The Wisdom Evolution Trend, otherwise known as the DIKW model (Data, Information, Knowledge, Wisdom), is very important and relevant to both quantitative and qualitative data analysis. The idea is that whatever you are working with, you need to aim and progress towards obtaining wisdom. Insight sits somewhere between knowledge and wisdom. From data we have information, and from information we then have knowledge. It is from knowledge that we subsequently have the wisdom to make wise decisions.
The Basic Building Blocks
At the beginning of the data journey numbers and figures take precedence. Initially we, the data analysts, input all these numbers into spreadsheets, and this is a great tool that everyone can use. These days, however, we 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. For instance, 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 in that we live in the Knowledge Economy, if you will, with all the worlds knowledge at our fingertips. We are, however, 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 it contains. It is rich, however, because this text data typically comes from customers and employees and is often an account of their experiences with a product, service or their 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!