Cracking the Code: Success in Internal AI Projects

Why Data Quality is fundamental for Achieving Success?

Today we will be discussing the lifecycle of Internal AI projects and the factors that contribute to their success or failure, whether they are internal or external projects. It is important to note that certain factors must be in place within an organization to ensure that an AI project is successful, such as having staff expertise and experience.

One crucial factor that can determine the success of an AI project is the quality of the data being used. It is essential to collect as much data as possible, particularly data related to the problem being solved. For example, if an organization is trying to solve a customer retention problem, it will need to know a lot about its customers and find ways to measure those things. Having complete data is crucial because having only a little bit of signal will not inform the AI well enough to solve the problem. In addition to hard facts, inferred facts or metadata, which provide context to the AI, are also important to inform the AI model.

How can we organize the necessary data for our Internal AI Projects?

It is also necessary to gather all the data in all the sequences and times an opportunity to describe the environment and customer has been available to avoid missing data. Missing data can lead to the observation of certain things being missed, not saved, or cataloged, resulting in an incomplete picture, which is essentially a needle in a haystack. To ensure that data is usable down the line, it is essential to have clean data, which means the data must be free of weird noise and bad signals. In addition, data must be labeled well, particularly if the AI project is an inference or classification workload. It is crucial to have good examples of what a good outcome is and how to demonstrate it to the AI model.

Data Silo: How to Overcome

Another challenge in working with data in large organizations is that each department has their own data silo, which is not communicated to other departments. This is due to security and sometimes ego, which leads to incomplete and missing data. Additionally, data sets are duplicated across different departments, resulting in wasted effort, time, and money. Duplicating the AI model is also a significant issue, which can be avoided if departments work together to create a unified data set.

Conclusion

In conclusion, ensuring the success of an AI project is challenging, and organizations need to ensure that data quality is high, complete, and labeled well. Collaborating across departments to create a unified data set is also crucial to avoid duplicating effort, time, and money. With these strategies in place, organizations can avoid falling into the trap of having an AI project that does not meet its desired outcome.