A Data Scientist Advice to Business Schools
To remain relevant business school graduates must learn to speak to Data Scientists, whose domain expertise is playing a vital role in an organization's ability to compete in today's market.
The nontraditional aspect of the Data Scientist role in business means the overlap between the approaches this expert takes to solve problems, and the operations a business is concerned with, is less obvious. But in a world fast becoming 'data everything' this represents a serious problem. For graduates tasked with striking effective conversations between themselves and the domain experts, that much-needed high-level language is critical.
There are 2 facts that lead to the undercurrent of concern within business schools:
Business schools must learn where the overlaps between data science and business operations exist. This can only be accomplished by learning how the data scientist approaches problem solving within an organization. More importantly, it requires an understanding of how data science differs from any other approach used to solve challenges. If business graduates do not understand the conceptual distinction between data science and other forms of analysis, they will be unequipped to strike meaningful and value-producing conversations with one of the key domain experts in modern enterprise.
Advice to Business Schools
To assist business schools, here are questions that are high level, yet get to the heart of what it means to do data science. They highlight the conceptual distinctive functions data science brings to the table that make it an altogether different approach to solving challenges. I encourage any course in business school to invite an experienced Data Scientist in for a real-world discussion with their students about what data science is, and what it is not. Through discussions like this a middle language can be built and business graduates can ensure their relevance in today's data-driven companies.
Bio: Sean McClure, PhD, is a data scientist at ThoughtWorks, a global technology company solving some of the world’s toughest problems.
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There are 2 facts that lead to the undercurrent of concern within business schools:
- The reality of today's world is that more and more businesses require software that houses models built by scientists; scientists who have analyzed and modeled the complex data the organization has collected; and,
- Many business graduates are not coming out with an understanding of what it is data scientists do, and therefore do not contain the middle language necessary to integrate one of the most important domain experts into their employer's strategic vision.
Business schools must learn where the overlaps between data science and business operations exist. This can only be accomplished by learning how the data scientist approaches problem solving within an organization. More importantly, it requires an understanding of how data science differs from any other approach used to solve challenges. If business graduates do not understand the conceptual distinction between data science and other forms of analysis, they will be unequipped to strike meaningful and value-producing conversations with one of the key domain experts in modern enterprise.
If business graduates do not understand the conceptual distinction between data science and other forms of analysis, they will be unequipped to strike meaningful and value-producing conversations with one of the key domain experts in modern enterprise.
Advice to Business Schools
To assist business schools, here are questions that are high level, yet get to the heart of what it means to do data science. They highlight the conceptual distinctive functions data science brings to the table that make it an altogether different approach to solving challenges. I encourage any course in business school to invite an experienced Data Scientist in for a real-world discussion with their students about what data science is, and what it is not. Through discussions like this a middle language can be built and business graduates can ensure their relevance in today's data-driven companies.
- What type of software is impossible to build in the absence of data science?
- How does data science elevate staff to work on more value-delivering, creative tasks?
- What is the difference between what a software developer automates and what a data scientist automates?
- How can data science augment marketing efforts, sales engagements, operations, and inventory management in a way that is not possible using more traditional forms of analysis?
- How could data science reduce a task that requires 20 levers to pull, down to one that only requires 5 levers?
- What is it about human decision-making that makes it limited when using large amounts of data?
- Why is there very little overlap between business intelligence and data science?
- What are the differences between Data Science and Big Data? Where do they overlap?
Bio: Sean McClure, PhD, is a data scientist at ThoughtWorks, a global technology company solving some of the world’s toughest problems.
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