Make code your company’s “natural language” and it will grow and innovate faster than the competition, writes David Waller for MIT Sloan Management Review.
Code is just for the IT department, right? Wrong. If you want to solve analytical problems, taking full advantage of advances in AI and machine learning, forget writing formulas in spreadsheets and take what Waller labels a “code-centred approach”, retraining your staff to write code and think algorithmically.
THE BENEFITS
Waller, a Partner and Head of Data Science and Analytics for management consulting firm Oliver Wyman’s Oliver Wyman Labs, highlights three ways a “code-centred approach” could benefit your business:
1) It makes it easier to separate data from data analysis. Thisallows different teams to focusondata and analysis independently of each other, which, Waller argues, will lead to faster progress.
2) It is easier to share and reuse. If you adopt these principles, espoused by software developers and the open-source software movement, you will enable more effective collaboration.
3) It is better for analysis. Researchers developing AI and machine-learning techniques use code. If you understand that code, you will have quick, easy and free access to state-of-the-art technology.
THE ROAD TO SUCCESS
Waller advises taking these three steps:
1) Eliminate language barriers. To reap the rewards of collaboration, communication is key. Choose one analytical programming language and use it company-wide. When everyone can “speak” this language, collaboration will become easier.
2) Embrace open source. Create a shared code repository where your staff can share their coding work.
The easiest way to test this is to pick a project, create a code repository and invite contributions from a wide audience. Utilise a code-sharing platform such as GitHub or Bitbucket.
Many companies, including Google and Microsoft, have fully bought into open source, going as far as to publicly share code.
3) Make code king. If you want to generate value from advanced analytics, code-based modelling must become “business as usual”.
Senior executives must emphasise their belief in “analytical excellence”.
Staff training must be provided, whether in the form of boot camps, online courses or customised in-house instruction.
Support is essential. “Progress stalls when the same handful of individual super-users are questioned repeatedly,” warns Waller. “They become overwhelmed. But people who are just one step ahead on the journey can become mentors for others just starting out.”
BEYOND SPREADSHEETS
Spreadsheets combine data and data analysis, making it difficult to extract solutions to your problems. The future of analytics is code.
“Popular answers, whether found through a search engine, a training resource, or a peer teacher, are almost always elegant and reusable,” writes Waller.