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Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Predictive analytics is the use of techniques such as statistical modeling, forecasting, and machine learning to make predictions about future outcomes.
More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Data analytics and data science are closely related.
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.
Most companies find themselves in the bottom left corner, in the Descriptive Analytics and DiagnosticAnalytics sections. You likely already have some form of scheduled reports, are drilling down into your data, discovering what is in your data, and may even be visualizing to some extent. Do you want to be more efficient?
Think your customers will pay more for data visualizations in your application? But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics. Five years ago they may have.
Artificial Intelligence Analytics. AI can be applies to all 3 major types of analytics: Descriptive Analytics: The entire journey of the descriptive and diagnosticanalytics process includes data extraction, data aggregation and data mining; 3 applications where AI is widely used to reduce costs, and eliminate complex actions.
Data analysts leverage four key types of analytics in their work: Prescriptiveanalytics: Advising on optimal actions in specific scenarios. Diagnosticanalytics: Uncovering the reasons behind specific occurrences through pattern analysis.
Bottom line is that analytics has migrated from a trendy feature to a got-to-have. Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning. It’s all about context.
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