DIKW and Big Data
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The document discusses the DIKW (Data, Information, Knowledge, Wisdom) hierarchy and how it relates to big data. It defines each level as: Data as "know-nothing" without meaning; Information as making sense of data to "know-what"; Knowledge as applying lessons from information to "know-how"; and Wisdom as evaluating knowledge through experience to "know-why" and predict outcomes. It argues big data tools must help transition between each level, from collecting and sorting large datasets into meaningful information, deriving actionable insights, and enabling accurate predictions.
DIKW and Big Data
- 1. 1 Big Data Perceptions – 6/2015 DIKW reflections – does big data mean big wisdom? Kamil Brzak If you investigate the abbreviation DIKW, you can find out very quickly its meaning. It is one of the essential principles of knowledge management. Better said a tool how to approach and understand the process of learning. What the DIKW really is? The exact origin of the term is pretty unknown. For sure, it was communicated in early 80’s by come people in different forms. About history, you can find many articles so let’s go for the nutshell – what is what and how to understand the things in regard to big data. D – DATA aka KNOW-NOTHING On that level we do no know anything. We just have many different elements – and yes, many many many different elements in case of big data. It looks like a heap of garbage with no sense. At that moment, it is necessary to realize that there should be something inside what could be certainly very valuable. Similarly when the very baby could not understand the words. But it is very curious and motivated to understand and communicate back. And that is the reason why it become to understand and communicate in the right time (my children were very motivated). So the outcome from above is that we need something, a tool or an ability, how to process the huge amount of everything. Example of data: 1492, 1779, apple, nice, smell, medium, bad, red, ball I – INFORMATION aka KNOW-WHAT It is very important moment now. If we moved from KNOW-NOTHING to KNOW-WHAT „phase“, we made to ourselves an attention that we realize something important. The process of data collection runs now. Data are sorted to logical molecules and these molecules give us – the information. What we need now is the evidence that
- 2. 2 our information gives sense. Again we need something what helps us to distinguish the sense and non-sense. We do not want to have an information (usually!) such: „rectangled apple“, or „wet sun“ thus if you are not e.g. modern artist. We need on the contrary an information which help us to know something, e.g. that apple is red, or sun is hot. We need to correlate information into valid knowledge. Tah dah! Example of information: red apple, smelling mushroom, hot sun, wooden water K – KNOWLEDGE aka KNOW-HOW Knowledge is very beautiful „phase“ of the process. It is the phase of two faces: very soothing and on the other hand misleading. When we grab, gain or create knowledge from information, there is a risk we are going to create a monster. Or a misconcept. Let me give you an example. You KNOW now, how to approach information about things. We can automate the knowledge – „Apple is always red, that knows every little child dude!“, or „I always lock my door“, or... „Robbery gives me money“.What we exactly did now? We built up the knowledge based on available information or information set, based on load of data. But is it correct? Maybe yes – we took data, mixed up to information and glued into knowledge. So, why robbery does not work every time? And why the apple is green sometimes. Or yellow? Well, it looks like we need something what can help us to evaluate gained or potential knowledge. We need experience. We need wisdom. We need continuous iterrations. Example of knowledge: smelling mushroom grew in the wood, flat frog leaps to sharpen pillow W – WISDOM aka KNOW-WHY And that’s it. To get the right outputs and results, we need evaluated, well sorted and correct knowledge (I know it could be a little issue from philosophical perspective, but let me stay with my pretty, western-culture-based stance). That knowledge could not come from nowhere. It is a process to face many failures, misunderstandings, patience, love. It is continuous recognition of the world around and this recognition could lead us to know, why things happen. Thanks to that, we do not repeat to eat poisoned mushrooms... well... most of us. Or, we can predict many things – like that during the winter snow could fall down. We can combine – snow will fall down during the winter and the chance is bigger in mountains. Example of wisdom: smelling mushrooms grew in the wood because there are a cold, wet conditions Conclusion So, DIKW process or cycle is very important to survive and to get the competetive advantage. In every „phase“ we appreciate the right tool for pass through to next phase, or level of pyramid. Shortly summarized: DATA – I need something to gain all that data like the essential building blocks INFORMATION – I need something to sort and combine data into meaningful product KNOWLEDGE – I need something what helps me gain a value from information – to transform it to another (bigger) value WISDOM – I need something what supports me to predict things, to change the paradigm when necessary, to lead and help others Think about it while you look for the right big data tool. It should fulfill all the needs coming from each „phase“ above: Could get huge amount of data in real-time (independently on its source) Could sort data quickly and correctly and give the instant information Could correlate information into knowledge Could predict the trends based on previous experience or knowledge I can do it all with Splunk.