Data In Wisdom Out
I am often asked by the curious, what do we actually mean by “wisdom out”. Is it just a marketing ploy or is there something more to it? How do we get wisdom out of data? And, what is this so-called wisdom good for, anyways?
In this article, I am sharing my thoughts on the concepts of data and wisdom in general, and how it inspired the architecture and foundation of diwo (“data in wisdom out”) platform, in particular.
Being the first employee of a new startup about three years back, I had the unique privilege of gazing into the crystal ball, and project how the enterprise software space might look like in three to five years time when we are fully ready to enter the market. This exercise was essential in setting up a vision and a development road map for the company. Furthermore, it was also important for me personally to take on a challenge that expands the frontiers of my intellectual and entrepreneurial journey.
At that time, “Big Data” was the most trendy topic and IBM was feverishly pushing the concept of cognitive computing fueled by the famous jeopardy challenge win by their meticulously hand-crafted custom made intelligent question answering system, called Watson.
I concluded at the time that being a late entrant in the big data space, it won’t be wise for us to count on riding the big data wave. Big Data meme was subsequently superseded by IOT, big data analytics, and data science memes among others. More recently, both big data and cognitive computing seems to have given way to the latest hype around “Artificial Intelligence”.
Having a comprehensive background in distributed computing, artificial intelligence, and distributed problem solving, cognitive computing meme was a natural hook for me to gravitate toward. Question answering systems like Watson represented a tip of the iceberg. I saw an immense potential in borrowing from the progress in cognitive science and cognitive psychology and applying those concepts to develop higher layers in the enterprise software stack to make it easy for business users to deal with complexity caused by information deluge and to reduce their cognitive burden in their routine decision making activities.
Inspired by systems thinking approach, the first conscious effort was to contrast ourselves away from data first and silo-ed approaches. Instead of data first approach, we subscribed to ‘business first’ approach. A business is in the game of creating and capturing value. Data is useful only and as much as it participates in generating business value.
Data is simply a trace of historical events captured by tracking and measurement mechanisms. Data, in itself, has no intrinsic value but can potentially participate in the creation of value. Unbridled data collection is a foolhardy pursuit and must be tampered by business context. Business goal dependent, perception systems with attention filters must be designed and applied at the very first contact with data to retain only what might be useful and to discard the rest.
To adopt a business first approach in practice, we advocate starting from the top and understand what business value a company is striving to create, how do they go about creating it, and what hurdles they have to overcome in the value creation process.
Instead of developing a raw analytics capability by climbing up on the analytics ladder, from bottom raw data layer to descriptive, predictive, prescriptive analytics, and so on, we took a fundamentally different and an intelligent systems approach.
We imagined an enterprise to be a continuously evolving intelligent system that continuously senses its environment, makes informed choices, and acts upon these choices to influence the environment in its favor in a tangible and quantifiable way.
This cognitive view of enterprise, required that we go beyond the traditional notions of data, information, and knowledge. For this purpose, we introduced the concepts of awareness, intuition, and wisdom in diwo architecture while relating these to the concept of data and knowledge.
Data is the raw information that one receives from external sources. We keep it confined to the data staging area for subsequent reference and delete it when it is no longer relevant.
Knowledge is what is obtained after applying perceptual attention and feature extraction filters and transformers.
Awareness layer is about understanding the value and power of knowledge one has accumulated or has access to. Knowledge is power, they say, but one must be aware of this for the knowledge to be of any value.
A knowledgeable person or a business is one which has access to a lot of knowledge. But one who understands the power of this knowledge is said to be aware. In diwo, we implement the notion of awareness by a time-sensitive opportunity sensing module.
Once we are aware, we move to realize the higher level cognitive capability of wisdom.
Wisdom is knowing when and how to harness the power of knowledge once you are aware of it.
In diwo, this is achieved by an opportunity exploration module which provides a guided cognitive decision-making capability built around the concept of continuous business optimization.
Finally, few word about intuition! By intuition, one generally means unique human ability to arrive at useful decisions without quite knowing how those decisions were arrived at. In that sense of the term, current AI systems (deep learning neural networks) can be called as having intuitive ability. It is not that the AI systems can’t be intuitive, the real issue is that we don’t want AI systems to be intuitive and demand explanation from them to compensate for our lack of trust.
This concludes my brief explanation for the phrase “Data in Wisdom out”.
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