There is a big difference between knowledge and wisdom. Knowledge can be observed as the ability to retain facts, while wisdom is having the foresight and understanding to use said facts accordingly. The Wisdom Evolution Trend, otherwise known as the DIKW model (Data, Information, Knowledge, Wisdom), is a very important element 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, subsequently, have the wisdom to make wise decisions…if the data is analysed correctly.
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, and have fantastic algorithms and a huge capability to analyse data and move it along towards information and knowledge.
However, knowledge and wisdom are very different things. 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.
This analogy can be taken further when referring to data collection. We live in a Knowledge Economy, if you will, with all the worlds knowledge at our fingertips. Yet, despite this, we are drowning in knowledge, 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 rich, and therefore extremely useful, as it typically comes from customers and employees and, therefore, provides accounts of their experiences with a product/service/ employment.
To make sense of this knowledge (experience), and extract value from this rich data source, you first need the capability to analyse text- as knowledge is predominantly recorded as text. Recent advances in technology, in particular Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (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 AI, ML, and NLP in more depth!