If you’ve heard of data science (if you haven’t, where have you been and how did you find this blog?), you’ve probably heard of “fail fast”. The fail fast mentality is based on the notion that if an activity isn’t going to work, you should find out as quickly as possible, and stop doing it.
As the size, complexity and number of new data sources continues to increase, there is a corresponding increase in the value of discovery analytics. Discovery analytics is the method by which we uncover patterns in data and develop new use cases that lead to business value.
It is easy to see how discovery activities lead to a fail fast method. However, how can we learn from these failures, and how can we proceed without experiencing the same failures time and again?
Good failure, bad failure
There are two different types of failure possible in a data science project: good failures and bad failures. Good failures are a necessary part of the discovery process, and an important step in finding value in data. On the other hand, bad failures occur when they could have been avoided, and are basically of waste of everybody’s time. Examples of the cause of bad failures include:
- Poor specification – this is not specific to data science and applies to any project that isn’t specified properly in terms of expected results and appropriate timelines.
- Inappropriate projects for a data science methodology – it has become increasingly common to call all analytics data science. If a project can be solved using a standard data warehouse and business intelligence method, then you should probably just do that.
- Poor expectation management – many data science projects suffer from this. It is important to ensure stakeholders are aware what can and cannot be expected from the results.
- Data supply – a vital first step in any analytics project is to ensure that the necessary data feeds are available and accessible.