Become a Hero for Data Science in Your Company

Looking to be an agent for data-driven change at your organization? Taking these steps will not only help you win, but help your company win too!

Your Origin Story

Some organizations are more reluctant than others to dive into data science and analytics.  The buzzwords are flying around them and the pressure is building, but it continually falls into a parking lot of to-dos for the future instead of a list of things to start today.

There is no denying that the fear can be real. Data science is a term that can be intimidating to those that don’t understand the process and skills required. An organization that historically makes decisions behind closed doors will struggle to experience data-driven efforts unearthing both company weaknesses and their “secret sauce.”  Currently, most company's decisions may have been successful based on gut feel, or the success of one gigantic Excel file. Gut feel and large Excel files aren’t scalable, and they are only right until they are very wrong. Maybe those decisions that did not succeed even ended in sudden leadership restructuring or, sadly, layoffs.

Being a data science hero means bravely conquering fear that may come in all shapes and sizes including:

• Fear of failure. 
• Fear of criticism.
• Fear of change. 
• Fear of the unknown.
• Fear of being terminated.

How do you bring the right hero powers to battle?  Here are some superpowers that will help you. 

Redefining Success

Data science is a science. If data science is intimidating, another term in the industry is “decision science.” After all, data science in business is all about driving better decisions. It starts with a hypothesis, then a well-designed experiment, and through iterations of learning it gets more accurate.  

When defining the initial project scope, discuss the accuracy required for “success” and how to fail fast when the answer is not attainable with the current data. Manage how you will learn from that experience to inform your next project. In the early phases of implementation, we like to define success as having more evidence to make a better business decision now than before. 

Collaboration

We've all heard it before: “garbage in” equals “garbage out”.  Working closely with the business injects every project with years worth of industry knowledge.  Using visualization tools allows the team to share insights throughout the project, unveiling the story behind the numbers.  Business users can easily and quickly sniff out data issues and find lots of opportunities for improvement.  There never fails to be a hidden complexity in the data only recognized by the experienced eye. 

In the end, this collaboration will be the key to better evidence for a business decision and a better data solution.

Communication

A data science project is not something you can do behind the curtain. Stakeholders need to be part of the process.  They should walk away with descriptive statistics at every update that they can use to make more informed decisions early and often. These early wins build trust throughout the organization and get everyone excited for the next steps.  Breaking down the process into bite-sized chunks full of valuable information will not only help in educating all parties, but also will have them begging for future projects.

Are you ready to become a data science hero at your company?  We're ready to help!

.