Dealing with big data comes with a number of challenges for organisations. Very often, this data is unstructured and disorganized, making it very difficult to find and work with specific information when you need it. For these reasons, many companies are now turning to outsourcing their data to external services for analysis and categorization. One such organisational tool that is being used very effectively to help categorize data is data labeling. Read on to find out what it is and why you should consider it for your organisation.

What is data labeling?

Data labeling is the process of applying semantic tags to data, generally done for easier recognition and sorting. This has a wide range of applications across many industries that have to sort and store vast quantities of data, from healthcare to banking and finance, manufacturing, retail, and plenty more.

Without data labeling, data may be disorganized and unstructured. This makes it difficult to locate and difficult to work with, as unstructured data is hard to search and sort, particularly at scale. This is why many organisations turn to data labeling services to help turn their unstructured data into categorized, actionable data.

Why is data labeling necessary?

There are both regulatory and practical reasons for organisations to seek out data labeling solutions. Legislation such as GDPR and CCPA put a requirement on every organisation to keep customer and client data secure. In addition, organisations must make all data held on an individual available upon request. Other industries such as banking and healthcare may have even stricter requirements about data gathering and storage.

If data is stored in an unsorted and unstructured manner, then even simply finding all of the data on an individual customer can present a challenge. Beyond regulatory requirements, a lot of this gathered data could be analysed and used to help improve and refine business processes. Research suggests that up to 80% of data gathered by organisations is unstructured and underutilized. This represents both a cost to organisations to store the data and a missed opportunity to analyse it for potentially valuable information.

How does AI help power data labeling?

Working with data at scale presents several technical and practical challenges for organisations. Large organisations like banks and healthcare providers are required to sort and store data they hold according to very strict regulations. Data must be sorted and stored correctly, but the amount of data generated means that human annotators often simply cannot keep up with the pace of data being created and stored. However, leaving data unstructured simply makes it harder to find and process even when time is found.

Practical uses of AI data labeling

Data scientists use data labeling to understand, enrich and improve data quality. Labels can be applied to any type of raw data, from basic text to images, videos, audio files, emails, spreadsheets, presentations, and plenty more. These labels provide additional context and can contain information about nearly anything, from describing what is in a picture to attributing author information or annotating key phrases in an audio clip.

Artificial intelligence can be leveraged to make this contextual information much more powerful and useful. For example, AI models can be trained to understand and extract key parts of a standard business contract automatically. The related entities in a contract may be automatically identified and then linked to other information held about those entities by the organisation, such as linking a newly signed contract to a customer’s profile held in a CRM system. This enriches data and also takes care of data association without extra effort on behalf of the organisation.

Unlocking data potential with data labeling

Data labeling is a relatively simple idea of applying meaningful labels to data that helps to provide additional context. This in itself is useful, but augmenting this with AI opens up many more possibilities for organisations to make practical use of the data they store. From automatically creating associations and relationships between stored data to being able to quickly retrieve and make use of relevant data at a moment’s notice, data labelling provides a backbone to organisational data.

Much of this data is already being stored by organizations, but it is not being effectively utilized. Taking advantage of AI processes to automatically sort, label and organize data not only helps find data, but it can also uncover surprising insights and relationships between data that are not otherwise apparent.