Supportsoft Glossary

Unstructured Data Processing for Business Intelligence

Unstructured data is any information that has not been defined before and does not conform to a specific standard. Examples of unstructured data include documents, emails, photographs, videos, posts on social media, and voice recordings or audio files. Compared to structured data, unstructured data has additional challenges when it comes to storing, searching, and analysing.

Despite the obstacles of working with unstructured data, this type of data represents an incredible opportunity for a business. Unstructured data offers insight into customers' actions, interests, emotions, attitudes, etc. These insights can often be lost in a structured data source, but processing unstructured data greatly enhances a company's overall business intelligence.

Examples of how unstructured data processing can be used in IT and IT-enabled service environments include document analysis, sentiment analysis, image recognition, and speech processing. Machine-learning algorithms can assist businesses in transforming unstructured data into valuable, usable information by providing sophisticated analytics that can convert "raw" data into actionable insights for organisations.

To properly manage unstructured data, organisations should have data storage solutions designed specifically for the specific types of unstructured data being managed. Creating a data pipeline that collects relevant structured-and-unstructured-data sets into a centralised location, as well as providing appropriate analytical support for both sets, creates a comprehensive view of how the organisation operates.

Business operations will greatly benefit from the combination of structured and unstructured data. By unlocking the untapped value behind unstructured data, organisations can gain in-depth knowledge of their customers, make well-informed business decisions, and be proactive in responding to any shifts in their market's needs.