Quality data translates to success stories while poor data quality makes for a good case study. Some of the most impactful case studies on AI functionality have stemmed from a lack of quality datasets. While companies are all excited and ambitious about their AI ventures and products, the excitement doesn’t reflect on data collection and training practices. With more focus on output than training, several businesses end up delaying their time to market, losing funding, or even pulling down their shutters for eternity.
If you’re in a similar situation (we hope not), where your AI models are failing and you’re quite unsure of what to do. In this post, we will explore the importance of quality data, where you could source it from, and look at some crucial data acquisition considerations you need to keep in mind when sourcing data. This will not only help you build a model that is impeccable but save on your AI budgets as well.
Data is what gives AI models a purpose. Without AI, AI models would only exist as exhibits devoid of meaning. However, instilling a purpose alone isn’t enough. The outcomes of the purpose and vision should be enriching and rewarding and this can happen only through quality data.
Quality data is anything that complements your AI model’s goals and purposes. That’s why companies strive to source the most impeccable quality AI training data for their models. The attributes of quality data include being –
When your AI model trains on such quality datasets, the results speak for themselves. If you’re looking for quality data for your AI projects, there are some exhaustive sources you could tap into. Let’s explore some of them.
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These are open-source avenues from where you can source AI training data for your projects. These avenues could be anything from forums or data search engines to directories, archives, and portals. Based on your requirements, you can head to an avenue and mine data.
Internal sources are data resources that are defined by you through data touchpoints. Your CRMs, app usage statistics, heat maps, analytics reports, and more are internal sources that you have exclusive access to. These are relevant and contextual sources that are also updated on a daily basis. This helps you come up with effective AI models that offer tailor-made resolutions to your problems.
However, the catch here is the availability of massive volumes of datasets. Depending on your website traffic, app downloads, or more, the data volume varies. If you’re starting out new, internal resources would be of little help.
These refer to outsourcing methodologies, where specialist companies work on collecting and annotating data based on your requirements. They have their own data collection modus operandi and they ensure they deliver the most accurate and quality data to you for your ambition. This is where Shaip can help you source the right training data to train your AI/ML models.
Because the aspect of data in building AI models is layered, we need to consider a lot of factors before we zero in on our data collection resources and the type of data we intend to acquire. The cost of bad data is expensive and it could deter your financial standings, product launch deadlines and most importantly, the trust you aim to build in your market segment.
So, to ensure your data collection strategies pay off, let’s consider some inevitable factors.
The relevance of data is directly proportional to your business needs. What do you intend to achieve with your AI model? What real-world problem is it going to solve? What are your intended demographics and market segment? More such questions will give you a clear idea of how you could go about collecting data for your project.
This is crucial as data and privacy concerns are increasingly becoming prominent around the world today. As countries wake up to the importance of data confidentiality and integrity, it is on you to consider all possible compliances and standards you follow and implement when it comes to data collection.
From GDPR and USGS and other federal and government regulations on data protection, you have to ensure every single compliance is met. This becomes more important when your data resources are open-source, where there is hardly any information available on how credible or compliant the data is.
Free and internal resources are ideal but if you end up spending more time on annotating, cleaning, or removing bias from your datasets, you are only ironically prolonging your project’s time to market. In the grander scheme of things, paid sources or outsourcing options turn out to be more cost-effective. So, compare accordingly and invest in data collection.
The recency of data has an impact on AI models’ outputs. For most projects, data needs to be fairly new, current, and recent. On the other hand, some projects also require data from specific time periods or seasons to generate the required results. As far as predictive models are concerned, they require data that is new and historic that could date back to the origins of your niche, organization, or segment.
So, when you are sourcing data, consider how recent or historical your data should be to train your model for optimum results.
Projects that are super-niche or specific require more time for data acquisition and sourcing than other generic ventures. This is also applicable to projects that are about to be rolled out in a geographical location uncharted by your competition or other companies in general. For visionary and pet projects, the data sourcing time is crucial and that’s why you need to know your project’s data immediacy requirements and get started with data collection several months in advance.
For products with limited time to market, data urgency is key and proper planning could save you from a lot of time-related trouble.
Close to 80% of the data scientists out there spend most of their time cleaning data. That’s why you could follow specific formats when you collect data to make everyone’s life easier. Specify if all your images should be of the format jpeg or png, audio files’ sample rates, the appropriate formats for video files, cells for spreadsheets, and more to make compilation simple. This way, you could directly feed your data into your model and initiate training.
So, these were the data acquisition considerations for 2021. No matter if you are about to venture into building an AI model or you’re halfway through it, consider these tips to acquire quality AI training data. This will help you save time, resources, and money.
As Co-Founder and CEO of , Vatsal Ghiya has 20+ years of experience in healthcare software and services. Besides Shaip, he also co-founded ezDI — a one-of-a-kind cloud-based software solution company that provides a Natural Language Processing (NLP) engine and a comprehensive medical knowledge base with products such as ezCAC and ezCDI, which are computer-assisted coding and clinical documentation improvement products called. In addition, Vatsal co-founded Mediscribes, a company that provides medical transcription-based offerings in the healthcare domain.