Remove Predictive Modeling Remove Risk Remove Visualization
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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

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Why you should care about debugging machine learning models

O'Reilly on Data

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. That’s where model debugging comes in. Interpretable ML models and explainable ML.

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

This proactive approach to data quality guarantees that downstream analytics and business decisions are based on reliable, high-quality data, thereby mitigating the risks associated with poor data quality. The fourth pillar focuses on testing the results of data models, visualizations, and other applications to validate data in use.

Testing 173
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How Big Data Impacts The Finance And Banking Industries

Smart Data Collective

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. Big Data provides financial and banking organizations with better risk coverage.

Big Data 145
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What Is The Difference Between Business Intelligence And Analytics?

datapine

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. Usage in a business context. Let’s see this through an example.

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How Can BI Consulting Services Help Foster Data-driven Decisions

BizAcuity

Making decisions based on data, rather than intuition alone, brings benefits such as increased accuracy, reduced risks, and deeper customer insights. Advanced analytics and predictive modeling are core offerings of BI consulting services, enabling organizations to move from descriptive reporting to proactive decision-making.