AI Performance Paradox: why the true measure of AI’s value is an organization’s ability to adapt
AI improves individual tasks but rarely company-wide results. See why CIOs should add adaptability to cost metrics and which five KPIs help measure it.
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To get interesting insights from your data, you need to collect data from many different sources and combine them into a cohesive dataset. Subsequent analysis may uncover correlations and relationships between parameters that are seemingly distant from each other.
Missing information, incorrect data, or values in different formats or units can lead to incorrect conclusions or will obscure the picture. To prevent such situations, perform a data cleanup with proper care beforehand. This process needs to be repeated each time new data are added to the dataset. Always consider removing low quality data from the data set, than putting as much as possible, as this approach usually gives better results.
With a good quality data set, you can start analyzing them. Define the hypotheses you want to confirm with data analysis. Perform data mining to uncover hidden structures and trends. Experiment with different AI/ML algorithms and models. Measure the accuracy used models to select the best final solution.
Use your domain knowledge to interpret the obtained results. Turn the insights you gain into business decisions that optimize the operation of the organization and deliver new value for customers.
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Explore our ServicesAI improves individual tasks but rarely company-wide results. See why CIOs should add adaptability to cost metrics and which five KPIs help measure it.
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