Data Warehouse Data-driven Forecasting Slice and Dice 
IBM Big Data Hub
OCTOBER 27, 2022
Left to their own devices, they had resorted to using legacy reporting tools such as Excel that required manual gathering, slicing and dicing of data. Consequently, this data was siloed, unshareable, hard to use, lacked quality and governance controls, and could not be used in automated processes.
IBM Big Data Hub
DECEMBER 7, 2023
Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.
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Sisense
JANUARY 6, 2020
Best practice blends the application of advanced data models with the experience, intuition and knowledge of sales management, to deeply understand the sales pipeline. For this partnership to work, it requires sales leaders who really care about data and are open to analysts’ advice about how to use the Salesforce data they generate.
Jet Global
MARCH 20, 2024
In today’s dynamic business landscape, data is king. Your finance team is under pressure to make data-driven decisions that optimize financial health and fuel strategic growth. This creates a queue and delays in accessing critical data. But a hidden roadblock can impede your progress: report generation.
Jet Global
MAY 12, 2023
Analytics is vital now because providing end-users with the ability to analyze, slice, and dice data within the context of their application is essential to staying competitive in today’s fast-paced digital world. Product Analytics Embedded Analytics What data does it provide?
Jet Global
DECEMBER 7, 2021
The capacity to facilitate exploration differentiates business intelligence, allowing users to quickly and easily slice and dice their data in various ways to produce meaningful insights that direct leaders toward better business decisions. Phase 2: Gathering and Organizing Data.
Jet Global
MAY 1, 2023
By leveraging data analysis to solve high-value business problems, they will become more efficient. This is in contrast to traditional BI, which extracts insight from data outside of the app. that gathers data from many sources. These tools prep that data for analysis and then provide reporting on it from a central viewpoint.
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