If you’re reading this, you’re likely at a crossroads between adopting a data labeling tool for your company or keeping things in-house. You may have found that your data labeling needs are growing as your business grows. There are a lot of options out there, and it can be overwhelming trying to decide if—or when—to move from in-house to a data labeling tool.
We’re at a crossroads in the world, too. NLP is swiftly becoming a critical part of everyone’s product, and businesses are realizing that this will not slow down and finding the best process for quality data now is important. And NLP can do so much, from analyzing the sentiment of reviews, to labeling legal documents, to searching through medical records for similarities, to finding trends in customer service reports. This won’t slow down, and finding the right process for your labeling now is key.
When it comes to data labeling, there are two approaches: automated data labeling and manual data labeling. Depending on your needs, your company might use manual, automated, or some combination of the two. Manual data labeling is still heavily relied on, but technology and businesses are moving towards automated systems at a rapid pace. It pays to stay ahead of the curve and streamline your data processes now, since it’s not a question of if businesses will move towards automated data labeling and AI, but when.
Many companies start the data labeling process in-house. Typically, this involves engineers creating a data labeling process, executive-level signoff on keeping the process in-house, and then in-house engineers maintaining the process/tools (and building them out as needed). This way, the entire data management process is centralized and maintained, as are all of the costs associated with it.
Datasaur is a labeling platform that specializes in text labeling and audio labeling. It is designed with usability and customizability at the core, and is the leading NLP labeling tool. In Datasaur, you can use automated project creation to quickly create projects, then you can leverage ML-assisted labeling through popular libraries/integrations like spaCy or Hugging Face to automatically annotate the bulk of your data. Once your projects are created and up to 80% of your data is automatically labeled before your annotators even touch it, enterprise-grade labeling features are then built in to make the rest of the labeling process simple and streamlined (think coreference resolution, inter-annotator disagreement tools, audio labeling tools, and more).
1. Engineering time: It’s easy to underestimate the amount of engineering time that goes into lifting an in-house data labeling option. It’s also easy to underestimate the time that goes into maintaining an in-house data labeling solution. Moving to Datasaur can save you the equivalent of 2-3 engineers simply by freeing up time and energy.
2. Customizability: One of the big perks of labeling in-house is that you can build processes and solutions around your needs and your needs alone. You have full control, customizability, and agency (so long as things stay within budget and engineering capacity). This can be a major factor since you don’t want to give up that customizability and control if you move to an external platform.
Datasaur is also highly customizable, which is one of the things that sets it apart in the NLP space. There are so many ways that you can customize the UI to make sure it fits your needs. With Datasaur, we can customize the interface and configure your data labeling workflows to suit your needs. You also get a dedicated project manager and access to our full support team, who are on board to make sure your labeling needs are met.
3. Price: Price is a major factor when finding the right labeling option. When you build an in-house labeling option, all of the costs are centralized and you can choose where funds are allocated. The main drawback here is that it’s incredibly easy to sink money into building and maintaining the option as your needs grow. Simply put, it can feel cheaper to keep things in-house, but internal engineers building and supporting labeling features gets expensive fast, so this is something to watch carefully.
Datasaur stands out in the industry because it is highly cost-efficient. There are three different price ranges: Free, Growth, and Enterprise. (Note that the free version does not include a team space and is more of an individual playground). All of the product features Datasaur hosts are offered to both Growth and Enterprise customers, the main difference for us is the number of people using the platform.
4. Efficiency: As your data labeling needs grow, it’s important to make sure you’re able to keep labeling efficiently, without losing accuracy. This is a key piece to keep in mind if you’re building an in-house option, and is at the core of Datasaur’s product. This is why we built features like QA review tools, inter-annotator disagreement features, and workforce management options at the individual and team level. This gives you access to all of the metrics so you can make sure that you’re keeping—or even bolstering—efficiency as your labeling scales up.
5. Advanced options: As your data labeling needs grow, you’ll need more features and tools. These can be built in-house if you have the capacity. Datasaur can also support this, as Datasaur is built to handle enterprise-level labeling needs. Some of the options you might want as your labeling needs scale are:
Datasaur could be the right fit for you if:
Datasaur may not be the right fit for you if:
We hope you find the option that is best suited to your company. If you have NLP labeling needs and would like to find out more, request a demo today to see how Datasaur could streamline your data labeling.
