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Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis

DataKitchen

Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It’s that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You’ve got yourself a recipe for data disaster.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science.

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The quest for high-quality data

O'Reilly on Data

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

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Why Data Driven Decision Making is Your Path To Business Success

datapine

3) Gather data now. Gathering the right data is as crucial as asking the right questions. For smaller businesses or start-ups, data collection should begin on day one. Once it is identified, check if you already have this data collected internally, or if you need to set up a way to collect it or acquire it externally.

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What you need to know about product management for AI

O'Reilly on Data

For example, if engineers are training a neural network, then this data teaches the network to approximate a function that behaves similarly to the pairs they pass through it. That foundation means that you have already shifted the culture and data infrastructure of your company. If you can’t walk, you’re unlikely to run.

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How Data Cleansing Can Make or Break Your Business Analytics

Smart Data Collective

This market is growing as more businesses discover the benefits of investing in big data to grow their businesses. One of the biggest issues pertains to data quality. Even the most sophisticated big data tools can’t make up for this problem. Data cleansing and its purpose. Tips for successful data cleansing.

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Lilly revolutionizes clinical trials with intelligent sensor cloud

CIO Business Intelligence

Defined as quantifiable and objective behavioral and physiological data collected and measured by digital devices such as implantables, wearables, ingestibles, or portables, digital biomarkers enable pharmaceutical companies to conduct studies remotely without the need for a physical site.