Train Your People to Think in Code

Taking a code-centered approach to work will benefit organizations in three ways.

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An MIT SMR initiative exploring how technology is reshaping the practice of management.
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Today, most companies equate doing analysis with writing formulas in spreadsheets. But the business landscape has shifted seismically since the invention of the spreadsheet. Today, organizations must think in terms of millions of individual customers, not just a handful of segments, and solve problems with reusable solutions to avoid reengineering the process from the ground up. And they want to benefit from the latest advances in machine learning and AI, not simply throw regressions at whatever analytical problem they face. In short, companies need to retrain for writing code, not formulas, as the future of work will entail thinking not just analytically but also algorithmically.

This change of perspective is significant. Most companies might see code as something confined to obscure corners of the IT department or as the exclusive province of a select group of data scientists. But organizations that manage to make code the natural language for diffusing analysis across their business can often grow and innovate faster than their peers.

Taking a code-centered approach will benefit organizations in three ways:

First, thinking in code allows companies to cleanly separate data from analysis of the data, which allows teams to improve each one independently of the other. When data and analysis are cleanly separated, different teams can focus on independently improving each aspect, leading to faster progress.

Second, code is much easier to share and reuse — the entire open-source software movement rests on this idea. Software developers have spent years building tools to make their work easy to trace, modify, and share. By adopting key principles of software development, such as version control, enterprise teams can be more efficient and collaborative as updates to files are tracked throughout their lifetime and changes can be reversed easily.

Finally, code is better for both simple and complex analysis. Breakthroughs in machine learning and AI techniques are implemented as code, and by cloning the code researchers are using, individuals can gain access to state-of-the-art techniques in analysis, quickly and for free.

So what must managers do to move their existing workforce along the spectrum from formula to code? In our experience, there are three practical steps leading companies in this area take.

Tear down the ‘Tower of Babel.’ Communication is a prerequisite to collaboration. Language barriers create some of the strongest barriers to effectively sharing ideas.



An MIT SMR initiative exploring how technology is reshaping the practice of management.
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Comment (1)
Gopi Gopalakrishnan
Hi David, as a coder, software engineer and banker myself, your articles fully resonates with me.