What does it mean to become a data-centric organization?

By Eric Schmidt

Data alone doesn't change the world. People do.

Moving to a data-driven organization can be scary.  Especially when you have been a successful business or entrepreneur that has relied on gut or feel for so long, it can can be incredibly scary or overwhelming to rely on data.  So many questions about the data  – do you trust it?  Is it accurate? Etc…

If you have survived this long (and been successful) you might be tempted to think you can keep doing what you have been doing.  Unfortunately, that thinking won’t get you very far these days.

The world of business is being transformed before our very eyes. Consumers' and business' expectations are changing weekly, maybe even daily. Innovation is happening at a breakneck pace. Technology is driving new models and new roles within organizations. It's an exciting time and yet, all this change can easily leave a company paralyzed trying to find that magic formula to growth.

We are not saying that becoming a data-centric organization is the magic formula to growth – but it plays a huge part.  In future blog posts we will explore the other components of that formula – especially the people/culture/philosophy part (which we believe might be the most important component).

You can hope the whole data thing blows over and goes away (we don’t recommend that).  As scary as “data-driven product development” or “data-driven marketing” sounds, with the right tools, basic foundational knowledge and some courage you can collect and analyze data quickly to make the best decisions for your business.  You are not collecting data in order to judge, but rather in order to study it to determine how you can improve.  It’s not about success or failure – but rather making better informed decisions.

One of the important things to keep in mind, is that gut or feel still plays a key role (in our minds) in becoming a data-centric organization.  In our case, developing initial hypotheses is a critical starting point – waiting to accumulate data or analyzing existing may not always be the most practical starting point.  Another important aspect to becoming a data-centric organization is to remember that data-centric doesn’t necessarily mean deep data capabilities  – analytics, predictive modeling, AI, BI and other sophisticated methods and expensive tools.  All of that is important but can be overwhelming and reminds me of this quote:

“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”- Dan Ariely, Author of Predictably Irrational
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’.

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