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5 Factors that Make Data Labeling Ineffective

5 Factors that Make Data Labeling Ineffective

A significant hurdle AI/ML projects face is the need to have high-quality data available all the time.

While there is no shortage of raw data, the need to produce usable data remains a big challenge.

  • Automotive companies generate tons of data from cameras, sensors, and other equipment.
  • Financial organizations create a lot of user data every day.
  • Medical and pharma companies have tons of patient and scientific data available at their disposal.
The challenge, however, is how to process and label this data to make it usable.

In this post, we will look at five factors that affect data labeling.

Data quality is a big concern

Accurately labeled data ensures that your ML systems establish reliable models to recognize patterns in data, which forms the foundations of every AI project.

Nearly 80% of your ML project time is consumed in applying complex ontologies, attributes, and various annotation types to train and deploy deep learning and machine learning models.

19% of businesses say that lack of data and data quality issues are their primary obstacles to AI adoption.

Encountering data with low labeling quality is a common obstacle that undermines your complex AI-based project by invalidating predictive models.

Many organizations enthusiastic about leveraging data to build better products and services say that their most significant problems concern data quality, data labeling, and building model confidence.

If you look closer at the foundations of data labeling, five factors affect enterprises in producing high-quality data.
  1. Managing your labeling workforce.
  2. Best practices and skills.
  3. Your machine learning project budget.
  4. Protecting your data.
  5. Data labeling tools.

1 - Managing your labeling workforce

Data labeling is a high-volume task. It would be best to have a team of data labelers and annotators trained on the labeling standards and the tools used for labeling.

Maintaining and motivating a data labeling workforce is challenging for two reasons:
  • One—the constant demand for high-quality labeling throughout the project can be challenging and laborious.
  • Two—the data labeling requirements change as you move along training, testing, and validating your machine learning models. Your labelers should acclimate to delivering the amount of data required during all three phases.


As you move along managing multiple ML projects, you need a bigger labeling workforce. The logical solution would be to hire an external workforce—a data labeling partner.

Hiring a data labeling partner is also a challenging task, but if done well, you can achieve two things:
  • One—Reduce your data labeling time,
  • Two—And reduce your overall ML project budget.

2 - Labeling best practices and skills

The two types of data labeling bring their challenges.

Type 1 - subjective data labeling

When there is no single source of truth to guide your labeling, data labeling quality can vary.

For example: labeling videos or movie scenes as funny or not funny. Every labeler brings their definition of what's funny and what isn't.

Type 2 - Objective data labeling

Unlike subjective data, objective data does have a single correct answer, but it still presents challenges.
Objective data labeling can go wrong and affect the labeling quality if your workforce has no expertise in the subject matter.

For example: labeling agricultural images to detect diseases in a specific type of leaf. If your workforce cannot distinguish between a dry leaf, a healthy leaf, and a diseased leaf, then your labeling quality won't be good.


When you hire labelers internally or seek to sign up a data labeling partner, verify their expertise in delivering data labeling in the subject matter.

3 - Your machine learning budget

Inability to assess the workload, workforce, tools, timeline leads to incorrect budgeting.

26% of enterprises say their machine learning projects stall due to a lack of budget.

Other practices that can outrun your budget:
  • lack of constant monitoring throughout the project,
  • lack of communication between labelers, data engineers, ML engineers, project leads, etc.
  • lack of transparency in pricing if you choose external partners to aid your ML projects.


Establish best practices and standards that require your internal and external stakeholders to understand and adhere to easily.

Establish a simple, frequent communication standard to address any friction and find solutions daily.

Negotiate for a transparent pricing policy with external vendors where you pay for only what you get.

4 - Protecting your data

Labeling customer data such as faces, health records, vehicle license plates, etc., requires you to follow multiple layers of privacy regulations.

Hiring data-labeling contractors from other countries demands another layer of privacy adherence.

Region-specific privacy regulations such as GDPR, DPA, and CCPA add to the complexity.

Protecting your data may restrict you from hiring local contractors, which means you have to pay more than outsourcing data labeling to vendors in countries like India and the Philippines.

Another restriction is that you may be forced to host your data locally if you are working with sensitive data.


Understand and create a privacy best practice while assessing your machine learning project timeline and budgeting. Hire contractors and vendors who are certified and comply with the latest security standards.

5 - Scaling labeling tools

Scaling your data labeling requirements is possible if your labelers are skilled, experienced, and are AI-assisted.

It would help if you combined people power and machine power to scale your data labeling quickly and cost-effectively.

One way to speed up the labeling process is via AI-assisted data labeling. As human data labelers label a small set of data, an AI-assisted data labeling tool learns the patterns and conventions of data labeling. It can independently start labeling data at a tremendous speed.

With a bit of supervision, you could get an AI-assisted tool and a bunch of data annotators to label all your data in quick time.

Further reading: Understand the difference between Manual data labeling and Automated data labeling

Many organizations feel that they need to build a custom data labeling tool from scratch if they believe their labeling requirement is unique or dealing with sensitive data.

The challenge with building a labeling tool in-house is investing time and money to develop the tool. On top of that, you need to manage and support the tool throughout your ML project.

In most cases, you can do well by choosing a ready-made labeling tool and paying for what you get. Ready-made labeling tools can be scaled quickly and cost-effectively if you negotiate for a transparent pricing model. And the companies that build ready-made data labeling tools offer great support as well.


If you have a large enough workforce trained in labeling standards and tools quickly, you may choose a ready-made AI-assisted labeling tool.

If you have to outsource data labeling, choose a partner who offers both the labeling tool and trained labelers and annotators.

We at Traindata are a team of Ex-Yahoo!s with over 15 years of experience in data preparation for AI and ML projects.

We have a team of skilled data annotators and labelers experienced in using the latest labeling tools.

To get your data labeled and annotated securely, in quick time, and on budget, write to us now at [email protected] or visit www.traindata.us to learn more.