My last article dealt with the proposed URLA/ULAD and how data should be and is the major focus for the mortgage industry going forward. Data is among the most powerful, underutilized, and sometimes misunderstood forces in technology today. The power of data can be used to create powerful change. It’s time to discover its full potential for problem solving. So let’s begin by exploring a number of philosophies about data.
We will start by examining the model of the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy. This hierarchy is part of the canon on information and science and is intended to represent the progression from knowing nothing to knowing why. While there are both pros and cons on the DIKW Hierarchy as a useful and intellectually desirable construct, my purpose here is to offer a different thought process when it comes to collecting, analyzing, interpreting, and using the results to make business decisions.
Most images depict DIKWs as a pyramid or as an ascending slope where we increase our understanding by adding value at each step in the process. Since data is the focus of this article we will use that as the foundation (see diagram 1) and build up from there.
Data, level one, is simply a collection of disconnected, objective facts about an event. It is raw data with no patterns or relations. It can exist in any form, usable or not. We start giving context and add value as we move to the next level.
Information, level two, is where the data is analyzed and organized to describe who, what, where, and when. Data is categorized, calculated, corrected, condensed and given labels and definitions to provide structure. Through this organizational mapping of data, patterns emerge and meaningful relationships can be identified. This is the role of MISMO in the mortgage industry. We give meaning as we move to the next level.
We are just gathering information in the first two levels. We know nothing in the data level and start to know what in the information level. We start to develop a theory or a framework for explaining behavior. We create novel ideas. We rely on experience or knowledge gained through doing. Together theory and experience lead to moving to the next level.
Knowledge, level three, is where we are able to make informed decisions by asking how? The European Committee for Standardization, in its Guide to Good Practice in Knowledge Management, described knowledge as “the combination of data and information, to which is added expert opinion, skills and experience, to result in a valuable asset which can be used to aid decision making.” Think of this as contextualized and organized information. We give insight as we move to the next level.
The first three levels are based on observation and past experience, where hopefully we are doing things right. Now let’s look to the future with the next two levels where we hope we are doing the right things.
Wisdom, level four, is where we are able to explain why and demonstrate judgment. We start to have an evaluated understanding and are able to learn from our accumulated knowledge. We think about what is best. We give purpose as we move to the next level.
Decisions, level five, is not really one of the levels of DIKW, but where we have enlightenment and clarity of perception. Hopefully, this got you thinking.
Now, let’s look at this from the opposite direction. In his book, The Design of Business, Roger Martin unveils a new way of thinking that balances the exploration of new knowledge (innovation) with the exploitation of current knowledge (efficiency) to regularly generate breakthroughs and create value for companies. In short, design thinking converts need into demand. Think about how this approach could impact your organization.
The Knowledge Funnel is the three-stage-model (see diagram 2) for altering knowledge from mystery to algorithms.
1.) Creates value in form of better efficiency.
2.) Requires continuous exploration and exploitation of knowledge.
3.) Functions especially in design thinking oriented communities.
This form of thinking is rooted in how knowledge advances from one stage to another—from mystery (something we can’t explain) to heuristic (a rule of thumb that guides us toward solutions) to algorithm (a predictable formula for producing an answer) to code (when the formula becomes so predictable it can be fully automated). As knowledge advances across the stages, productivity grows and costs drop, creating massive value for companies.
The mystery stage comprises the exploration of the problem. We discover disparate ideas and concepts. At the heuristic stage a rule of thumb is generated to narrow work to a manageable size. We experiment and start to define and develop models. Your intellectual capital and advantage is here. In the algorithm stage the general heuristic is converted to a fixed formula, taking the problem from complexity to simplicity. We deploy systems and procedures. Once systemized it is possible that the algorithms can be commoditized.
There is no better time than now to take a step back and evaluate your IT infrastructure and process flow.
In today’s digital world, business users need easier ways to explore data, uncover new insights and make informed decisions instantly from any device. However, achieving this can be difficult for many organizations given the complexities of outdated IT infrastructures. (PwC)
I will leave you with this final thought, “No new ideas, concepts or innovations can be formed from past information, data or knowledge – all new ideas can be validated only through unfolding the future events.”