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From project to product: Architecting the future of enterprise technology

CIO Business Intelligence

Documentation and diagrams transform abstract discussions into something tangible. By articulating fitness functions automated tests tied to specific quality attributes like reliability, security or performance teams can visualize and measure system qualities that align with business goals.

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Why Data Driven Decision Making is Your Path To Business Success

datapine

By implementing the right reporting tools and understanding how to analyze as well as to measure your data accurately, you will be able to make the kind of data driven decisions that will drive your business forward. What Is Data Driven Decision Making? Quantitative data analysis focuses on numbers and statistics.

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A Guide To The Methods, Benefits & Problems of The Interpretation of Data

datapine

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. For a more in-depth review of scales of measurement, read our article on data analysis questions.

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How Block is accelerating engineering velocity through developer experience

CIO Business Intelligence

This article goes behind the scenes on whats fueling Blocks investment in developer experience, key initiatives including the role of an engineering intelligence platform , and how the company measures and drives success. These select choices can then be of high quality, well-supported, documented, maintained, secure, and reliable.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. A planned BI strategy will point your business in the right direction to meet its goals by making strategic decisions based on real-time data.

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

DataKitchen

We’ve got siloed expertise that would make medieval castle builders proud, documentation so sparse it could win a minimalist art competition, and a reliance on “data heroes” that would make Marvel envious. Loss of Trust: As errors persist, stakeholders lose confidence in the data and the team responsible for it.

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The quest for high-quality data

O'Reilly on Data

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Alex Ratner on “Creating large training data sets quickly”.