To ensure data is fit for AI usage, the author suggests investing in data quality early in the process. This includes tidying the data by identifying and correcting errors, integrating complex data from different sources, enforcing data governance rules, eliminating biases, and regularly updating the data. Before integrating AI, businesses should clean, standardize, and organize their data. This will provide a solid base for success and ensure AI provides useful insights and outcomes.
Key takeaways:
- AI systems are data-hungry and require accurate, clean, and comprehensive data to function effectively. Inaccurate or incomplete data can lead to poor or erroneous outcomes.
- AI lacks the ability to set objectives on its own and relies on data for guidance. Therefore, for AI to provide valid results, it must draw on accurate and relevant data.
- AI cannot fix bad data, in fact, it can make it worse. If there are inaccuracies or biases within the data, AI will depend on these flaws, disrupting operations.
- Before integrating AI into business practices, it's crucial to invest in data quality early in the process, identify and correct errors, eliminate duplication, verify data accuracy, and keep the data freshly updated.