AI Data Strategy DD Checklist (External)

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1	Revelry Venture Partners - @RevelryVC
2	Peter Liu - @NewOrleansVC
3	Gerard Ramos - @gerardramos
4	Working on AI? Send us a note on any channel.
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8	Company Name: [Redacted]
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10	DD Checklist - AI Data Strategy	Points	Deal Team Notes / Next Steps
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12	Data Acquisition (hover for description)
13	Data sources: Are data sources diverse, relevant, and reliable? (0-5 points)	4	Uses diverse sources like web scraping, APIs (e.g., Twitter API), and purchased datasets from companies like Data Axle
14	Data volume: Does the startup have access to a sufficient amount of data? (0-5 points)	3	Has access to a moderate amount of data but could benefit from more, such as partnering with data brokers like Quandl
15	Data partnerships: Has the startup formed partnerships for data access, if needed? (0-5 points)	3	Has partnerships with data providers like SafeGraph and Data Axle
16	Data quality: Is the data accurate and free from biases? (0-5 points)	3	mostly accurate, but may contain some biases, potentially due to over-representation of specific demographics
17	Data representativeness: Does the data accurately represent the target population or use case? (0-5 points)	4	The data generally represents the target population or use case, with a focus on urban environments
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19	Data Preparation and Preprocessing
20	Data cleaning: How robust is the process for handling missing, noisy, or inconsistent data? (0-5 points)	3	uses Python libraries like pandas and NumPy for handling missing, noisy, or inconsistent data
21	Data transformation: Are appropriate techniques used to transform raw data into a suitable format for AI models? (0-5 points)	4	TensorFlow and PyTorch
22	Automation: Is the preprocessing process automated to save time and resources? (0-5 points)	2	preprocessing process is partially automated using tools like DataRobot
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24	Data Labeling
25	Labeling process: Is the data labeling process efficient and accurate? (0-5 points)	4	uses Amazon SageMaker Ground Truth
26	Annotation quality: Are the annotations of high quality and consistency? (0-5 points)	4	The annotations are of high quality and consistency, good use of guidelines and quality assurance checks
27	Labeling costs: Is the startup managing data labeling costs effectively? (0-5 points)	4	using a mix of in-house annotators and crowdsourcing platforms / Amazon Mechanical Turk
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29	Data Storage and Management
30	Storage infrastructure: Is the data storage infrastructure secure and scalable? (0-5 points)	5	google cloud storage
31	Data management practices: Are the startup's data management practices effective and compliant with regulations? (0-5 points)	4	tools like Apache Airflow for data pipeline management and adhering to GDPR and CCPA regulations
32	Data protection: Is the startup adhering to relevant data protection laws and best practices? (0-5 points)	4	using encryption tools like Google Cloud KMS and IAM policies for access control
33	Data versioning: Does the startup have a system in place to manage different versions of the dataset for reproducibility and traceability? (0-5 points)	4	using DVC to manage different versions of the dataset for reproducibility and traceability
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35	Data Augmentation
36	Augmentation techniques: Is the startup effectively using data augmentation techniques to enhance dataset size and diversity? (0-5 points)	3	image rotation, scaling, and flipping with libraries such as imgaug and Albumentations
37	Impact on performance: Do the augmentation techniques improve model performance? (0-5 points)	3	looks like moderate improvement in model performance, particularly in handling edge cases; need to reduce bias (todo: connect with Mostly.ai)
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39	Data Privacy
40	Privacy measures: Does the startup have strong data privacy measures in place, such as anonymization or differential privacy? (0-5 points)	4	implemented differential privacy using tools like Google's TensorFlow Privacy library
41	Compliance: Is the startup compliant with relevant data privacy regulations? (0-5 points)	3	mostly compliant with data privacy regulations like GDPR and CCPA but could improve in some areas, such as data breach notification
42	User trust: Is the startup transparent about its data practices, fostering user trust? (0-5 points)	5	transparent about its data practices, sharing privacy policies and data processing details on their website
43	Data retention: Does the startup have clear data retention policies in place, ensuring data is not stored longer than necessary? (0-5 points)	5	clear data retention policies in place; using Google Cloud Storage Object Lifecycle Management
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45	Tradeoffs and Overall Strategy
46	Tradeoffs: Is the startup aware of the tradeoffs made in its data strategy and their impact on the AI system's performance? (0-5 points)	5	to discuss
47	Strategy alignment: Does the data strategy align with the startup's overall business goals and market needs? (0-5 points)	5	yes
48	Adaptability: Is the startup's data strategy adaptable and flexible to accommodate changes in the market or technology landscape? (0-5 points)	5	yes
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50	Scorecard
51	Data Acquisition	17
52	Data Preparation and Preprocessing	9
53	Data Labeling	12
54	Data Storage and Management	17
55	Data Augmentation	6
56	Data Privacy	17
57	Tradeoffs and Overall Strategy	15
58	Total Score	93
59	% of Max	78%
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