The Peter Principle states that in most hierarchical organisations, there is a natural tendency for employees to rise in the management hierarchy through promotion until they reach the levels of their respective incompetence. This is a statistical statement about people in an organisation, and there are cultural and management practices that can be put in place to … More An Email Test for the Peter Principle
We have all read about network effects and how they underlie the business models of fast-growing and high-margin companies like Facebook, Google, Netflix, and Apple. The book Platform Revolution by Parker, Van Alstyne and Choudary seeks to distill the key characteristics of these so-called Platform Businesses, whose overarching purpose is “to consummate matches among users … More Financial Intelligence as a Platform Business
What is the optimal way to introduce and embed analytics in an organisation? This is a question I have been wrestling with for years. There are three broad options and a lot of in-between: (1) a centralised analytics team that services different business units in the organisation; (2) a distributed structure where analytics professionals are … More The Case for Centralising Analytics
I recently concluded a two-year stint managing a team of ten highly skilled analytics professionals spread across three different locations. There were of course many challenges but the team over-achieved on just about every measure of success one can imagine. The team’s wins include completing on-time and under-budget a data-matching project that delivers tens of … More Agile Data Science: A Portfolio Approach to Managing An Analytics Team
There are many gems in the book Team of Teams: New Rules of Engagement For a Complex World, written by General Stanley McChrystal and co-authors based on McChrystal’s experience leading the Joint Special Operations Task Force in Afghanistan and Iraq fighting Al-Qaeda. One that stuck with me is a small section called The Need-To-Know Fallacy. … More The Need-To-Know Fallacy
Many of my most successful data science projects happen by accident. You know, the little skunkworks that arose from a serendipitous hallway conversation where an important and urgent business problem meets a half-baked analytical idea. With a suitable dash of data and the right mix of office politics and corporate kung fu, a baby data-science … More Agile Data Science: On Opportunism
I put together this simple mapping of corporate leaders as a half-joke last week. I think there’s something in it for everyone. (I’m an ex-consultant so naturally I think anything that requires more than two dimensions is unnecessarily complicating things. That’s another half-joke. :P) Joke aside, there is a serious dimension to the post. Much … More Organisational Psychopaths
In this third post on setting up a data science practice, I address some of the analytics processes that need to be in place to maximise value from analytics. After more than two decades of practice and development, there are now well- established data analytics frameworks like the Cross Industry Standard Process for Data Mining. … More Setting up a Data Science Practice: Analytics Processes
In the previous post, we discussed the key principles of setting up a data science practice. In this post, we’ll discuss the people dimension. One should read the below as suggestions, not prescriptions. There is more than one way to set up a data science practice. Critical to the success of a data science practice are … More Setting up a Data Science Practice: People Dimension
I have been involved in the setup of several data science practices in both industry and government. Here are a few key principles I use in establishing a data science practice. Principle 1: Building a predictive enterprise is, first and foremost, about building a human infrastructure. Many companies mistakenly believe that analytics is primarily about software … More Setting up a Data Science Practice: Fundamental Principles
Mental Models 4 Life 