There is a big difference between knowledge and wisdom. Knowledge can be observed as the ability to retain facts. Wisdom is having the foresight and understanding to use these facts accordingly. The DIKW model (Data, Information, Knowledge, Wisdom), is very important to both quantitative and qualitative data analysis. From data we have information, and from information we have knowledge. It is from this knowledge that we have the wisdom to make wise decisions.
If the data is analysed correctly, that is.
The Building Blocks
At the beginning of the data journey numbers and figures usually take precedence. Initially, data analysts input these numbers into spreadsheets, or the like. They have fantastic algorithms and a vast capability to analyse data, and move it along towards information and knowledge.
Knowledge is useful, but what we want is wisdom. Knowledge is knowing that Fukushima nuclear power plant sits on a massive geological fault line. This being the Pacific ring of fire. Wisdom is knowing not to build a Nuclear power plant 30 feet above sea level when there is a 200-year history of 50-foot waves.
We live in a Knowledge Economy, with all the worlds knowledge at our fingertips. Yet, despite having the information, we are drowning in data, whilst facing a Tsunami of problems.
Organisations typically sit on terabytes of unstructured text. This text is called unstructured text because no one really knows what information is contained within. It is extremely useful as it typically comes from customers and employees. It, therefore, provides accounts of their experiences with either a product/service/ or employment.
To make sense of this knowledge (experience), and extract value from this rich data source, you first need the capability to analyse text. Recent advances in technology, in particular AI, ML, and NLP, has given many Tech companies a platform, and the potential capability to make sense of unstructured text.
But if only it were that easy. Artificial Intelligence, Natural Language Processing and Machine Learning is a fantastic technology. But it is simply a technology, not a solution. It is how companies turn it into a solution that is the critical element that needs to be understood.
To help you through that understanding, take a look at next week’s article, where we look into the practical elements of attaining wisdom from your data.