IBM Big Data Hub
SEPTEMBER 19, 2023
Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.
Sisense
SEPTEMBER 12, 2019
Every business needs a business intelligence strategy to take it forward. . As the Global Team Lead of BI Consultants at Sisense, I can say that the projects I’ve worked on where a BI strategy was involved, were more successful than projects without a strategy. But what is a BI strategy in today’s world?
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Smart Data Collective
NOVEMBER 24, 2020
Prescriptive Analytics. In the future of business intelligence, it will also be more common to break data-based forecasts into actionable steps to achieve the best strategy of business development. Unique feature: custom visualizations to fit your business needs better. This shows why self-service BI is on the rise.
FineReport
MARCH 24, 2024
Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptive analytics: Assessing historical trends, such as sales and revenue.
Sisense
JUNE 30, 2020
In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. Data visualization and visual analytics are two terms that come up a lot when new and experienced analytics users alike delve into the world of data in their quest to make smarter decisions.
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.
AWS Big Data
MARCH 26, 2024
In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.
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