In our minds, you can start down the path of data-centricity with simple mindset shifts – start with a gut feel hypothesis and then get out in the world and validate it.  A simple example of a past project for us was working with ice cream shops (changed the industry for confidentiality purposes), we wanted to develop a cornerstone piece of collateral that would resonate with and speak to solutions specifically for a subset of a specific type of ice cream shop.  We built an MVP – minimum viable product (a huge concept for us – cultural/philosophical approach – more on that later) and proceeded to take that product out to our targeted ice cream shop owners to validate our hypothesis.  Turns out we missed the mark and our messaging really wasn’t limited to that specific type ice cream shop – the problem we were addressing was the same for all ice cream shops.  The messaging needed to shift to the specific problem and solution vs the original hypothesis of these issues only existing in specific ice cream shops.  We were now able to look for all ice cream shops that would potentially have those issues or problems vs targeting a specific type of ice cream shop.  Again, the goal is not to get into specifics on ice cream shops, but rather to illustrate that becoming data-centric can be as simple as shifting mindsets – getting outside the four walls of the business and truly starting to understand your customers. In the spirit of our mission to provide tangible, specific examples of some of these theories and concepts, let us provide a tangible, real-world example.  Again, this example is to illustrate that you can easily get on the path to becoming a data-centric organization without a huge investment or crazy level of maturity or sophistication in the data world. In this example we worked with a SMB (small-to-medium sized B2B) distributor.  We used their historical sales and marketing data (CRM data) to analyze their major sources of revenue.  The slides are as follows:



Hopefully this example helped provide some context as to how you can slowly move towards becoming a more data-centric organization without feeling like you have to have incredible data scientists, sophisticated processes and platforms.  Eventually the goal is to get to more and more maturity and sophistication, but that will be a natural evolution as you get more and more comfortable with the data and the decision making abilities it enables.  One of the biggest issues we see and will explore in a subsequent blog post is best described by the following illustration.  This (in our experience) especially holds true in the SMB market space.  So stay tuned as we will share our experiences with the ‘Data Wheel of Death’.