Remove Metrics Remove Slice and Dice Remove Visualization
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Super-charged pivot tables in Amazon QuickSight

AWS Big Data

Additionally, with Amazon QuickSight Q , end-users can simply ask questions in natural language to get machine learning (ML)-powered visual responses to their questions. This involved migrating complex tables and pivot tables, helping them slice and dice large datasets and deliver pixel-perfect views of their data to their stakeholders.

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13 power tips for Microsoft Power BI

CIO Business Intelligence

Power BI is Microsoft’s interactive data visualization and analytics tool for business intelligence (BI). With Power BI, you can pull data from almost any data source and create dashboards that track the metrics you care about the most. Power BI’s rich reports or dashboards can be embedded into reporting portals you already use.

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Know Your Data Ingredients

Juice Analytics

This is an often overlooked step on the rush to visualize data. In an effort to lay a strong foundation for your visualizations, here are three steps to understand and evaluate your data fields before you throw it into the Cuisinart that is your visualization tool. (1) 1) Separate your metrics from your dimensions.

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Data Visualization Inspiration: Analysis To Insights To Action, Faster!

Occam's Razor

Like a vast majority on planet Earth, I love data visualizations. A day-to-day manifestation of this love is on my Google+ or Facebook profiles where 75% of my posts are related to my quick analysis and learnings from a visualization. Data visualized is data understood. Short story #4: Multi-dimensional Slicing and Dicing!

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How Model Observability Provides a 360° View of Models in Production

DataRobot Blog

By tracking service, drift, prediction data, training data, and custom metrics, you can keep your models and predictions relevant in a fast-changing world. Model Observability compounds performance stats and metrics across the entire model lifecycle to provide context to problems that can threaten the integrity of your models.

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Data Storytelling: What's Easy and What's Hard

Juice Analytics

Gathering a collection of visualizations and calling it a data story is easy (and inaccurate). Making it meaningful is so much harder. Making data-driven narrative that influences people.hard. Schedule a demo.

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5 tips for excelling at self-service analytics

CIO Business Intelligence

Having that roadmap from the start helps to trim down and focus on the actual metrics to create. Have a data governance plan as well to validate and keep the metrics clean. As soon as one metric is not accurate it is hard to get the buy-in again, so routinely confirming accuracy on all analytics is extremely important.”

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