What is data liquidity?

There are many aspects of data that are important to keep in mind when building a data-driven organization. One of them is data liquidity.

When talking about creating value from data, we often think about data sharing and data monetization. However, before we can share or monetize the data, we need to be able to access and exchange it in a standardized, secure, and scalable way. This can be defined as data liquidity.

One of the most important aspects of data liquidity is the ability to access data in a standardized way. This means that the data should be made available in a format that is easy to understand and manipulate by both humans and machines and that it should be easy to integrate the data with other data sources.

Through the process of combining data from multiple sources, we can create new insights and value that would not be possible to create from a single data source. This is why data liquidity becomes increasingly important for organizations that seek to become more data-driven.

See also

• Navigating unstructured data: The rise of question answering - Question answering technologies are key to efficiently dealing with overwhelming amounts of unstructured data.
• Data discovery at Uber: The continued success of Databook - How Uber's in-house platform powers discovery, exploration, and knowledge at scale.
• Linked data for the enterprise: Focus on Bayer's corporate asset register - An overview of COLID, the data asset management platform built using semantic technologies.
• Document understanding: Modern techniques and real-world applications - How document understanding helps bring order to unstructured data.