Project managers have a significant opportunity to increase the value they bring to an organization by embracing AI. This is especially relevant to data management. Making a machine learning algorithm effective requires determining the best set of project features to use as input. Data scientists are now a scarce resource. Yet, it is doubtful that a data scientist is prepared to understand the nuances of project data.
Here are four practical steps for successful data management when using AI. These steps are designed to be straightforward and manageable, empowering project managers to take control of their data.
- Determine what project data is available, how it can be accessed, and the format for the files being accessed.
- Understand how to perform data wrangling. Ensure there is structured project data. This means consistency in the format, and resolving any anomalies, such as blank data fields, multiple entries for a single field, and inconsistent meaning for entries within a field.
- Become familiar with feature engineering. Determine how to manage data fields that have the same meaning, add data fields that are missing, and improve the details of data that is captured and used by AI-based software
- Improve your knowledge of basic statistics. Be able to analyze outliers and decide on appropriate action. Understand probability, sample distributions, and causal correlation. You don’t need to be an expert, but basic knowledge is essential.
With increased skills in basic data management, the project manager becomes an integral part of the organization's successful adoption of AI-based tools. Their role becomes significantly more valuable, and their contributions will be recognized as they help navigate the complexities of AI.
Community Champion
