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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes.

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Five Steps for Building a Successful BI Strategy

Sisense

And every business – regardless of the industry, product, or service – should have a data analytics tool driving their business. Most companies find themselves in the bottom left corner, in the Descriptive Analytics and Diagnostic Analytics sections. What is the market segment we should focus on?

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Our BI Best Practices demystify the analytics world and empower you with actionable how-to guidance. 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: painting a picture of your data.

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Understanding BI Tools in Today’s Market

Smarten

Without business intelligence, the enterprise does not have an objective understanding of what works, what does not work, and how, when and where to make changes to adapt to the market, its customers and its competition. This approach typically focuses on descriptive analytics based on historical data to answer the question “What happened?”

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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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Incorporating Artificial Intelligence for Businesses : The Modern Approach to Data Analytics

BizAcuity

By 2025, AI will be the top category driving infrastructure decisions, due to the maturation of the AI market, resulting in a tenfold growth in compute requirements. 85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams.(Source: AI in Marketing. Source: Gartner Research).

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. It’s worth noting that there is a landscape of proprietary tools dedicated to producing descriptive analytics in the name of business intelligence. Data visualization blog posts are a dime a dozen.