Top 5 Challenges Making Data Labeling Ineffective

As enterprises across the verticals, from entertainment to agriculture and from retail to robotics, rush to apply AI to their business practices, they repeatedly trip up over one obstacle: efficient data labeling at scale.

At Dataloop, we’ve seen enterprise after enterprise flounder when faced with the need to produce usable data. They have no shortage of raw data; on the contrary, companies have a glut of data, with more of it flooding in all the time. Vast amounts of images from cameras, sensors and other equipment are collected by organizations at any given moment. The challenge, however, is how to process and label this data in order to make it usable.

Accurately labeled data ensures that ML systems establish reliable models for pattern recognition, which forms the foundations of every AI project, but applying complex ontologies, attributes, and various annotation types to train and deploy deep learning and machine learning models takes up to 80% of AI project time. 19% of businesses say that lack of data and data quality issues are their main obstacles to AI adoption. Time and again, we’ve seen data with labels that are lacking in accuracy, quality, or both undermine an entire, highly-complex AI-based project by invalidating predictive models.

The majority of organizations struggling with AI and ML projects say that their biggest problems concern data quality, data labeling, and building model confidence. We’ve identified 5 primary factors that lie at the foundation of these problems, holding enterprises back from efficient data labeling:

  • Workforce management
  • Dataset quality
  • Financial obstacles
  • Data privacy
  • Smart tooling

Understanding the roots of these data labeling challenges is the first step to solving them and improving AI project success rates.

1. The challenge of workforce management

Successful data labeling is a workforce challenge for two reasons: the need to manage enough workers to process the massive volume of unstructured data, and the need to ensure high quality across such a large workforce.

Data labeling is a high volume task, but quality matters just as much as quantity. Enterprises have to perform a tricky balancing act between expanding their workforce quickly, and training and managing such a large and disparate group. We’ve seen successful startup teams and even enterprises begin by managing data labeling and other data processing needs in-house. This works, but only as long as datasets remain a manageable size.

As companies grow, the labeling workload grows too. As we’ve observed, it’s logical to seek larger external data labeling workforces, but a larger workforce brings new issues:

  • Training many labelers for their tasks.
  • Distributing work seamlessly across large, varied teams and dividing tasks up into individual assignments.
  • Tracking individual progress without losing track of the project as a whole.
  • Ensuring smooth communication and collaboration between labelers and data scientist(s) to maintain quality control, validate data, and resolve workforce issues.
  • Overcoming language, geographic, and cultural barriers between labelers who might fail to annotate data correctly because they’ll miss certain cultural cues.

2. Managing consistent dataset quality

It’s obvious that good data rests on high dataset quality, but this brings its own challenges. Companies need to find ways to make sure that labelers have the capabilities to produce consistent dataset quality.

In our experience, there are two main types of dataset quality — subjective and objective — and they can both give rise to data quality issues.

Subjective data

This concerns how to define the label in cases where there’s no single source of truth. We frequently see how the labeler’s domain expertise, language, geography, and cultural associations can all influence the way that they interpret the data before them.

For example, there’s no conclusive answer to whether a given video scene is “funny.” Not only will each data labeler give a different answer, based on their own biases, personal history, and culture; the same labeler might give a different answer when they repeat the task in the future.

Because there’s no single “correct” answer for subjective data, the data ops team needs to set clear instructions to guide how the workforce understands each data point.

Objective data

Unlike subjective data, objective data does have a single correct answer, but it still presents challenges. For a start, there’s a risk that the labeler might not have the domain expertise needed to answer the question correctly. For example when labeling leaves, will they have enough expert knowledge to identify them as healthy or diseased? Additionally, without good directions, labelers might not know how to label each piece of data, like whether a car should be labeled as a single entity “car,” or if each part of the car should be labeled separately.

Finally, it’s impossible to entirely eradicate human error, no matter how good your dataset quality verification system.

