An executive’s guide to machine learning

McKinsey recently published at excellent guide to Machine Learning for Executives. In this post I categorise the key points that stood out from the perspective of establishing machine learning in an organisation. The key take away for me was that without leadership from the C Suite, machine learning will be limited to being a small part of existing operational processes.

Iron Man

Iron Man

McKinsey recently published at excellent guide to Machine Learning for Executives. In this post I categorise the key points that stood out from the perspective of establishing machine learning in an organisation. The key take away for me was that without leadership from the C Suite, machine learning will be limited to being a small part of existing operational processes.

What does it take to get started?

Strategy

  • C-level executives will make best use of machine learning if it is part of a strategic vision.
  • Not taking a strategic view of machine learning risks its being buried inside routine operations. While it may be a useful service, its long-term value will be limited to “cookie cutter” applications like retaining customers.
  • C Suite should make a commitment to:
    • investigate all feasible alternatives
    • pursue the strategy wholeheartedly at the C-suite level
    • acquire expertise and knowledge in the C-suite to guide the strategy.

People

  • Companies need two types of people to leverage machine learning.
    • “Quants” are technical experts in machine learning
    • “Translators” bridge the disciplines of data, machine learning, and decision making.

Data

  • Avoid departments hoarding information and politicising access to it.
  • A frequent concern for the C-suite when it embarks on the prediction stage is the quality of the data. That concern often paralyzes executives. Adding new data sources may be of marginal benefit compared with what can be done with existing warehouses and databases.

Quick Wins

  • Start small—look for low-hanging fruit to demonstrate successes. This will boost grassroots support and ultimately determine whether an organization can apply machine learning effectively.
  • Be tough on yourself. Evaluate machine learning results in the light of clearly identified criteria for success.

What does the future hold?

  • People will have to direct and guide the machine learning algorithms as they attempt to achieve the objectives they are given.
  • No matter what fresh insights machine learning unearths, only human managers can decide the essential questions regarding the company’s business problems.
  • Just as with people, algorithms will need to be regularly evaluated and refined by experienced experts with domain expertise.

You can read more in the original article here. You can also read a more general guide to building data science capability here.

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