The 3 D Dilemma

The time between Discovery to Disruption, or from Disruption to Discovery, or from Data (Science) to Discovery has become truly short. The IT Department and indeed the entire organization, must learn to spin on a dime. Repeatedly.

The 3 D Dilemma - CIO&Leader

This is the fourth in our series examining Data and its idiosyncrasies. In the last three instalments we looked at Data Visualization and Stories, Thick Data, and Data Confluence. We start this viewpoint with three words. Data. Discovery. Disruption. Question: what three words start with D and are tightly integrated. Any one of the D can start a cycle: and then stay alone, or take along one of the other two Ds, or take along both. We of course, gave the answer before we asked the question. And the confusing question can be simplified by a rudimentary pictorial.

There are three possible scenarios: (1) D; (2) D à D; (3) D à D à D. The contention is that you can take any one of Data, Discovery or Disruption and put it anywhere in any of the three possible scenarios. Each such permutation can be empirically proven by real-life examples. Let’s look at some of such D cycles from real-life.

  1. Recent Data shows that the biggest spenders now are the Millennials (20s and 30s age group). It is no longer the patriarch (or Generation X / Boomers), who used to earlier decide what toothpaste to buy for the family. Data analysis led to this Discovery. And it in turn Disrupted the whole Marketing paradigm. Sales and Marketing teams are now addressing 3-5 different tastes and habits in the same family. The target segment has now become plural, and new Marketing methods are needed.
  2. A few years ago, a high-end holiday planner worked with a Financial Services organization and discovered that HNI individuals’ laundry spend shoots up around October every year. Here, Data led to a Discovery that allowed this holiday planner to focus its marketing effort.
  3. E-commerce was originally designed for pornography, so that the providers can bypass the laws and regulations. Retailers Discovered it and experimented with it. This one Discovery Disrupted Retail and Financial Services sectors, among others.
  4. Internet, which was meant for defense and academia, entered the business world and Disrupted it. And this cycle is still moving. Internet led to more Data and more Discovery, each of which in turn spawned even more of other D.

We can write a book on these examples, but we will refrain here from building up this list. Instead, we need to focus on what it means for the IT team. To begin with, one might now wonder that we have a very robust theoretical framework for Data Science, which is increasingly being deployed in business. Why is there no Discovery Science or Disruption Science? The answer is obvious: Science requires some degree of measurability to create predictability. If something is measurable and has a model for prediction, then it is no longer a Discovery or a Disruption.

There is an additional complexity that any of the D will change itself once the cycle starts. An example is Big Data, which was leaving the outliers out. Now there is a focus on adding in Thick Data to the schema. So, the D of Data itself has undergone a change in this cycle that is meant to lead to Discovery. And even if we were to not worry about a D itself changing, we have the issue of increased velocity of the cycle. The time between Discovery to Disruption, or from Disruption to Discovery, or from Data (Science) to Discovery has become truly short. The IT Department and indeed the entire organization, must learn to spin on a dime. Repeatedly. Talk about living in a VUCA world!

So, this begs the question: what is the solution to this chaotic scenario? One can respond with a counter question: does there need to be a “solution”, that will take care of all of the above? The real question should be, how does IT and the organization prepare for this? Two critical things come to mind. The first is the ability to reskill the technology personnel rapidly and continually. Waiting for the outside world to learn and be available as a resource, to assist with the changed world, will take so much time that the goal post would have moved again.

The second critical call to action is an organizational change leading to a technology team structure change. The business (and therefore the IT team) has to be broken into independent, yet tightly intercoupled units; what Gartner calls Composable Business. The technology team must also change itself into this new building blocks model. This allows any VUCA situation to be handled by the right building blocks with the needed intensity; without letting the impact creep into all the systems. Essentially, we are looking at being prepared and being prepared to change.

One last observation about this D Dilemma. It has brought new words from the dictionary into the business technology world, for example Transformation, Innovation, Intervention, Intelligence, Invention, and so on. And has spawned many new sub-industries of the IT world: Analytics, AI / Machine Learning / Deep Learning, Thick Data, Data Engineering, etc. etc. The IT Department needs to not let any of this intimidate them. Take whatever is relevant for its own VUCA, learn it, use it. Repeat.

The author managed large IT organizations for global players like MasterCard and Reliance, as well as lean IT organizations for startups, with experience in financial and retail technologies

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