High-quality annotated data is essential to build successful AI and machine learning (ML) models. While industry analysts estimate AI project teams spend about 80% of their time on data-related tasks, this statistic is somewhat misleading. That time doesn’t necessarily reflect the importance that AI teams place on data quality. Instead, much of that time is often spent on inefficient processes, costly rework, and the frustrating reality of training datasets that — even after multiple iterations — fail to deliver the desired model outputs. Having a lot of data isn’t enough. To truly unlock the potential of AI, you need high-quality, accurately annotated data at scale.
To overcome these data challenges and effectively scale data annotation, AI project teams require a robust and strategic approach. This article dives into the key elements of a data annotation strategy and how you can create efficient workflows to fuel your AI initiatives.
Why is data annotation important?
Data annotation enriches raw, unstructured data — such as images, text, audio, or video — to make it understandable and usable for machine learning models. This involves labeling, tagging, or categorizing specific features or objects within the data, incorporating contextual information. That way, it enables AI models to recognize patterns, learn relationships, and make decisions.
Machine learning models, while sophisticated, are not able to determine if data is accurate or valid on their own. Their performance depends entirely on the quality of the training data they are fed and the accuracy of the annotation process that prepares that data. The quality of the training data directly and profoundly impacts the model’s performance. This means that a model trained on poorly annotated data will inevitably produce flawed or unreliable results.
Why is a data annotation strategy essential?
Because data quality is vital to the success of any AI project, AI teams must create and execute an effective data annotation strategy. This strategy must address:
- Data consistency. The data operation team must decide on the best annotation methodology to use for the project. Then, all data must be collected and annotated consistently according to those guidelines.
- Domain coverage. Training data needs adequate volume and must represent real-world operating conditions and “what if” scenarios. Furthermore, the dataset must include a distribution of data that approximates what the model will encounter in the real world.
- Bias mitigation. The data annotation strategy must also include how the team will ensure that training won’t result in bias. The data annotation strategy must include tactics for representing all user demographics and situations.
- Security and privacy requirements. If the project uses sensitive or personally identifiable data, the strategy must include how to protect this data and comply with regulations.
- Storage. Large training datasets require sizeable data storage. Teams need to determine the best way to store, back up, and access data.
- Data annotation resources. AI project teams need enough skilled and trained resources to create datasets and to adjust annotation or augment the dataset with each iteration of the training process.
According to McKinsey Global Institute, 75% of AI and ML projects demand learning datasets to be refreshed once per month, and 24% of AI and ML models require refreshed data daily. A data annotation strategy should define how many human resources are needed, their skill sets, and how they can effectively support the project.
Key elements of a data annotation strategy
- Data preparation and annotation tools: Teams must determine the best tools for data cleansing, classification, and selection. They also need to choose the best tools for annotating the type of data necessary to train the model, whether image, video, audio, text, or tabular. Teams must also explore whether AI-based tools would help them automate processes and work more productively.
- Quality assessments: An effective data annotation strategy must also include quality assessment methodologies to ensure data annotations are accurate and effective at training the model. Quality assessments should be quantitative, using metrics such as precision, recall, F-1 score (for balance between precision and recall), word error rate for speech recognition, and intersection over union for object detection.
- Communication: Your strategy must include how you will ensure open communication with data annotators. A tight feedback loop ensures you will maintain agility and data quality.
- Timeline: A well-planned data annotation strategy will include how to complete phases of the project to ensure the data is ready when needed and won’t create a project bottleneck.
- Budget: Data acquisition, data preparation, annotation tools, human resources, and material resources for the projects all take time and investment. Ensure the strategy will allow the AI project team to complete the project successfully on budget.
AI teams also need to acknowledge that data annotation strategies are project-specific. A data annotation strategy should reflect factors including the problem the AI project will solve, the market the solution is designed for, performance levels the use case requires, and real-world operating conditions.
No two AI solutions are designed for the same purpose. Therefore, no data annotation strategy will translate exactly from one project to another.
How much training data do you need to build out an effective strategy?
A key element of a data annotation strategy is how to determine and produce the right amount of accurately annotated data for the project.
These factors help AI project teams arrive at the perfect balance:
- Performance objectives. The more demanding the performance requirements, the greater the volume and complexity of training data. For example, recognizing products that are consistent in size and shape will take fewer images than training a model for a machine vision system designed to perform quality assurance on detailed electronic components with tight tolerances. An effective data annotation strategy uses performance objectives to guide all decisions.
