Data contextualization: From Isolation to Integration

Data silos are isolated repositories of data that are controlled and not easily accessible by other parts of an organization. Still, it doesn’t mean that one department is hiding information from the others, but, in most cases, the data in hands is incompatible with other systems and data sets.

According to The Journal of Petroleum Technology, 80% of employee time in the offshore industry is spent looking through unstructured data to inform decisions. Indeed, as organizations grow, different departments develop their own systems and processes, leading to isolated data storage. Employees may not have direct access to all necessary information, requiring multiple steps or special permissions to retrieve data, which adds to the time spent.

IDC Market Research states that

Companies can lose up to 30% of revenue annually due to poor-quality data stored by data silos.

These can be particularly challenging when dealing with massive amounts of historical data, as they represent isolated pockets of information, often leading to hindered collaboration, difficulties in data analysis, and duplicated or incomplete information. In the offshore industry, collaboration is crucial for making informed decisions about operations, safety, and maintenance. Employees need to aggregate data from various silos to create a comprehensive view, which is time-consuming.

For this reason, data contextualization comes as an opportunity to ensure seamless data connectivity and unified processes within an organization. Data contextualization refers to the process of adding context to raw data to make it meaningful and useful for analysis and decision-making. It involves understanding and interpreting data within the framework of its origin, environment, and relevance to specific tasks or objectives.

Giving context to unstructured data can be represented by integrating all data into a unique digital ecosystem. Thus, all areas of a company can leverage holistic data analytics with a cohesive and accessible knowledge base. Data contextualization enhances decision-making by providing a clearer, more complete picture of the situation at hand, allowing for more accurate analyses and predictions.