Data annotation refers to the process of assigning labels to data, in order to allow AI-driven systems to recognise that data type automatically when they encounter it in the future. Different types of data can be annotated in this way. Document annotation, image annotation and video annotation are all examples. Data annotation makes it possible for algorithms to sort and categorise large amounts of data automatically. There are various ways that data annotation can be performed, depending on factors such as the data type, its complexity and the available resources.

Data Annotation Methods

Internal annotation

In some cases, an organisation may assign the task of annotating data to its own staff. This is known as internal annotation or internal labelling. It has some advantages but is generally only appropriate for large organisations. The amount of time, resources and labour involved make internal annotation impractical for small and mid-sized businesses.

Outsourcing

If internal data annotation isn't possible, the task can be outsourced to another company. This is often the best option for smaller organisations, who may not have the workforce to tackle a large data annotation project.

Crowdsourcing

Many companies find it effective to crowdsource data annotation, recruiting large numbers of individuals to perform data annotation on a per-task basis. This can be efficient and cost-effective, although the quality can be variable.

Synthetic Annotation

Synthetic data annotation has various advantages, particularly the fact that it can generate new data based on previous data sets. It doesn't require as much human labour as other forms of data annotation. The main disadvantage of synthetic annotation is the amount of computing power required, which can be expensive.

Programmatic Annotation

Programmatic annotation uses automated scripts to detect data and annotate it. This can be a very efficient approach. Programmatic annotation is prone to error and requires human involvement to address this. Even so, it can dramatically cut the amount of time and labour required for a project.

Data Annotation in Action

Data annotation has a range of benefits and applications, making it hugely important for companies that want to remain competitive in an increasingly data-driven world. Organisations are increasingly reliant on artificial intelligence for the execution of routine tasks. Customer service, quality control and even product development can all be greatly facilitated through the use of automated systems. Without quality data annotation, this would be impossible.

One area in which artificial intelligence is playing an ever-greater role is in knowledge management. Businesses today generate enormous quantities of data in various forms, from emails between staff members to bills and invoices, as well as information from outside the organisation and its vendors such as customer feedback.

Organising all this information in a meaningful and useable way is a Herculean task. With a quality document annotation tool, many aspects of knowledge management can be handled by artificial intelligence. For instance, a machine learning model can be trained to recognise and distinguish various types of documents (invoices, emails, maintenance reports etc) and ensure that they're stored in the correct location. AI can also facilitate retrieval by the continual refinement of search functions.

Through this kind of automation, time can be saved, waste can be reduced and skilled personnel can be freed up to perform tasks that require human intervention. This all requires effective data annotation as the starting point for machine learning.

Data Annotation and Your Business

Before starting any data annotation project, it's important to establish clear goals and formulate a meaningful strategy. Without this initial step, it might be necessary to change parameters and revise the annotation project after it's already underway. It may be useful to consult with experts on data annotation to determine your aims and how to address them.

The next step is to determine how you're going to carry the project out. For large organisations with an extensive pool of IT staff, it may be desirable to carry out the work in-house. In other cases, it's a good idea to outsource the work to an expert data annotation provider. If you're planning to perform the work in-house, investing in a quality data annotation tool can be a wise move.

When addressing any data annotation project, security needs to be at the forefront. Determining how you're going to ensure that personal information, financial data and other sensitive materials are protected should be a primary concern.

Takeaway

Document annotation and other forms of data annotation are increasingly crucial to modern businesses. Establishing data annotation strategies is something that every business needs to do. It's important to have clear goals and invest wisely in data annotation technology, such as a document annotation tool platform.