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How Anomalo solves unstructured data quality issues to deliver trusted assets for AI with AWS

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As a result, the competitive edge is shifting toward data access and data quality. Transforming unstructured files, maintaining compliance, and mitigating data quality issues all become critical hurdles when an organization moves from AI pilots to production deployments.

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Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

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For example, in the bank marketing use case, the management account would be responsible for setting up the organizational structure for the bank’s data and analytics teams, provisioning separate accounts for data governance, data lakes, and data science teams, and maintaining compliance with relevant financial regulations.

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Data mesh

Dataconomy

The origin of data mesh The concept of data mesh was introduced by Zhamak Dehghani at Thoughtworks in 2019. It emerged as a response to the limitations of traditional centralized data systems, such as data lakes and warehouses, which often became bottlenecks in data management.

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Data Integrity for AI: What’s Old is New Again

Precisely

Each source system had their own proprietary rules and standards around data capture and maintenance, so when trying to bring different versions of similar data together such as customer, address, product, or financial data, for example there was no clear way to reconcile these discrepancies. A data lake!

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Evaluating Data Lakes vs. Data Warehouses

Dataversity

While data lakes and data warehouses are both important Data Management tools, they serve very different purposes. If you’re trying to determine whether you need a data lake, a data warehouse, or possibly even both, you’ll want to understand the functionality of each tool and their differences.

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Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach

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Enterprisesespecially in the insurance industryface increasing challenges in processing vast amounts of unstructured data from diverse formats, including PDFs, spreadsheets, images, videos, and audio files. All contain critical information across the claims processing lifecycle.