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Table of Contents
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the following is a revised edition.
Generative AI, as well as other AI applications, with their remarkable capabilities, have become a driving force across various industries. From streamlining operations to powering creative solutions, its applications are limitless. The sheer volume of data tools available may seem overwhelming, but they bring unprecedented opportunities. However, innovation remains at the core of technological growth, and organizations must adapt to evolving demands and changing landscapes.
In this article, we'll explore how to optimize your AI experience through effective organizational practices and the integration of Product Thinking in data. Let's delve into the key strategies to harness the full potential of generative AI.
This principle applies to GenAI models just as much as it does to any other type of machine learning model. If you feed your model poor quality data, it will learn the wrong patterns and generate poor quality outputs, no matter how many optimizations you use or state-of-the-art models you deploy.
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That's why it's important to use high-quality data to train your AI. This means organising the data, ensuring quality and governance, cleaning, debugging, and formatting it in a way that is compatible with the AI model.
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π‘ AI, in most fundamental terms, is an equation. In other words, itβs just a channel to process your data and deduce information from it.
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Data as a product principle is designed to address the data quality and age-old data silos problem; or as Gartner calls it dark data - βthe information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposesβ. Analytical data provided by the domains must be treated as a product, and the consumers of that data should be treated as customers - happy and delighted customers.
~ Data Mesh Principles
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Related Post : β‘οΈThe Data Product Strategy | Becoming Metrics-First
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Why spend time understanding Data Products? Data is at the forefront of any data-related challenge, and understanding the context is paramount. Applications that depend on data, be it simple machine learning models or complex GenAI apps, are essentially fancy layers which feed on the data underneath.
For the same reason, many enterprises experience poor ROI from their AI counterparts due to a consistent denial of poor data they own. Here's why a product approach for data makes sense:
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Related Post: β‘οΈ Optimizing Data Modeling for the Data-First Stack
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By following these best practices, your organization can optimize the use and implementation of generative AI, stay ahead in technology, and unlock innovative possibilities in a rapidly evolving landscape.
Follow for more on LinkedIn and Twitter to get the latest updates on what's buzzing in the modern data space.
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Feel free to reach out to us to become a contributor or reply with your feedback/queries regarding modern data landscapes. We look forward to your much-valued input to the Editor.
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