What the Heck is Data Labeling?

All types of data can tell businesses things they need to know. Data can build an account of production or sales history, or it can produce a record of product defects and service or replacement requirements. Data can also provide insights into customer behaviors, performance against benchmarks, and market share. A human interacting with data can read, see, or hear it. But when you want a machine to interpret data, the process becomes a little more complicated.

Supervised machine learning (ML) requires preprocessed data, which is used to train the ML algorithm or model so it can identify images, content, or patterns. Data labeling, which assigns meaning to data in ways that machine learning technology can understand, is key to creating an effective training data set.

In an image recognition program, you need to teach the machine to recognize, for example, a correctly assembled product vs. an incorrectly assembled one. A training data set can also “teach” autonomous vehicles to recognize people, hazards, signs, and route markers. Or, labeled healthcare images can train a machine learning system to spot cancerous lesions or other evidence of disease.

The Data Labeling Process

Data labeling involves adding an interpretive layer on top of the data, which describes it but doesn’t change it. Accurate interpretations require substantial data of all possible scenarios, and it’s easy to see what a time-consuming, and potentially error-prone process this can be. Fortunately, data labeling platforms make the process easier. There is a wide range of solutions available that enable a labeler to annotate various types of data, such as text, image, video, and audio. Basically, a human reads the text, looks at the image, watches the video, or listens to the audio within the platform. Then, based on the criteria that the machine learning system needs to know, the data labeler identifies words, phrases, images, or sounds and enters labels via the platform, which completes the task of adding the interpretive layer  to the data file.

Efficient and accurate data labeling takes skill. Data labelers need knowledge of the images or the text language they’re labeling. They should also have the flexibility to adapt when changes occur, such as adding new criteria or extending the duration of a labeling project. Data labeling also requires exceptional attention to detail and the ability to maintain consistency in their work.

The data labeling process doesn’t end when the label is created. The essential next step is ensuring accuracy and quality. Data sets must be tested to show how accurately the machine learning system identifies objects or patterns in real-world situations, and, especially when more than one data labeler is working on a project, how consistent labels are. Reliable data labeling is necessary for a machine learning system that will produce desired and reliable results.

The Best Way to Spend Your Time

Although you may be able to use an existing training data set for some machine learning applications in your organization, most of the data your business generates will require labeling. The video from cameras at your facilities isn’t generated with code that tells a computer the types of vehicles, machinery, signage, or objects that appear in it. Data labelers need to do their jobs to communicate that information with a machine learning system.

In its Data Prep and Labeling 2019 report, market research and intelligence organization Cognilytica states that 80 percent of artificial intelligence (AI) project time is spent on data prep and engineering. Your in-house resources probably could spend more than three-quarters of their work time in more productive ways than labeling data.

Options for data labeling beyond using in-house resources include:

• Crowdsourcing: You can engage people through crowdsourcing platforms to label data. This strategy may get fast results — sometimes crowdsourcing can produce labels for a data set in just hours — but you will have little control over the accuracy and consistency of the work.
• Data programming: It’s possible to build scripts that automate data labeling; however, the resulting data set is often subpar compared to one created with human input. Although computers can execute many functions on their own, some require human intervention. Data labeling is one of them.
• Outsource to data labeling companies: In response to organizations’ growing data labeling needs, firms specializing in data labeling offer reliable services and quality data sets.

When you work with data labelers outside your organization, take measures to ensure the security and privacy of your data. Ensure their workforce has undergone background checks and have nondisclosure agreements that prohibit sharing your data with other parties.

The New Normal

As machine learning and other forms of artificial intelligence (AI) continue to offer businesses and organizations more capabilities and new insights, data labeling will become a standard, essential task in those operations.

What data are you collecting that could benefit your business with insights that lead to greater efficiency, competitiveness, and ability to innovate? And how will you tackle the task of getting that data ready to produce that value?