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A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration
- Data integration projects before starting with Data Science and Machine Learning (DS/ML) may not be ideal, as integrating data without knowing its use can lead to unfit data for ML use cases.
- It is suggested to integrate data on a use-case-per-use-case basis by working backwards to identify the required data, optimizing value for money in integration efforts.
- Drivers for premature data integration include difficulty in identifying AI/ML use cases due to unknown data availability, but this can be better solved by communication and dialogue within teams.
- Integrating data without clarity on the ML use case may result in unnecessary data integration leading to increased cost and storage of unused data.
- Cultural barriers to data sharing can be better addressed by involving relevant team members in projects and fostering communication rather than mandating data integration.
- Setting up a data platform strategy and creating a catalog of dataset descriptions for search can be a cost-effective data discovery method for ML projects.
- Data integration should focus on necessities for each use case, and solving organizational, political, and technical challenges prior to ML projects can help tackle data access issues.
- In summary, tackling data integration by prioritizing use-case-based integration, fostering communication among teams, and utilizing low-cost data discovery tools can enhance the success of ML projects.
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