The Brightest Future Belongs to Businesses Who Adapt


In the context of , let me qualify what change is not – change is not replacing the name plate on your department from “Department of Intelligence” to “Department of Science” nor is it, “Let’s grab a few individuals from IT and execute on a couple of algorithms like RandomForest.” It’s also not, “Let’s promote Jane to lead our Data Science effort because she knows the most about our service.” Applied Data Science is a unique bird and that unique bird is what we’ll address here. In the end, no matter whether you’re reading this for your company or for yourself – the truth is resounding: embrace change or suffer the consequences. How can we view this statement from a Data Science point of view? How can a help his company or team? How can Data Science create deep impact for an ?

Innovation Is Necessary

One only has to look at nature to see that not everything survives. The same is true in business as well. Applied Data Science in business creates new methods, new ideas, and new products. Imagine not only coming up with big ideas, but also being able to support why the innovation should require your organization’s buy-in.

There have been many articles put forth like “What Is the of Data Science?” and “Can Your Organization Get an ROI out of Data Science?” I am not 100% confident these juicy titles weren’t written simply to capture new viewership, or if they were designed to explore business expectations around Data Science. To be clear, an organization should expect Data Science to create data-driven innovation.

Build New Value with Old Data

The market will always place a premium on getting more from what was discarded or seen as worthless.

One of my favorite client reactions is the question “Where did you get this data?” In my judgment, that is a hallmark of good Data Science and a demonstration of what Albert Einstein called “new angles, old problems”. This can happen in a myriad of ways – defining new targets, enriching the data, augmenting the data, etc.

Innovation with any value should be useful; however, I find that data-driven innovation is among the most useful. As Data Scientists we have an entire sandbox to work within where, for the most part, people have left their shovels and pails and gone on to other things. The reason – the belief that there is just nothing more to do in the sandbox. A Data Scientist with a keen eye and an aptitude for solving business problems will ignore that completely and get to work in what seems to be a new data paradise. Quite frankly, the largest challenge for Data Scientists in building new value with old data is found in their organization’s ability to assimilate new findings and implications.

Commit to Shaping the Future

It’s important to build partnerships in Data Science.

A valued part of Data Science is the ability to communicate findings. While I agree that better visualizations around data products will absolutely help in this area, I simply mean sharing what a Data Scientist finds. If your organization is committed to shaping the future of your industry and you are a Data Scientist, you need to be working to find alignment with others in your organization. This needs to be a priority no matter what level you are in the organization – from Associate Data Scientist all the way to Chief Data Scientist.

Breaking it down

Being able to communicate to a business that Data Science is a unique approach to problem-solving can be a difficult task, after all you have to be bi-lingual (Data Science and Business). However, focusing on data-driven innovation, building new value with old data, and committing to shape the future with your company are key. Following these three objectives will help you break down walls in any organization and allow you to add extraordinary value where the business needs it most – its future.

[Originally Posted by Damian Mingle on LinkedIn]

1 Comment

  1. Monica Lee Reply

    At my organization we focus on the three things you mentioned in your article, however many times the business simply doesn’t assimilate how are data science team would want them to. Sometimes I feel like the innovation we speak about should only be slightly ahead of where the business is…too far and people shut down.

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