Transitioning from Academia to Business? 3 Things to Keep in Mind
Congratulations! You’ve gotten your first job in Data Science in the business sector. Whether you’re fresh out of school or coming from a distinguished career in academia, you’re transitioning from academia to business — you feel ready to make an impact in your company.
[Related Article: 3 Unique Ways to Get a Job in Data Science]
And you are. You’re on the cusp of helping to transform a business for the modern era of machine learning and AI initiatives. Your knowledge and expertise are a vital part of that company’s growth and even survival, so don’t underestimate your potential to have an impact.
However, the world of business is a bit like speaking a different language. Sure, those languages may be related. You feel like you understand what’s going on, but there are a few things to keep in mind so you make your transition as smooth as possible.
1. Business Moves Fast
Research and academia are thorough, diligent, and full of detail. They’re also slow — really slow in business terms. If you want to make it in the business world, you’re going to have to speed things up.
That doesn’t mean you toss that diligence out the window. A big part of your job is to make your company more thorough and build a culture of decision making based on reliable data. You have expertise in what makes good data, something people in your company will need moving forward.
But you’ll have to get moving. Projects in academia and research can take weeks or months to come to fruition. In business terms, that could mean you miss the boat. Speeding up means prioritizing differently and learning how to prepare those insights from a business perspective, which leads us to our next idea.
What you bring to the table: efficient progress and an understanding of what kind of and how much data you need to proceed.
2. Business Goals Are Different
Business and data science goals aren’t different on the surface. Both want the best data and the best result. When you pull back the veil, however, you get two different meanings for “best.” Research/academia defines “best” as “the best according to the science” or “the coolest thing to happen” or “something no one else is doing.”
In business, it simply means “most valuable” and that value differs from company to company. Your job is now to produce value for the business. An ultra-complicated process may be the scientific “best,” but if it doesn’t create coherent business value, it’s not going to work.
The best route is to keep open communication with your business side to glean what “value” really is. Businesses have KPIs to follow, so the best solution could be the one that blows that KPI out of the water sooner. It could merely be “the thing the business will use” instead of fancy code that quickly goes dormant in the hands of shareholders.
Don’t take it personally; the business is doing what it needs to, and chances are, that business intuition is a valuable learning curve for you. However, delivering consistent results from a business perspective could lead to trust. That trust could lead the business to do some of those cool things you’re itching to incorporate from your research days.
What you bring to the table: The ability to innovate when the situation arises and knowing when the right time is to push a business out of its comfort zone.
3. Simple Is Better Than Novel
In research, you get points for doing something novel and exciting. The goal is to create or do something no one has done before. While that’s admirable, in the business world, sometimes simple is better than novel.
Businesses need a practical approach. If the code is so complex that no one can hope to understand it in the future, and it will be a nightmare to maintain, it’s not a good fit. If your product is something no one will use, it’s not a good fit. Humans like to believe we’re logical, rational beings, but many other factors go into whether we’ll use something.
The root is trust. Businesses won’t deploy things they don’t trust. As more business owners leap data-driven initiatives, transparency is going to become a key issue within the data science community. While certain deep learning will remain a black box, you’ll need to figure out how to make most processes more trustworthy.
What you bring to the table: A teaching spirit and the ability to make meaningful connections between the ideal and the messy reality.
[Related Article: 4 Examples of Businesses Solving Problems with AI]
Don’t Abandon Your Gifts
Adjusting to the business world doesn’t mean abandoning your gifts. It means learning how to listen and adjust based on your company’s goals. Within that framework lies a powerful position that can ensure a business survives the next tech revolution.
As someone in the data science field, you have an incredible gift you bring to the table. Businesses struggle with what to do with the data they have, and you can help unravel the mystery.
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