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Data Mining Technology Helps Online Brands Optimize Their Branding

Smart Data Collective

Data mining technology is one of the most effective ways to do this. By analyzing data and extracting useful insights, brands can make informed decisions to optimize their branding strategies. This article will explore data mining and how it can help online brands with brand optimization. What is Data Mining?

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What is Data Security? |Threats, Risks and Solutions 

Analytics Vidhya

These alarming numbers underscore the need for robust data security measures to protect sensitive information such as personal data, […] The post What is Data Security? Threats, Risks and Solutions appeared first on Analytics Vidhya.

Risk 288
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AI Helps Mitigate These 5 Major Supplier Risks

Smart Data Collective

AI is particularly helpful with managing risks. Many suppliers are finding ways to use AI and data analytics more effectively. How AI Can Help Suppliers Manage Risks Better. Here are some of the risks that organizations face in dealing with suppliers, and what they can do to mitigate those risks with artificial intelligence.

Risk 144
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Ignoring data lifecycle management is putting your business at risk

CIO Business Intelligence

This approach allows enterprises to streamline processes, gather data for specific purposes, get better insights from data in a secure environment, and efficiently share it. 1 A clear picture of where data lives and how it moves enables enterprises to consistently protect this data and its privacy.

Risk 98
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Small Businesses Use Big Data to Offset Risk During Economic Uncertainty

Smart Data Collective

Big data helps businesses address cash flow needs A growing number of companies use big data technology to improve their financing. They can use data mining tools to evaluate the average interest rate of different lenders. Therefore, data-driven pricing may be even more critical during a bad economy.

Big Data 105
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The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below. Baggett, W.C.

Modeling 207
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3 new steps in the data mining process to ensure trustworthy AI

IBM Big Data Hub

To help data scientists reflect and identify possible ethical concerns the standard process for data mining should include 3 additional steps: data risk assessment, model risk assessment and production monitoring. Data risk assessment. Model risk management.