Master Data Management

Factors to consider while choosing the right Data Annotation Tool

The data annotation you deploy for training Machine Learning (ML) algorithms can be a critical factor for the success of your intelligent automation. The importance of data annotation cannot be stressed enough because of its role in training ML algorithms. Data annotation is essentially tagging, transcribing, or labelling specific features in datasets to train the ML system. ML algorithms are trained to identify specific aspects of data with the help of annotation tools. The training enables these algorithms to discern the same elements in the data sets that have not been annotated.

Data annotation tools, which may be cloud-based, on-premise, or containerized software, help in the annotation of data. There are specific data annotation tools for different types of data like images, video, text, audio, spreadsheet, etc.

There are multiple data annotation tools available in the market. Each tool is designed with a specific set of features. Choosing the best annotation tool for your business can be difficult.

Here’s a guide for choosing the right data tool

The features of the tools need to be assessed in depth. Here are some aspects you need to consider:

1. Managing Datasets

An important factor to consider while choosing a data annotation tool is its capability to support the volume of data and file types that need to be annotated for your application.

The tool must have the capacity to search, filter, clone, sort, and merge datasets.

Make sure the tool can provide outputs that meet the specific needs of your team. Also, the tool must be compatible with the file storage systems you have.

2. Annotation Techniques

Annotation tools differ in the methods used for annotations. Some specialised tools can annotate only specific types of labelling. On the other hand, some vendors offer generalised tools for annotating a variety of use cases. Based on the current and future needs of your organisation, you can select a specialised or generalised type of tool. Based on the current and future needs of your organisation, you can select a specialised or generalised type of tool.

Some examples of annotation techniques

Image Annotation: Image annotation tools can identify what the image is about. The ML algorithm can identify a single object in the image with multiple elements or it can separate different elements in one image. Image annotation tools offer labelling because of features like bounding boxes, cuboids, polygons, etc.

Video annotation: Since videos are essentially a series of images, a typical video annotation tool includes all features of image annotation. Expert users  recommend the use of features such as scene classification and object tracking features as well.

Text Annotation: Text annotation tools leverage Natural Language Processing (NLP) to understand the themes, sentiments, and phrases from text written in natural language.

Audio Annotation: These tools enable ML models to understand speech. The speech is then transcribed into text which is annotated with NLP. Audio annotation tools must have audio transcription and word/phrase tagging features.

3. Workforce Management

All data annotation tools require human intervention. The role of humans becomes especially significant for handling exceptions and quality assurance.

Annotation tools with features that offer workforce management are also available. Such tools can determine productivity based on the time spent on each task/sub-task and optimise productivity with effective task assignments.

Many vendors offer the services of annotators to clients. You also have the option to select members from within your existing workforce to perform the task.

Since humans play a vital role in the data annotation process, it is a good idea to hire a workforce that is open to learning and can be trained to use the tool.

4. Security

Security is a vital aspect of data annotation tools. It is recommended that you verify the security features in the tool. Some standard security features to confirm are secure file access to users, restricted viewing rights to data, etc.

The next area to focus on is the specific requirements of your organisation. This will enable you to select the tool that is the right fit for you.

1. There are different tools for annotating text, images, video, or audio content. While some vendors offer the tool for the annotation of specific types of data, others provide a consolidated platform that can annotate various types of data.
2. Some tools have features that enrich data while there are others that not only support data annotation but also the AI development process.
3. Certain tools support multiple storage options. They may be compatible with cloud, network, or local storage. Make sure that you choose the tool that matches your storage systems.
4. Another factor that needs to be considered is the future requirements of your business. It is a good idea to select a versatile tool that is flexible and offers scalability.

The most popular data tools available in the market are:

Commercially-viable tools which are ideal for organisations operating at scale or looking to sustain. These tools have the flexibility to be customised to fulfil the specific requirements of your business.

Open source tools that allow modification of the source code to customise the tool according to the needs of your business. Open-source tools offer great flexibility because you have control over them and can make any modification at any stage.

These are some vital factors to consider while choosing the right data annotation tool for your business. However, just having the right tools is not sufficient, you will also need data scientists, skilled labourers, etc. to support annotation. Hence, it is recommended that you engage/train your workforce to harness the tools efficiently for better outcomes.

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