Today a business without Artificial Intelligence (AI) and Machine Learning (ML) is at a significant competitive disadvantage. From supporting and optimizing backend processes and workflows to elevating user experience through recommendation engines, and automation, AI adoption is inevitable and essential to survival in 2021.
However, getting to a point where AI delivers seamless and accurate results is challenging. Proper implementation isn’t achieved overnight, it is a long-term process that can continue for months. The longer the AI training period, the more precise the results. With that said, a longer AI training duration demands more volumes of relevant and contextual datasets.
From a business perspective, it is near impossible that you will have a perennial source of relevant datasets unless your internal systems are highly efficient. Most businesses must rely on external sources like third-party vendors or an AI training data collection company. They have the infrastructure and facilities to ensure you get the volume of AI training data you need for training purposes but choosing the right option for your business isn’t that simple.
There are plenty of subpar companies offering data collection in the industry and you must be careful who you choose to collaborate with. Partnering with the wrong or incompetent vendor could push your product launch data indefinitely or result in a capital loss.
We’ve created this guide to help you choose the right AI data collection company. After reading you will have the confidence to identify the perfect data collection company for your business.
Internal Factors You Should Consider Before Looking for A Data Collection Company
Collaborating with a data collection company is only 50% of the task. The remaining 50% revolves around groundwork from your perspective. The perfect collaboration calls for questions or factors to be answered or further explained. Let’s look at some of them.
What’s Your AI Use Case?
You need to have a proper use case defined for your AI implementation. If not, you are deploying AI without a solid purpose. Before implementation, you need to figure out if AI will help you generate leads, push sales, optimize workflows, have customer-centric results, or other positive outcomes specific to your business. Clearly defining a use case will ensure you look for the right data vendor.
How Much Data Do You Need? What Type?
You need to put a generic cap on the volume of data you need. While we believe that higher volumes will result in more accurate models, you still need to define how much is necessary for your project and what type of data will be most beneficial. Without a clear plan, you will experience excessive waste in cost and labour.Below are some common questions business owners ask while preparing for collection to identify what:
- Is your business based on computer vision?
- What specific images as datasets you will need?
- Do you intend to bring predictive analytics into your workflow and require historic text-based datasets?
How Diverse Should Your Dataset Be?
You also need to define how diverse your data should be, i.e., data collected from age group, gender, race, language & dialect, education qualification, income, marital status, and geographical location.
Is Your Data Sensitive?
Sensitive data refers to personal or confidential information. Details of a patient in an electronic health record used to conduct drug trials are ideal examples. Ethically, these insights and information should be de-identified because of the prevailing HIPAA standards and protocols.
If your data requirements involve sensitive data, you should decide how you intend to go about de-identifying data or if you want your vendor to do it for you.
Data Collection Sources
Data collection comes from various sources, from free and downloadable datasets to government websites and archives. However, the datasets must be relevant to your project, or they won’t possess any value. Apart from being relevant, the dataset should also be contextual, clean, and comparatively of recent origins to ensure your AI’s results align with your ambitions.
How To Budget?
AI data collection involves expenses such as paying the vendor, operational fees, data accuracy optimizing cycle expenses, indirect expenses, and other direct and hidden costs. You need to carefully consider every single expense involved in the process and formulate a budget accordingly. The data collection budget should also be aligned with your project’s scope and vision.
You need to put a generic cap on the volume of data you need. While we believe that higher volumes will result in more accurate models, you still need to define how much is necessary for your project and what type of data will be most beneficial. Without a clear plan, you will experience excessive waste in cost and labour.How to Choose the Best Data Collection Company for AI & ML Projects?
Now that you have the fundamentals established, it is now comparatively easier to identify ideal data collection companies. To further differentiate a quality provider from an inadequate vendor, here’s a quick checklist of the aspects you should pay attention to.
Sample Datasets
Ask for sample datasets before collaborating with a vendor. The results and performance of your AI modules depend on how active, involved, and committed your vendor is and the best way to gain insight into all these qualities is by getting sample datasets. This will give you an idea of whether your data requirements are met and tell you if the collaboration is worth the investment.
Regulatory Compliance
One of the primary reasons you intend to collaborate with vendors is to keep the tasks compliant with regulatory agencies. It’s a tedious job that requires an expert with experience. Before deciding, check if the prospective service provider follows compliances and standards to ensure the data procured from diverse sources are licensed for use with appropriate permissions.
Legal consequences could result in bankrupting your company. Be sure to keep compliance in mind when choosing a data collection provider.
Quality Assurance
When you get datasets from your vendor, they should be formatted correctly and ready to be directly uploaded to your AI module for training purposes. You shouldn’t have to conduct audits or use dedicated personnel to check the dataset’s quality. This is only adding another layer to an already tedious task. Ensure your vendor always delivers upload-ready datasets in the format and style you require.
Client Referrals
Talking to the existing clients of your vendor will give you a first-hand opinion on their operating standards and quality. Clients are usually honest with referrals and recommendations. If your vendor is ready to let you speak to their clients, they clearly have confidence in the service they provide. Thoroughly review their past projects, speak to their clients, and seal the deal if you feel they are a good fit.
Dealing With Data Bias
Transparency is key in any collaboration and your vendor has to share details on whether the datasets they provide are biased. If they are, to what extent? Generally, it is difficult to eliminate bias completely from the picture as you can’t identify or attribute the precise time or source of the introduction. So, when they offer insights on how the data is biased, you can modify your system to deliver results accordingly.
Scalability Of Volume
Your business is going to grow in the future and your project’s scope is going to expand exponentially. In such cases, you should be confident that your vendor can deliver the volumes of datasets your business demands at scale.
Do they have enough talent in-house? Are they exhausting all their data sources? Can they customize your data based on unique needs and use cases? Aspects like these will ensure the vendor can transition when higher volumes of data are necessary.
Your Future Depends on Utilizing AI and Machine Learning
We understand that finding the right data collection company is challenging. It doesn’t make sense to ask for sample sets individually, compare vendors, and test services with quick projects before committing. Even when you find the right company, you must dedicate up to two months preparing for data collection.
That’s why we suggest eliminating all these instances and getting straight to that phase of collaboration, and getting quality datasets for your projects. Get in touch with Shaip today for impeccable data quality. We exceed all the elements we have mentioned on the checklist to ensure our partnership is profitable for your business.
Talk to us today about your project, and let’s get this rolling as early as possible.
