Driven by the need to optimize your results and make way for more AI training with additional volumes, you could be at that point where you’re not sure if you should consider crowdsourcing data collection or stick to your internal sources. With the onset of crowdsourcing platforms, it might seem relatively simple to get the required volumes of data at just the right quality.
Crowdsourced data could either break or make your AI ambitions and before you proceed to go ahead with this process, you need to understand the benefits and pitfalls of crowdsourced data.
Being in the industry for years, we understand how the system works and we’ve dealt with diverse data collection techniques to have an authority on this. So, from our expertise and perspective, let’s analyze if crowdsourced work is the route you should take.
Decoding The Benefits And Pitfalls Of Crowdsourced Data For Machine Learning
Quick Reference
| Pros | Cons |
|---|---|
| Saves Time | Maintaining Data Confidentiality |
| Minimizes Expenses | Wavering Data Quality |
| Removes Data Bias | Lack Of Standardisation |
| Reduces Pressure on Your In-house Talent Pool | |
| Highly Scalable |
Advantages Of Crowdsourcing Data Collection
Saves Time
Research reveals that data scientists and AI experts get to spend only 20% of their time building and developing machine learning models. The remaining time is spent on compiling, curating, and cleaning data. This means the tasks that demand their attention and intervention are prioritized after data collection and annotation tasks.
However, crowdsourcing data collection through an experienced vendor eliminates this phase and automates the data collection and annotation processes. With rigid guidelines and protocols, they ensure crowdsourcing of data is uniform and standardized. This frees up the time of experts to focus on what matters more, eventually decreasing the time to market for your product or service.
Removes Data Bias
For your AI model to churn out meaningful results that are universal, it has to be trained with rich pools of datasets. Crowdsourcing complements this process by allowing people from diverse backgrounds to upload required data and make your AI models as wholesome as possible. You would have ultimately eliminated bias to a significant extent.
Minimize Expenses
Data collection is not just tedious and time-consuming but expensive as well. Regardless of whether you have internal teams or 3rd party vendors, profits happen only when the process is long-term. So, comparatively, crowdsourcing data collection minimizes the expenses you would incur in data sourcing and labeling. For bootstrapped companies with limited budgets, this could be an ideal solution.
Reduces Pressure On Your In-house Talent Pool
When you employ your existing team members to collect data and annotate it, you are either asking them to work additional hours or compensating them for it. Or, you are asking them to accommodate this task amidst their work hours and tight deadlines.
Regardless of the case, it adds pressure on your employees and it would spoil the quality of both the tasks they are trying to juggle. This could lead to attrition and more expenses on training new recruits. In this instance, crowdsourcing data collection arrives as a reliable alternative as your team has standardized data in their hands to work on.
Highly Scalable
Relying on internal sources to generate more volumes of data than the current numbers could prove expensive. While collaborating with data collection and annotation companies would be a better alternative. (Read: Points to be kept in mind while shortlisting a data collection vendor.)
Crowdsourced work comes as a relief by allowing you to scale your data volume requirements. You could both increase your data volume or decrease it at any given time. All you have to do is ensure there are adequate QA processes set to ensure quality output.
Cons Of Data Crowdsourcing
Maintaining Data Confidentiality
Maintaining data confidentiality is a huge task ahead of you when it comes to crowdsourcing. Now, it is on the vendor and crowdsources team to maintain and respect data integrity and confidentiality by adhering to protocols and data privacy standards. If the data is related to healthcare, additional measures and compliances like HIPAA should be met as well. This could take a significant portion of your team’s time setting the protocols up.
Wavering Data Quality
There is no guarantee that the final quality of the data you receive will be airtight and impeccable if controlled properly. One of the major drawbacks of crowdsourcing data collection is that you will encounter wrong and irrelevant data. If your process is not set up right, you could end up spending more time and money on this than working with data vendors.
That’s why we recommend checking out our crowdsourcing guidelines.
Lack Of Data Standardisation
With crowdsourced work, that’s not the case. There is no proper standard followed and it all depends on individual contributors and how experienced they are at participating in crowdsourcing data. You could receive both haphazard and clean files from time to time, making it difficult for you to establish standards.
So, What’s Better?
It depends on your urgency and budget. If you feel you have a very limited time and crowdsourcing data collection is the only inevitable way forward, it would work because you would be willing to compromise on a few aspects as we discussed.
However, if you feel your AI ambitions are more important and that you wouldn’t offer any scope or space for concerns to crop up, the best way forward is to look for ideal data vendors like us how can help you reap the benefits of crowdsourcing.
