Data
1. Data
In this lesson, we will learn the areas to think about when it comes to the data for a POC.2. Importance of data
Without data, there aren't any patterns to learn from, and therefore there can’t be an artificial intelligence about the problem area. Likewise, if the data is incomplete, wrong, or biased, the AI will amplify these problems. The AI solution is therefore only as good as the data that it is built from.3. The four areas for data
Four areas to think about when it comes to the data for a POC are - acquisition,4. The four areas for data
volume,5. The four areas for data
quality,6. The four areas for data
and security.7. Acquisition
To solve a business problem, data about it is needed. This could include customer demographics, sales figures, product inventory, marketing campaign performance, and more. For instance, if a business aims to optimize its supply chain, it would need data on procurement, transportation, and inventory levels to identify bottlenecks and improve efficiency. As another example, AI for image classification solutions to identify mechanical failures will require thousands of images labeled as "failed" or not.8. Acquisition
In some cases, businesses may already possess the necessary first-party data, or data from the business. Sometimes, additional data will be required - data from third-party sources. One option is to explore first-party data further and potentially get creative with how and what is captured. Other groups outside of an immediate business group may have access to the type of data needed. If not, a new data strategy may be required and will take time to acquire the needed data. Another option is to utilize third-party data providers who specialize in collecting and aggregating data from diverse sources. Likewise, a business can partner with another to share complementary datasets.9. Volume
An AI solution requires a lot of data. The exact amount of data will depend on the size and complexity of the underlying model. The more complex - for example, the more categories of images that need to be classified - the more data is needed. A Machine Learning scientist can help determine the volume requirements.10. Quality
Once data sources are identified, it's important to understand their quality. As any chef will say - if using bad ingredients, it will be a bad meal. AI solutions are not any different.11. Quality
The quality of the data is crucial in ensuring the reliability and accuracy of the AI solution and therefore, the decision-making process. Data quality can be measured across several dimensions, including completeness - ensuring all the necessary information is available;12. Quality
accuracy - verifying the data reflects the real-world events it represents;13. Quality
relevance - the data is applicable to the problem;14. Quality
and timeliness - the data is up-to-date or about the desired time frame.15. Quality - data bias
Another factor of quality is data bias. This is the systemic errors or prejudices in the data that can lead to inaccurate or unfair outcomes. There are tools and methods for recognizing and evaluating biases in data. This is essential to preventing skewed outcomes and biased decision-making, and plays a vital role in building responsible AI solutions.16. Security
Data protection and security is crucial to maintain customer trust, comply with regulations, and safeguard business interests. Measures, such as removing personal identifiers and retention policies, must be in place to protect sensitive customer data. Likewise, encryption and access control for the storage and other infrastructure components are necessary to protect from unauthorized access and breaches. Some sort of security should be in place at all stages of AI implementation.17. Let's practice!
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