Remove Data Quality Remove Measurement Remove Risk Management Remove Strategy
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Building a Data Strategy for Defence Partners

Alation

Data gathering and use pervades almost every business function these days — and it’s widely acknowledged that businesses with a clear strategy around data are best placed to succeed in competitive, challenging markets such as defence. What is a data strategy? Why is a data strategy important?

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.” Such a framework provides your organization with a holistic approach to collecting, managing, securing, and storing data.

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Best BI Tools Examples for 2024: Business Intelligence Software

FineReport

Additionally, BI tools enable organizations to adopt a data-driven approach to strategy formulation, leading to more informed decision-making at all levels. The implementation empowered the organization with predictive modeling capabilities, enabling proactive risk mitigation and personalized customer engagement strategies.

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4 Steps to Data-first Modernization

CIO Business Intelligence

The data-first transformation journey can appear to be a lengthy one, but it’s possible to break it down into steps that are easier to digest and can help speed you along the pathway to achieving a modern, data-first organization. Key features of data-first leaders. 5x more likely to be highly resilient in terms of data loss.

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Best Practices for Data Catalog Implementation

Octopai

For business users Data Catalogs offer a number of benefits such as better decision-making; data catalogs provide business users with quick and easy access to high-quality data. This availability of accurate and timely data enables business users to make informed decisions, improving overall business strategies.

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Data augmentation.

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Data Governance Program: Ensuring a Successful Delivery

Alation

Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data? Enterprise risk management.