All projects involving Artificial Intelligence (AI) and Machine Learning require AI training data. The only way AI systems can learn to become more accurate and relevant to their purpose is to input applicable information. Sourcing and preparing data sets is precisely where companies struggle to utilize AI and machine learning potential.
AI training requires consistent input of massive volumes of contextual data for machines to deliver precise results. That’s how they learn to become sharper with every yield. Sourcing quality data poses significant challenges to companies. They either run out of constant sources or fear they would run out of funding required to collaborate with data collection companies.
A common misconception is that data vendors aren’t affordable for business owners. We will address the cost of outsourcing your AI training and how an investment will save money in the long run.
Different Sources of Data
To understand how data vendors are cost-effective, we must first realize the multiple sources of data acquisition and their unique advantages and disadvantages. Furthering your understanding of each source will give you an idea of the benefits and drawbacks of each.
| Source | Advantages | Disadvantages |
| Free Resources | They provide datasets across industries and market segments for free. | Requires countless hours of manual work to explore multiple datasets and categories before finding the right one. |
| Companies have multiple options, for instance, Kaggle, AWS, Google Dataset Search Engine, and many others. | The datasets are mostly raw and uncleaned. | |
| The data has to be annotated manually, which is again time-consuming. | ||
| May involve licensing issues for certain datasets. | ||
| Internal Sources | They provide contextual datasets as they are generated in-house through diverse touchpoints defined by the company. | The volume of data available depends on traffic, traction, and other touchpoint-based metrics. |
| Datasets can be customized according to requirements. | Collaborations among and within departments could be daunting at times. | |
| If your product has a limited time to market, internal sources could cause significant delays. | ||
| Data annotation is still a manual task. | ||
| Paid Sources or Data Vendors | Perennial sources of quality AI training data. | May be expensive based on how niche your product is. |
| Datasets can be customized according to project requirements. | ||
| Data is always delivered on time regardless of your time to market. | ||
| Licencing and compliances are taken care of by vendors. | ||
| Datasets are annotated and checked for quality before delivery. |
If you look at the table above, you will understand that data vendors offer more advantages than disadvantages. To give you a better idea, let’s explore these aspects in detail.
How A Data Vendor is always beneficial for your AI Projects
Data vendors are specialists in their domain. They are pioneers who have been familiar with AI and ML even before they became mainstream. Data collection companies have massive networks and access to databases that have diverse varieties of datasets. They also have the influence and infrastructure to generate new datasets from scratch using their networks and contacts.
Data collection firms will deliver impeccable datasets consistently for your projects. Apart from this, here are some of the competencies they bring to the collaboration:
- Vendors can generate, curate, and deliver data from across different formats. For instance, if you intend to develop voice search modules for your app, they can get you voice data relative to your needs. They can also deliver image, text, or video-based data advantageous to your project.
- Data experts will take care of all the hindrances and headaches that come with licensing and regulatory compliance. The datasets they provide would be completely devoid of limitations.
- Data Collection companies ensure the data you receive is unbiased, or they will let you know of possible biases so you can modify your systems for relevant results.
- You will get the most updated datasets from backgrounds, demographics, market segments, and other critical segments as needed.
Why Data Vendors are Less Expensive
Data vendors and specialists can charge competitive rates because they have customized contracts for bulk projects. Their massive networks are also one of the primary reasons why they prove to be less expensive in the longer run. Having been in the industry for years, they know which source is applicable for each type of dataset, how to fetch data swiftly under tight deadlines, and who to contact for accurate datasets.
As the duration of your collaboration increases, they will comprehend your requirements and autonomously deliver quality datasets. You will end up incurring absolutely zero expenses on data quality optimization cycles, overhead costs, training, annotation, and other costly expenditures.
The Shaip Advantage
At Shaip, we are veterans in the field of data annotation and acquisition. With over 13 years of experience, we understand data requirements like nobody else in the market. We have three rounds of rigorous quality checks to ensure the data you receive is ready for upload. We also take pride in our transparency and have built our model around delivering on our promises.
A Quick Case Study
We specialize in providing quality healthcare data. One of our most successful collaborations has been with an insurance company. They wanted to deploy AI-driven modules such as predictive analytics to assess the probability of its insurers developing ailments and offer customized premiums accordingly.
To accurately predict outcomes, they required massive volumes of healthcare data from specific demographics. With voluntarily provided details, insurers would be able to get an idea of the possible conditions they would develop based on their lifestyle, genetics, hereditary, and other factors. The insurance company collaborated with us for datasets, and we delivered them in the stipulated time frame.
One of the significant challenges concerning healthcare data is ensuring we de-identified patient data and implemented HIPAA protocols. Our rigorous process guaranteed the data was protected from any form of re-identification and ultimately meeting all compliance standards.
Wrapping Up
Utilizing data vendors instead of resorting to free resources saves money in the long run and prepares your company for exponential growth. If you want your AI modules to deliver accurate results, you should first feed them relevant data, which can come only from experts like us.
Get in touch with us today to discuss your ideas and requirements.