the move of its reporting and analytics to a standard digital delivery platform.
Cloudera
AUGUST 8, 2022
With Iceberg in CDP, you can benefit from the following key features: CDE and CDW support Apache Iceberg: Run queries in CDE and CDW following Spark ETL and Impala business intelligence patterns, respectively. 8 2001 5967780. We start by creating a Spark 3 virtual cluster (VC) in CDE. 1 2008 7009728. 2 2007 7453215.
CIO Business Intelligence
MAY 13, 2023
Artificial intelligence (AI) in 2023 feels a bit like déjà vu to me. Back in 2001, as I was just entering the venture industry, I remember the typical VC reaction to a start-up pitch was, “Can’t Microsoft replicate your product with 20 people and a few months of effort, given the resources they have?”
CIO Business Intelligence
JUNE 22, 2022
Huawei’s foray into the country began in 2001. The programme is targeted at firms that use Machine Learning & Analytics, IoT, Edge Computing, and Software as a Service (SaaS) applications, leveraging Huawei’s leadership in technology and innovation. .
AWS Big Data
OCTOBER 11, 2023
Organizations with legacy, on-premises, near-real-time analytics solutions typically rely on self-managed relational databases as their data store for analytics workloads. Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities.
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