From data to knowledge and AI via graphs: Technology to support a knowledge-based economy
These past few months have not been kind to any of us. The ripples caused by the COVID-19 crisis are felt far and wide, and the world’s economies have taken a staggering blow. As with most things in life, however, this crisis has also brought some interesting side effects.
The way to cope for practically every one of us, including organizations of all sizes and shapes, can be summarized in one infamous buzzword: digital transformation. This can mean different things depending on who you ask, from video calls and remote collaboration apps to more software as a service, cloud and machine learning investment.
Whatever digital transformation means, as the aphorism by Microsoft CEO Satya Nadella went, the COVID-19 crisis brought years worth of digital transformation in months. Those who had strategically invested are reaping the benefits, those who did not are either out of the game or left with a patchwork of disparate initiatives and apps in place.
There’s one thing in common however, regardless of which part in that journey you’re at: as digital transformation accelerates, an increasing part of all business activity is leaving its footprint behind in the form of data. Eventually, every employee, customer and supplier interaction, every lead, every information bit and every process, will either take place digitally or be documented digitally.
What this means in turn is that in theory, it should be possible to not just derive insights, as the promise of big data and analytics has been, but to go from data to information, and from information to knowledge.
Yes, this does sound a lot like the journey towards the fourth industrial revolution, and the promise of artificial intelligence. In the near future, organizations will be data-driven, and the economy will be knowledge-based. Here’s a shortlist of technologies and processes that can support it, and what they are about.
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. 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, “18122020” 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 18th 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. But there is another well-known progression at play here.
By now, the classification of various forms of analytics from simple to advanced is also well known and understood.
