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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward. Business analytics techniques.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

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.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Today, the most common usage of business intelligence is for the production of descriptive analytics. . Descriptive Analytics: Valuable but limited insights into historical behavior. The vast majority of financial services companies use the data within their applications for what is called “ Descriptive Analytics.”

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What Is The Difference Between Business Intelligence And Analytics?

datapine

While BI tells you what has happened in the past and what is happening now (descriptive analytics), BA tells you what will happen in the future (predictive analytics). Descriptive analytics : As its name suggests, this analysis method is used to describe and summarize the main characteristics found on a dataset.

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way. Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was.

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What Is Embedded Analytics?

Jet Global

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.

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An Interview with a Data Scientist

Grooper

For simple reporting projects, I might spend 8 hours getting the right data and then just a couple of hours producing the needed visualizations. What are the metrics that business wants to see and why it is valuable? We plot visualizations and look at the data to understand how occurrences of different categories of data columns.