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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics. The types of data analytics Predictive analytics: Predictive analytics helps to identify trends, correlations and causation within one or more datasets.

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What Is The Difference Between Business Intelligence And Analytics?

datapine

On the other hand, BA is concerned with more advanced applications such as predictive analytics and statistic modeling. By using Business Intelligence and Analytics (ABI) tools, companies can extract the full potential out of their analytical efforts and make improved decisions based on facts.

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Improve Underwriting Using Data and Analytics

Cloudera

The next step leads to performing exploratory, descriptive analytics, “why is this happening,” and so on. Finally, the end goal is to enable proactive, predictive analytics — “what if” — using applied ML and AI to better predict what will happen and recommend actions to prevent or manage activities as necessary.

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AIOps reimagines hybrid multicloud platform operations

IBM Big Data Hub

The AIOps engine is focused on addressing four key things: Descriptive analytics to show what happened in an environment. Predictive analytics to show what will happen next. Prescriptive analytics to show how to achieve or prevent the prediction. Diagnostics to show why it happened.

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

BizAcuity

you already have a data strategy in place, then it is easier to identify and analyze where AI would be the most useful for your business.Analytics Insight has an informative blog on the wide range of use-cases of AI in prominent industries. Artificial Intelligence Analytics. Worth a read if you are brainstorming on AI strategy.

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

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. 5) Find improvement opportunities through predictions. The responsibility to take action still lies in the hands of the executives. 6) Smart and faster reporting.

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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. Analytics acts as the source for data visualization and contributes to the health of any organization by identifying underlying models and patterns and predicting needs. Visualizations: past, present, and future.