Business Analytics Data Warehouse Enterprise Reporting 
DataKitchen
JANUARY 3, 2022
Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. In business analytics, fire-fighting and stress are common.
CIO Business Intelligence
OCTOBER 26, 2023
This is done by mining complex data using BI software and tools , comparing data to competitors and industry trends, and creating visualizations that communicate findings to others in the organization. Other senior positions may require an MBA, but there are plenty of BI jobs that require only an undergraduate degree.
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Cloudera
OCTOBER 4, 2023
Flexible use of compute resources on analytics — which is even more important as we start performing multiple different types of analytics, some critical to daily operations and some more exploratory and experimental in nature, and we don’t want to have resource demands collide. Kudu has this covered.
Jet Global
FEBRUARY 5, 2019
Are you making decisions based on bad data? It’s hard to answer that question because, truth be told, you don’t know you’re using bad data until it’s too late. . states that about 40 percent of enterprise data is either inaccurate, incomplete, or unavailable. Gartner study ?states Download Now.
Cloudera
MAY 12, 2021
This category is open to organizations that have tackled transformative business use cases by connecting multiple parts of the data lifecycle to enrich, report, serve, and predict. . DATA FOR ENTERPRISE AI. DATA FOR GOOD. SECURITY AND GOVERNANCE LEADERSHIP.
Peter James Thomas
AUGUST 18, 2018
That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Overlapping with the above, from around 2012, I began to get involved in also designing and implementing Big Data Architectures; initially for narrow purposes and later Data Lakes spanning entire enterprises. In Closing.
Sisense
MAY 28, 2020
As enterprises demand data infrastructures that can meet this growth in real-time data — and ultimately assist with their product differentiation strategy — the pressure put on product teams is huge. Product teams are already having to manage the growing complexities that come with modern data environments.
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