We’re entering an AI revolution that requires a scalable data labeling process. More and more, enterprises are realizing that machine learning has the ability to transform their business. And as generative AI becomes more prevalent, the uses of AI have expanded well past just data analysis.
But here’s the thing: ML models require a steady stream of high-quality data to function correctly. Feed them “dirty” data, and these models inundate organizations with fast — but potentially undesirable — results.
A lot of that data “uncleanliness” comes down to how well data is labeled. Poor labeling can lead to a shoddy ML model, and it’s not uncommon. According to Gartner, only 44% of data and analytics teams feel effective at providing consistent business value.
What does that statistic really tell us? Just because ML models work fast doesn’t mean they work well. Accurate data labeling is one of the keys to making AI valuable to businesses. That’s why data teams must build a scalable data labeling process that handles massive amounts of data in and data out — to ensure that these ML models have enough accurately-labeled training data to be effective and efficient.
New data strategies bring new data problems. There are a few roadblocks in the current data landscape that make it challenging to build a scalable data labeling process.
First off, data is getting too big for human minds to handle. To train ML models alone, data teams must follow the “ten times” rule, which means ingesting ten times the data you “need” to establish a data set. That means these models need thousands, and certainly no fewer than hundreds, of data points to reference.
Teams constantly discover new things with such large volumes of data in play. That means organizations require constant revisions to existing data sets — sometimes more revisions than any team can manage independently. Simply put, automation and some human intervention are an absolute must.
ML models can’t be entirely left to their own devices. Why? Because the advancements in AI cannot yet account for every outlier, discrepancy, or nuance in data. ML models work fast, but they can also work wrong.
That leads to significant problems like data drift, where source and target data start to differ significantly the longer ML models are left running on their own. As it stands, a scalable data labeling process still needs the light touch of a human signal to make sure the data fed to these models starts off on the right track.
Data quality is critical. Inaccurate data labeling can be a sucker punch to your organization. It ultimately leads to your models processing “bad” data, and your business outcomes might suffer for it.
Take what happened to Amazon in 2021, for instance. An MIT study found that Amazon reviews were mislabeled as positive when they were actually negative (and vice versa). This could lead to consumers mistakenly buying a product that they thought had good reviews — when in reality it was poorly reviewed.
Once that product arrives at the consumers’ doorstep, they’ll quickly realize that what they paid for is not what they got and see that misleading review labels are to blame. In this case, inaccurate data labeling not only affects the integrity and reliability of the organization; it also directly impacts consumer experience.
Inaccurate labeling can lead to pitfalls other than outputs gone awry and unhappy customers. It can also lead to machine learning bias, which has the potential to damage both your company’s culture and reputation.
The science behind AI and ML is still rapidly developing and changing, which means it’s still imperfect and filled with human-made blindspots. For example, TechCrunch reported that most data annotators will likely label phrases in African American Vernacular English (AAVE) as toxic. This leads to ML models trained on that existing standard of labels to automatically view AAVE as toxic as well.
There are a few essential building blocks to making a scalable and efficient data labeling process. For starters, the process must be:
Now that we’re familiar with the roadblocks facing data teams today, let’s dive into how you can build a scalable data labeling process. We’ll share how data pros and annotators alike can conquer those new challenges with ease.
It’s key to start off with a well-defined and well-documented data labeling process. That way, data teams have a solid ground plan or basic root of truth to build off.
For starters, organizations should have a “style guide” that dictates specific guidelines for each type of data labeling within their process. This allows annotators to have a reference point to always look back to, preventing problems with data drift and quality assurance.
Next, teams should stay up-to-date by being on the lookout for new issues, exceptions, and expectations within their data labeling practice. This can be done by enabling annotators to make revisions to the guide themselves (e.g., maintaining an internal wiki) with a hold on review from administrators before changes are finalized.
More data means more people. As the volume your data labeling process consumes increases, so must the number of team members you need to bring on board.
Organizations have a few options when it comes to building a data annotation team. They can insource via full-time employees, outsource via freelancers, or even crowdsource.
Ultimately, the best way to create a data annotation team is highly dependent upon each organization’s needs, budget, and existing resources.
It can be tough to measure the quality of your data labeling process without concrete guidelines. That’s where metrics, like the Inter-Annotator Agreement (IAA), come in. These standards can provide a general numeric measure of the ongoing quality of data labeling practices. (Label Studio makes this a key part of the annotator analytics we provide.)
Teams can also introduce metrics into automated tagging. Simply calculate and keep tabs on overall relevance scores to ensure that automated processes stay accurate. Here, light human supervision and AI can team up to make high-quality, high-volume data labeling a reality.
When it comes to maintaining quality one thing is key: Check, double-check, and triple-check again.
Human error poses the largest risk to your data set’s potential usefulness. If those errors are allowed to slip through the cracks, they can cause even bigger problems down the line in the data labeling process.
Here, teams can enlist the help of a dedicated quality assurance staff to perform regular reviews of questionable data, data with low relevance, outdated data, and other data red flags.
Building a scalable data labeling process might seem like a daunting task, but it’s not impossible. With a clearly defined process, established metrics to measure quality, and the right tools for accurate labeling, data teams can scale up their work with ease.
The future of data labeling — especially when it comes to scalability — relies on the collaboration between human and machine. Learn more about how intelligent data labeling can take your organization’s data analysis to the next level.