The DIKA Model

 Information overload, also known as infobesity, is a major problem in both the business world and our society at large. Thanks to advancements in technology and the increased prominence of the global marketplace, access to data of all kinds has become much easier than ever before. Within an organization, we can now track many kinds of metrics and performance data in real time, giving rise to concepts like the Six Sigma and Just in Time models. Such plans that demand near-perfection and transparency would have been much more difficult to implement in prior generations.

There is reason to suspect that our obsession with performance data is killing performance, especially when it comes to employee expectations. However, data when properly used can be converted into powerful competitive advantages over the rest of the industry. Effective data collection, collation, and interpretation is something many businesses struggle with, but industry leaders tend to rely on. One tool to assist with informing decisions using available data is the DIKA model.

This is a simple workflow of sorts that can be used to guide the process of exploring a decision or issue and allowing the data to organically form a recommendation. It might be employed in order to solve a problem, or to decide on a course of action informed by historical data. The idea is an exhaustive collection of potentially useful data points are then analyzed, and only at that point is a theory crafted. In a complete subversion of the scientific method, data is collected and analyzed in order to form a hypothesis, not after.

During the information step, data is analyzed and measured against each other. Such patterns as reinforcing data points and consensus among disparate sources of data can be used to group the data points. The information step is more about pruning, reducing that exhaustive data list down to what appears to be the most founded facts. If there is something to prove, the facts with the most evidence in the data receives the priority. These most reliable pieces of data are renamed information.

The knowledge step should have expert or experienced analysts work on the refined list of information. A theory or hypothesis is extrapolated from the accumulated information. Although understood with the lens of expertise and context, it must ultimately be the information itself that points towards and justifies the conclusion. This interpretation of the information is called the knowledge.

After this, a plan of action is made and set into motion. The final step is no longer about making decisions but rather should consist of actionable recommendations. This may seem straight forward, but it may end up being one of the most time consuming steps, because converting a goal into immediate steps can be complicated. 

Like any data-driven decision making methodology, an analyst's understanding of the context of the data can help to ensure this is a powerful tool. Likewise, a lack of context can allow this to go off the rails. Businesses are inundated with data and it can be incredibly difficult to convert the recommendations in the data towards action items, especially within large organizations.

Unfortunately, within large corporations in the modern business world, short-term goals often take precedence over long-term goals. This is partially due to the quarterly and annually appraisals due to senior leadership, especially to the stockholders. Because of this, there is pressure to produce tangible results now, even if there may be long-term ramifications. The one metric everyone is measured on, in one way or another, is the profit margin.

This is to say that far too often, a sound and data-supported proposal will be shot down because there is a short-term expense. The more intangible or far off the benefits are, the greater the chance of the proposal failing is. Not every endeavor can be supported by data that will give reassurances of increased profitability of a certain amount. There is an art to persuading leadership to take risks, but any amount of framing will only have so much potential to move.

There is a vast number of situations this model could be applied to, some of which may benefit more than others. I believe that the defining feature of successful applications is its ability to increase profitability. If the DIKA model shows a certain path will lead to profits, management will be firm believers, but if it shows a path that leads to increased expenses then suddenly management will have their doubts.