This leaves data ops teams to find ways to resolve both subjective and objective quality issues by setting up a closed-loop feedback process that checks for errors.

3. Keeping track of financial cost

We’ve repeatedly encountered companies that struggle to budget correctly for data labeling in the absence of any standard pricing or established metrics. When asked why their AI projects are failing, 26% of enterprises blamed a lack of budget. Without metrics, responsible monitoring, and objective standards for data labeling success, companies are limited in their ability to track results in relation to time spent on work. We’ve often noted a lack of transparency into exactly what enterprises are paying for in their data labeling projects, whether it’s in-house or contracted out.

Organizations that outsource data labeling generally need to choose between paying for data labeling per hour, or per task. Paying per task is more cost effective, but it incentivizes rushed work as labelers try to get more tasks done in a given timeframe. In our experience, most enterprises prefer to pay per hour.

Small, in-house manual data labeling teams run expensive, due to the time and training needed to reach true expertise. As data quantity grows exponentially, prices are growing too, and we know that it’s impossible to predict the final volume of data for processing.

4. Complying with data privacy requirements

GDPR, DPA and CCPA are just the tip of the iceberg. Data confidentiality compliance regulations are multiplying worldwide, at the same time as organizations are gathering even more data. When it comes to labeling unstructured data, this includes personal data such as faces; license plates; and any other identifying data which could appear in images.

Enterprises are obligated to comply with principles like “processing data lawfully, fairly and in a transparent manner in relation to the subject matter.” They need to ensure that their data is secure, preventing workers from accessing it from an insecure device, downloading and transferring it to an unknown storage location, or working on data in a public location where it could be viewed by someone without security clearance.

In practice, we’ve often seen that data has to be managed and stored on premises and accessed only from approved devices. We realize that this makes it it challenging for organizations dealing with sensitive data, or that must comply to regulations, to outsource tasks to third party data labeling providers. It limits where employees can work, and adds yet another layer of complexity to workforce management. When you’re buying a labeling service, the data you use still has to comply with the regulations and the organization you work with needs to be compliant. It’s therefore essential that you both know what laws you need to follow as well as how to ensure they’re being followed.

5. Maintaining smart tooling at scale

High quality data relies on a combination of well-trained workers and smart tooling such as AI-assisted data annotation, automation, data management and data pipelines. We see that as AI reaches more domains and is expected to understand more human tasks, tooling requirements keep rising.

In our experience, organizations that begin with tools built in-house often discover that their annotation needs keep growing, so they have to work harder than expected to keep up. In-house tooling demands a great deal of both money and time to keep on supporting and extending the software, and over time, this level of investment in a tool that isn’t core to the business distorts their focus as a company.

At the end of the day, when it’s a question of build or buy, you need to consider the full impact of your decision. As stated above, building an internal tool means risking paying over the odds in terms of time, cost of going to market, and continual maintenance so you don’t fall behind. When it comes to buying, you need to consider whether the tools you select provide all the services that you’re seeking. That’s why it’s critical you find a platform that is robust enough to evolve with your projects, but also mature enough to ensure stability.

The right platform helps overcome data labeling challenges

As we’ve repeatedly discovered, data labeling underpins success in every AI project, making it vital to look for a platform that can help you to overcome these 5 most common data labeling challenges. Dataloop steps up to the plate to provide solutions for each issue. We grasp the critical role that effective data labeling plays for your organization, and have a deep understanding of the obstacles that lie in your way. That’s why we focus strongly on resolving these 5 data labeling challenges. 

Our proprietary platform integrates human and machine intelligence with cross-functional collaboration, closed loop feedback, and high data standards to ensure quality datasets, reduce the challenge of workforce management, and lower the costs of data labeling. Custom authentication and permission controls, military grade encryption, and single point of access keep your data confidential and secure, while our auto-scaling infrastructure is powerful and versatile enough to keep up with your growing data operations as your business scales.

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