In our ever-expanding digital universe, the sheer volume of data we produce on a daily basis is an important reminder of our dependence on web connectedness. For instance, online consumers spend an average of $1 billion a minute on eCommerce purchases, relinquishing information about their preferences and habits throughout the process of their buyer’s journey. The digital traces we leave behind via tweets, SMS texts, and online clicks can all amount to valuable information that companies utilize for data annotation purposes, mainly through Machine Learning (ML) for Artificial Intelligence (AI) models.
In other words, data matters, especially given the booming appeal of AI among enterprises seeking a competitive edge. These organizations recognize the benefits of adopting data annotation as an integral part of their business operations, where high-quality data is of utmost importance to operate at optimal levels. But common roadblocks are likely to arise from having to allocate precious resources to organize and “clean out” this data. In fact, many enterprise organizations report that one of the most prominent pain points impeding progress on their AI and ML projects consists of working with poor quality data. And the longer data annotation takes, the greater the bottleneck for your next product release.
So while the conversation is shifting from prioritizing big data, to good data for AI companies, it’s worthwhile to ask: how should you make this change without depleting your existing resources?
With a hybrid automation platform like Wrk, you don’t have to choose between poor quality data or time-consuming processes for your team. Many hybrid platforms create manual annotation strategies that are designed with automation to empower humans. This dual “automation-human” approach results in guaranteed accuracy that expedites data annotation procedures for AI projects from start to finish, allowing your data science teams to focus on more pressing tasks – a real win-win if you ask me!
Before we go any further, if you need a quick refresher on what data annotation actually is, then take the time to read through the following section: What Exactly is Data Annotation? If you feel like you’ve already got a good grasp of the basics of data annotation, then skip ahead to Value of Data Annotation in the AI Sector.
What Exactly is Data Annotation?
So, what is data annotation, anyway?
Imagine having to sort through thousands upon thousands of lengthy pieces of traffic cam footage with the task of labeling every vehicle that appears into frame. That’s typically what a day in the life of a data annotator looks like: it involves a meticulous process of labeling text, image, video, audio and a host of other data types, with the goal of rendering this data legible enough for AI-powered computer systems that are programmed for supervised learning.
It’s worth stressing that the labelling process itself involves humans. Datasets from unstructured sources become labelled, marked, colored, or highlighted to identify differences, similarities, or patterns between each other.
Once this data is annotated and ready, they’re sold to companies whose data scientists need large datasets to train their ML models. Data scientists need to work with clean, annotated “test” data in order to train their ML models to identify important patterns in actual data for real-world scenarios. Essentially, when you’re labeling data, you’re teaching your AI system the outcome you want your ML model to predict, for when you’re ready to “feed” it real incoming data.
Data annotation is a crucial part of the data pre-processing stage during supervised learning. With enough annotated data, ML models can identify the same patterns again and again.
Here’s a list of the most popular categories of data that are utilized by AI-powered businesses:
Data annotation for text
As its name already indicates, text annotation involves deciphering unstructured text to understand what’s being communicated in typed documents such as emails, chat transcripts, and online reviews. For instance, the availability of AI chatbots to deliver speedier and more efficient customer service is one example of how text annotation benefits us.
Data annotation for images
In image annotation, the primary goal is to render objects within an image recognizable to ML models programmed for visual perception. For instance, a dataset of images depicting people may have labeled rectangles drawn around every human element visible. Other examples include medical imagery such as x-rays, CT scans, MRIs, and ultrasounds for better disease detection and more accurate diagnoses.
Natural Language Processing (NLP)
Similarly to text, NLP annotation focuses exclusively on speech recognition for machines. Sentiment analysis of product reviews is a prominent example of the usefulness of NLP.
That was an overview of data annotation, including some of its most common use cases. But what’s the real value of data annotation for AI models? Let’s find out.
Value of Data Annotation to the AI Sector
For companies embarking on AI projects, data is part and parcel of effective Machine Learning. While the recent surge in the volume of data – particularly open-source or public – being readily available may seem like a blessing for data scientists who are training AI data in-house, quality matters as much, if not more, than quantity. This point is reiterated by Nathaniel Gates, CEO and co-founder of Alegion when he points out that the sheer volume of poor quality training data can overwhelm even the most skilled data science teams. To boot, 81% of enterprises surveyed by an Alegion study claim that the data training process had proven to be more difficult than initially expected.
In fact, many companies undertaking in-house data labeling are struggling to deliver a level of accuracy and sophistication that’s becoming increasingly required to produce ML models for realistic, high-stakes scenarios, such as self-driving cars or accurate medical diagnoses.
The Merits of a Hybrid Approach
For many businesses, in-house data annotation efforts turn out to be an impediment and not a solution. Often, it detracts from the important goals of an overworked and under-resourced data science team, who would much rather spend its limited time fine-tuning and delivering meaningful models. This is where hybrid solutions emerge to bridge the gap between accurate data and costly, time-consuming labor.
Wrk’s unique platform marries automation and a skilled workforce to speed up your annotation process while delivering accurate and consistent results, in the following ways:
To expedite your annotation process, a parallelized process of computing data allows larger tasks to be broken down into smaller tasks that are each individually and simultaneously solved by a team of skilled workers, also known as our Wrkforce.
Wrk’s hybrid system ensures that the quality assurance process is rigorously vetted by humans, who do the double-checking of tasks so your team won’t have to. This human-on-human supervision guarantees excellent end results.
While pure automation can account for the great majority of your tasks, human intervention is essential to fill the discrepancy of edge cases. At its core, Wrk’s platform, involving highly sophisticated automation, empowers workers to solve some of your most resource-draining tasks, with efficiency and precision.
A Cost-Benefit Analysis to Hybrid Automation
Wrk’s hybrid platform allows its skilled human labellers to manually annotate your raw data, proving to be more cost-effective than efforts attempted in-house and more accurate than “pure automation.” The benefits accrued from a hybrid approach boil down to two important factors: skill and cost.
Let’s look at both of these points a little closer.
If you want to avoid the mishaps of inaccurately labeled data at the hands of labelers who lack expertise in specialized domains, but also do not want to incur the cost of individually sourcing data analysts who will be a financial burden on your team, then you need to consider the hybrid automation approach. Fields with “high-context” data like legal contracts or medical imagery can be hard to get right without investing on an in-house addition. Thankfully, by relying on the services of a hybrid approach to automation with a ready-to-dispatch team, you can eradicate this step seamlessly.
A 2019 report by Cognilytica Research discovered that for every dollar that could be spent on data labelling services from third-party providers, five times that amount is actually dispensed on internal labelling efforts. These costs can be minimized and even eliminated if the annotation process is relegated to a highly trained team of labellers.
It’s incontestable: the use of poor quality data (from improper or partially labelled data) can result in inaccurate or biased models. And as standards for accuracy increase, data can’t just be computed via pure automation systems – human diligence and attention needs to be factored into the loop. That’s why Wrk’s hybrid automation platform boasts an impressively low margin for error. Wrk combines automation with a manual annotation system conducted by its specialized Wrkforce, offering a high probability for accurate results that won’t require additional verification from your data science teams.
Avoid in-house shortcuts that can end up being costly and time-consuming in the long-run. Partnering up with a data annotation solutions provider like Wrk can ultimately free up your team’s time spent on aggregating and cleaning reams of data, empowering them instead to devote their expertise to work that matters most to them.