Data, by definition, is comprised of stats and facts that have been collected and used for reference and analysis, yet, it’s so much more than that. It’s the building block for machine learning. It fuels our AI models and without it we wouldn’t have the technology we have today. That’s why data sourcing is a crucial step in managing data for the AI lifecycle and, is our first key takeaway in our 2022 State of AI and Machine Learning report.
In our report, we found that 42% of technologists say the data sourcing stage of the AI lifecycle is very challenging. Business leaders, however, were less likely to report data sourcing as very challenging (24%). This gap in perception of the level of effort needed to properly manage sourcing quality data could lead to misaligned budgeting and resourcing for AI projects.
Data Sourcing Basics
You get back what you put in. That old adage is particularly poignant when it comes to data sourcing. In order for an AI model to be trained correctly, the data needs to be high-quality, diverse and ethically sourced. What this means is using data that is free from bias and personally identifiable information (PII) and also contains data that supports a wide variety of use cases. Synthetic data is another resource that can account for those rare edge cases. Sounds like a tall order, right? At Appen, we leverage pre-labeled datasets (PLD), 1 million+ crowd workers, and our synthetic data partnership with Mindtech to source the right data needed for each project.
To start sourcing data for a particular project the following items are needed:
- List of desired data points
- Primary and secondary data source identification
- Desired data volume
- Quality expectations
These points are key as you identify the exact data you need and ensure your source(s) are capable of providing the necessary information. It’s also critical to identify exactly how much data is needed for your AI project. AI and machine learning models may not be trained properly if insufficient data is sourced causing challenges such as a lack of information for specific use cases. In addition to quantity, it’s also pertinent to set quality expectations to ensure the model is trained with enough high-quality data to function properly. Failing to source enough high-quality data at the start may require additional data collection, delaying the project timeline and possibly significantly increasing cost.
While the ability to gather data in itself seems relatively straightforward, it’s proving to be a major bottleneck for teams building artificial intelligence applications.
Some factors creating these challenges:
- Lack of sufficient data for specific use cases
- New machine learning techniques requiring greater data volumes
- Incorrect processes in place for acquiring data
Fortunately, these can be easily remedied. First and foremost, you want to ensure you allocate enough budget for data sourcing to get everything you need to properly train your machine learning model. In fact, in our 2022 State of AI and Machine Learning Report we discuss how the data sourcing stage receives the biggest budget allocation of the 4 stages of the AI lifecycle. By having enough budget dedicated to this stage, you ensure the ability to source enough data to address all necessary use cases. If there’s a specific use case that’s difficult to source data for, synthetic data can be used to generate data for said use case. As for making sure the correct sourcing processes are used, it’s essential to reach out to an experienced data sourcing company to validate the chosen methods work. You’ll get the right data you need the first time, and be able to stick the project timeline you’ve set.
Learn More about Data Sourcing
Data sourcing is critical to AI model success. Appen Industry experts share their thoughts on this stage and much more in our 8th annual State of AI and Machine Learning Report . Read it today to better understand the current industry trends and challenges. For further information, watch our webinar, where we discuss in-depth all topics covered in our State of AI Report.