AWS Big Data
FEBRUARY 28, 2023
Solution overview Online Analytical Processing (OLAP) is an effective tool for today’s data and business analysts. It helps you see your mission-critical metrics at different aggregation levels in a single pane of glass. This will allow for a smoother migration of OLAP workloads, with minimal rewrites.
Smart Data Collective
OCTOBER 25, 2023
The market for business intelligence technology is projected to exceed $35 billion by 2028. One of them is by helping them improve their social media marketing strategies. There are many ways that it can help with social media marketing. In the 1990s, OLAP tools allowed multidimensional data analysis.
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CIO Business Intelligence
JANUARY 20, 2023
Increased competitive advantage: A sound BI strategy can help businesses monitor their changing market and anticipate customer needs. The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs.
Octopai
AUGUST 30, 2020
In terms of system-to-system lineage or horizontal lineage , we’ve implemented enhancements that enable users to get the most expansive and complete lineage on the market today. Microstrategy coverage enhancements: Reports Data sets Metrics Filters Facts Attributes Schemas Dossiers. What else, you ask?
Jet Global
DECEMBER 2, 2019
As we will outline below when discussing the technical execution differences between reporting and BI, with business intelligence, it’s possible (and required) to universally define goals and performance equations through KPIs and metrics that are calculated in the BI environment indefinitely.
Sisense
AUGUST 11, 2019
BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics. But on the whole, BI is more concerned with the whats and the hows than the whys.
Jet Global
MAY 1, 2023
Section 2: Embedded Analytics: No Longer a Want but a Need Section 3: How to be Successful with Embedded Analytics Section 4: Embedded Analytics: Build versus Buy Section 5: Evaluating an Embedded Analytics Solution Section 6: Go-to-Market Best Practices Section 7: The Future of Embedded Analytics Section 1: What are Embedded Analytics?
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