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From Data to Knowledge

The representation of the relationships among data, information, knowledge and --ultimately-- wisdom, known as the data pyramid, has long been part of the language of information science.

Digital transformation has made this structure relevant beyond the confines of information science. We all know that COVID-19 has brought years' worth of digital transformation in just a few short months.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive.

So how do we go from data to information, and from information to knowledge?

Data is a collection of facts in a raw or unorganized form, such as numbers or characters. Without context, data does not mean much.

For example, “02122020” is just a sequence of numbers. But if we define this sequence as a date in the DDMMYYY format, we can then interpret it as the 2nd of December, 2020. With this added context, the numbers acquire a meaning.

Information is data that has been processed in a way that makes it easier to measure, visualize and analyze--for a specific purpose.

For example, we can organize our data in a way that exposes relationships between various seemingly disparate and disconnected data points. We can analyze the performance of the Dow Jones index by creating a graph of data points for a particular period of time, based on the data at each day’s closing.

Knowledge is information that has been processed, organized and structured in some way, applied or put into action.

For example, by capturing and expressing the meaning of relationships pertaining to our data points, we can automate insights, and extract new knowledge. A knowledge graph of semantic relationships can help explain how certain stocks influence the Dow Jones index, and how different events may affect their prices.

Adding context to data turns it to information 

Processing information turns it to knowledge

The keys to these transformations are connections and metadata

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