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Must-Have AI Features for Your App

Sisense

In the case of a stock trading AI, for example, product managers are now aware that the data required for the AI algorithm must include human emotion training data for sentiment analysis. Artificial intelligence is transforming products in surprising and ingenious ways.

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Understanding Data Loss Prevention (DLP)

GAVS Technology

Human Error: Mistakes such as accidental data sharing or configuration errors that unintentionally expose data, requiring corrective actions to mitigate impacts. Data Theft: Unauthorized acquisition of sensitive information through physical theft (e.g., stolen devices) or digital theft (hacking into systems).

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Fundamentals of Data Analytics

The BAWorld

As the volume and complexity of data increase, DA will become increasingly important in managing the digital age’s difficulties and opportunities. A retailer, for example, can examine sales data, customer feedback, and marketing campaign data to determine why sales fell in a specific month.

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Deep Dive into Predictive Analytics Models and Algorithms

Marutitech

Simply put, predictive analytics is predicting future events and behavior using old data. Predicting future events gives organizations the advantage to understand their customers and their business with a better approach. What is predictive data modeling? What is Predictive Data Modeling?

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GA4 vs Universal Analytics: Move your Data Today

Astera

Create a visual representation best suited to your data requirements to deliver insights to stakeholders effectively. Collaboration : Easily share custom-built reports with team members and stakeholders to make informed, data-driven decisions. Track up to 30 conversions: You can track up to 30 conversion events.

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What is Streaming ETL?

Astera

Streaming ETL is a modern approach to extracting, transforming, and loading (ETL) that processes and moves data from source to destination in real-time. It relies on real-time data pipelines that process events as they occur. Events refer to various individual pieces of information within the data stream.

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DNV Illuminates the Utilities Industry with Insights and Data

Sisense

In order to do this, my team uses data to identify problem areas and potential issues for our customers (ideally before they happen). This presented the first challenge for our product team in building Cascade Insight: What is the data that is most important to capture?