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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 2) Data Discovery/Visualization. We all gained access to the cloud.

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

DataKitchen

There are multiple locations where problems can happen in a data and analytic system. What is Data in Use? Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes.

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Why you should care about debugging machine learning models

O'Reilly on Data

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Small residuals usually mean a model is right, and large residuals usually mean a model is wrong. Interpretable ML models and explainable ML.

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How Can BI Consulting Services Help Foster Data-driven Decisions

BizAcuity

This strategic approach enables organizations to prioritize data projects that support their key goals, whether they aim to improve customer experience, reduce costs, or expand into new markets. By aligning the data strategy with business needs, companies can focus their resources on initiatives that yield the most value.

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Unveiling the Top 10 Data Visualization Companies of 2024

FineReport

In 2024, data visualization companies play a pivotal role in transforming complex data into captivating narratives. This blog provides an insightful exploration of the leading entities shaping the data visualization landscape. Market Impact The impact a company has on the market speaks volumes about its success.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics and data science are closely related. Data analytics is a component of data science, used to understand what an organization’s data looks like. Generally, the output of data analytics are reports and visualizations. Data science takes the output of analytics to study and solve problems.

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Case Study – Augmented Analytics Solution for India Powertrain and Sustainable Solutions Engineering Company

Smarten

Enhanced dashboards and interactive visualizations enabled real-time performance monitoring, and streamlined workflows, and identified performance gaps, while ensuring data integrity and consistency across all divisions and operations. Download the Case study