- Complexity. The more classes the model must address, the more data will be required to train it. Datasets must train the model on the similarities and differences among classes, input features, and each model parameter. Each function can multiply the amount of data needed to train and test the model.
- Variable environmental conditions. AI models that work in controlled environments usually require less data than the same model deployed where conditions change. For example, a computer vision project will require more data if it will operate in different lighting conditions, if cameras capture images from different distances, or if different cameras will take the images. Likewise, a voice project must be able to differentiate between spoken queries and background noise and understand inputs at different volumes.
- Intra-data variability. In some use cases, the data the AI model encounters can vary. Manufacturers may provide products in an assortment of colors, objects may be oriented differently, and people use different tones of voice or accents when they speak.
Additional factors to consider
Another factor to consider is how much original data is available. Data is sometimes difficult to collect – or, in R&D use cases, doesn’t yet exist. Data accessibility may also be restricted due to privacy regulations such as the EU’s General Data Protection Regulation (GDPR) or the U.S.’s Health Insurance Portability and Accountability Act (HIPAA). In these cases, creating datasets with adequate volume, all or in part, with synthetic data can be beneficial.
Teams should also plan for an adequate volume of data for testing and validating the model. This must include ground truth data to verify that the model meets performance standards in real-world conditions.
With some projects, it’s challenging to anticipate how much training data the model will require. Testing can lead to a better understanding of the project’s needs and how to adapt data annotation processes to produce better outcomes.
What are the biggest challenges for AI project teams?
Quality and accuracy at scale
As the demand for AI dataset volumes grows for training or informing the model to make future decisions, internal AI project teams eventually encounter a dilemma: quantity vs. quality. Even though a dataset may require millions of objects, each datum must be labeled correctly for the AI algorithm to learn and deliver desirable outcomes. AI project teams must establish and test quality management processes to ensure annotation at scale doesn’t sacrifice quality.
Human Resources
When projects scale, hiring in-house resources for data annotation is an option. However, it takes new employees months to be able to work independently and meet quality standards.
It may be tempting to assign data annotation to other members of the AI project team in an all-hands-on-deck approach. However, data labeling takes specific skills, such as patience, attention to detail, excellent short-term memory, and the ability to work consistently. Moreover, other team members may not have the temperament for this work – and taking them away from different parts of the project could cause delays.
Speed
Most AI projects are on a deadline. Annotating datasets of millions of data points can easily become the bottleneck that leads to delays or that allows a competitor to take a solution to market more quickly. An effective data annotation strategy includes time and resources for project-specific training and adequate time allocated for annotation itself.
Many companies don’t have the staff to complete large-scale data annotation projects in-house according to their preferred timelines.
Agility
AI project teams must also plan for work beyond the first round of annotation. AI-model building is iterative, and datasets must be updated or modified to refine the outcomes the model delivers.
Automating some processes to support humans, such as validating results, can be an effective strategy for increasing agility as requirements change.
Building a data annotation strategy for your team
Creating an effective, comprehensive data annotation strategy is challenging – but it’s a challenge that Sigma helps AI project teams overcome every day.
With Sigma, you can work with specialists experienced in 2D bounding boxes, landmark and point annotation, semantic segmentation, polygons, voice transcription, name entity recognition, search relevance annotation, and more.
Our approach ensures clients get the high-quality datasets they need to successfully train their ML models. Here’s what you can expect when you work with Sigma for data annotation:
- Project analysis: Our senior team discusses projects with clients and prepares a proposal that addresses their needs. We assign a project manager based on their skill sets and experience with the type of data annotation required.
- Guidelines and requirements: We align with project guidelines, review project requirements, and address objectives and deliverables.
- Tools and procedures setup: The next step is to choose the tools for your project, leveraging AI-assisted tools and testing and quality assurance procedures. The goal is to maximize throughput and quality.
- Comprehensive test: Next, annotators collect data using tools and procedures chosen for the project. We generate reports outlining results and suggestions.
- Client feedback: We share findings with the AI-project team to decide on the best course, whether that’s moving forward as planned or updating guidelines and retesting.
- Annotation/collection at scale: Once the plan is confirmed, we train annotators and move into data collection at scale.
- Quality assessment: We run quality assessments and provide a report on results as well as suggestions for improvements to processes or code.
Get started with Sigma
At Sigma, we are committed to helping AI project teams plan, build effective data annotation strategies, and adapt and scale as their project’s needs change. For more information on building an effective data annotation strategy, download our white paper “Defining the Right Data Strategy.”
Ready to scale data annotation? Contact us to discuss your specific requirements and learn how our expertise and tailored solutions can help you achieve your AI goals.