Launching a data collection program for machine learning? Do this first!
When developing a product for human interaction using artificial intelligence (AI) and machine learning (ML), having the highest quality data is necessary to effectively train AI/ML algorithms. Ground truth data is data collected at scale from real-world scenarios, to train algorithms on contextual information such as verbal speech, natural language text, human gestures and behaviors, and spatial orientation and behavior. Ground truth data collection ensures that the requirements for scale, diversity, user intent, and context can be met based on the specific needs of a project.
When beginning a data collection program, there are several considerations that will set a project up for success, long before a single data point is gathered. Here are four recommendations to help lay the foundation for an efficient, effective ground truth data collection program.
Identify the specific scenario for the target markets
Without specific markets to target through a clearly defined scenario, data collection will be unnecessarily time consuming and will be unlikely to yield the desired results. With a clearly identified target market, local market experts can leverage contextual information to improve feedback and scenario-based testing can target an ideal demographic for a project.
Consider privacy priorities
As privacy restrictions increase across industries, any project must consider when and how much personally identifiable information is needed for meaningful results. Understanding privacy protections and restrictions is necessary to optimize for a balance between stringent policies and efficient, useful data collection.
Create a testing tool
The most time-consuming obstacle to effective data collection, ingestion, and processing is the lack of a tool to appropriately handle each of these crucial steps. Developing a tool to handle each of these procedures in tandem or separately will ensure that the scenario from which a project is deriving data is clearly defined and that testing at scale can begin immediately. Remember to incorporate all stages of data handling into the tool to avoid a backlog of suboptimal feedback.
Field test the tool
To acquire feedback at scale, a data capture tool must be optimized for an end-user. Ensure that the completed data capture tool has a user-friendly interface with simple, intuitive user flow, and test the tool before attempting to gather data at scale. Without these precautions, data results will be skewed by usability issues rather than actual performance.
To learn more about ground truth data services, click here to view our case study on collecting speech data for an in-home smart-hub product developed by a leading social media company.