“Knowledge is knowing that tomatoes are fruits; wisdom is knowing not to put them in fruit salads,” said late British columnist Miles Kington. What’s knowledge and what’s wisdom in the information age? How can we leverage information to create knowledge and then scale that knowledge up to the wisdom of smart decisions and actions?
The path to wisdom begins at the data campsite, winds up the mountain of information and knowledge and finally reaches the wisdom peak which gives climbers a clear view of the area around them.
Going up the slope requires taking steps to turn data into information, information into knowledge, and knowledge into wisdom. In the information age, creating links between data, inferring new knowledge out of existing facts, and applying predictive models and taking data-backed business decisions is crucial for organizations.
Linked Data and semantic technology help them do that by smoothly integrating heterogeneous data from various sources and applying universal standards for usage. Semantic technology, the semantic graph database in particular, is able to infer new relationships out of existing facts, giving context and meaning to the links from many disparate sources. Having obtained that new knowledge, organizations gain a competitive advantage and support business decisions with facts which their semantic graph database has revealed to them.
Now let’s break the DIKW (Data, Information, Knowledge, Wisdom) hierarchy down to its building blocks and see the scaling up to the wisdom peak step by step.
Data is our base building block and the starting point of every wisdom value chain. Data represents the raw sources and resources, facts expressing the world around us in the form of words, numbers, signs and signals. Data loads and datasets are enormous and most disparate and unstructured. They are surely valuable, being the primary resource, but what’s more valuable is the data analytics, processing and linking.
That leads us to information: the processed and analyzed data which adds meaning to datasets. For example, enlisting Google’s closing prices on the stock market in the past ten trading days is data. Drawing a chart to show the trend in Google’s stock market price of the past ten days is information.
At this second building block of our pyramid, Linked Data, that is the link between various datasets of raw disparate and diverse facts, helps organizations get a clearer picture of their data. This allows them to easily store, search and retrieve the information they need.
The storage and usage of Linked Data and Linked Open Data (LOD) is being done by a graph database, also referred to as a semantic graph database. Such graph db not only represents the links between datasets, it is also able to infer new knowledge out of existing facts and discover new relationships.
Graph databases place the information within context and give meaning to the links from many sources to create knowledge, our third campsite on the path up to the wisdom peak.
Semantic technology ‘teaches’ computers to use inference in order to create knowledge by revealing hidden relationships which are not included in the original dataset. Inference is machines generating new relationships using the original dataset and additional information in the form of a vocabulary, schema, logical models or ontology.
For example, if the original dataset contains the statement ‘Flipper is a dolphin’ and an ontology defines the concept ‘every dolphin is also a mammal’, semantic technology ‘learns’ to make that connection which has been logical only to humans, and discovers the relationship ‘Flipper is a mammal’ which was not in the original dataset.
Extracting knowledge moves us up the value chain of data and information. The organizations which gain new insights out of their datasets and out of Open Data are a little further up the path towards the wisdom peak than enterprises that rely on just crunching the numbers.
Once organizations have gained insights, they have more resources and options to make data-driven decisions and employ predictive models proactively. This leads us to the applying of the knowledge gained or inferred to create smart decisions to the advantage of both the organizations themselves and society as a whole. We’ve reached the wisdom peak.
Whereas data and information are gathering and learning, a kind of look to the past, knowledge and wisdom are associated with ‘doing now’ and a look to the future. Knowledge, in terms of Linked Data and semantic technology, is creating meaningful interlinks which the competitors may not have because interrelations have been inferred. Wisdom is determining what outcome a decision may have and what value it will add to the business or the world.
Smart cities using and/or promoting the usage of Open Data are an example of wisdom for the greater good. Opening up city datasets boosts public services efficiency and increases transparency and citizen control. Giving users and developers the opportunity to work with open data creates new business models and spurs innovation, thus adding value to the knowledge economy.
For instance, Transport for London has released open data for developers to use in their own software and services. TfL is encouraging developers to use the feeds, and they have, creating hundreds of apps, including such for Tube travel news updated every minute, or personalized journey planning tools for public transport.
The New York City Fire Department uses a predictive analytics model to track which NYC buildings are at the highest risk of fire. The smart analytics model creates scores for buildings based on an algorithm of around 60 factors – including the age of a building, electrical issues, the number of sprinklers, and the presence of elevators. The NYC Fire Department targets inspections to buildings with the highest risk of fire, based on the scores.
To sum up, we can say that data and information answer the questions ‘who’, ‘what’, ‘when’ and ‘where’. Going up the mountain of wisdom, context and understanding increase. Knowledge holds the answer to the ‘why’ question, while wisdom is about ‘how to’, ‘what comes next’, and ‘what is best’.
So you’ve learned that tomato is a type of fruit because of its characteristics of a plant, but you predict it will not go well with bananas and apples in a fruit salad. Applied to business in the information age, the analogy goes like ‘we’ve had the facts, crunched the numbers, created links and inferred new knowledge; therefore, we have a vision for a future action that will be beneficial for the organization and the world.’