Ten Signs of Data Science Maturity by Peter Guerra and Kirk Borne

Author:Peter Guerra and Kirk Borne
Language: eng
Format: mobi
Publisher: O'Reilly Media, Inc.
Published: 2016-03-14T04:00:00+00:00

7. …celebrates a fast-fail collaborative culture.

Culture is a hard thing to define, but if you look at what a team celebrates, that is a good indicator. Some organizations are afraid to fail, or have a culture where that is frowned upon. They are more focused on strategy than culture. But many business experts remind us that “culture eats strategy for breakfast (or lunch).” Therefore, start working on your data science culture sooner than on your data science strategy. Admitting mistakes is one thing, but purposefully exploring the unknown with your data is not a mistake. Test your organization’s maturity by asking yourself: when my hypothesis fails, then what happens? The fast-fail mindset understands and appreciates the proper meaning of this adage: “Good judgment comes from experience. And experience comes from bad judgment.”

True data science (based on rigorous scientific methodology; see “4. …follows rigorous scientific methodology (i.e., measured, experimental, disciplined, iterative, refining hypotheses as needed).”) explores the limits of what can be learned quickly by iterating on multiple hypotheses with agility. This may require that you invite your business unit partners to explore with you — that’s DataOps (see “2. …uses Agile for everything and leverages DataOps (i.e., DevOps for Data Product Development).”). Having the data and tools to allow you to do this is directly related to its success and maturity (see “1. …democratizes all data and data access.”). Mature data science capability allows for an iterative fast-fail culture on your path to achieving the most rewarding discoveries, making the best evidence-based decisions, and delivering the most innovative choices for your organization.

The optics around a project failing is often difficult to overcome. It is hard to justify spending limited resources only to find out that the hypothesis was wrong — the value from knowing what not to do is often lost or not celebrated within the culture. A mature data science capability is familiar with traditional A/B testing — designing experiments to test and evaluate alternative hypotheses, one of which may include some sort of intervention or tuning (the treatment sample) and the other is the null hypothesis (applied to the control, untreated sample). Typically, one of those experiments will fail, and one of them will not. That’s the whole point of A/B testing. If an organization cannot accept failure, then they are not doing mature data science.

One could argue that fast-fail has an analytical foundation in machine learning algorithms. Specifically, in many classification algorithms, the goal is to define as accurately as possible the boundary (however complex) that separates different classes of objects. That boundary might be linear (e.g., if your team scores more points than my team, then you win), or it might be skew (e.g., if your total score on two exams A + B is greater than 140 out of 200, then you pass the course), or it might be complex (e.g., the hyperplane separating two classes in a Support Vector Machine algorithm when you are working with complex data that has high dimensionality).

In order to circumscribe the boundaries between complex classification rules (e.

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