Building out a Data Science Life Cycle (DSLC)

A project lifecycle can be a useful tool for structuring the process that a team follows. (A lifecycle is a repeating series of steps taken to develop a product, solve a problem, or engage in continuous improvement.) It functions as a high-level map to keep teams moving in the right direction. Although data science teams are less goal-oriented than more traditional teams, they too can benefit from the direction provided by a project lifecycle. However, traditional project lifecycles are not conducive to the work of data science teams.

In this post, I discuss two more traditional project lifecycles and explain why they are a poor fit for data science "projects." I then present a data science life cycle that is more conducive to the exploratory nature of data science.

The Software Development Life Cycle (SDLC)

The software development lifecycle (SDLC) has six phases as shown below. Under each phase is an example of an activity that occurs during that phase. This is typically called the waterfall modelbecause each one of these phases has to be complete before the next can begin.

SDLC works well for software development because these projects have a clearly defined scope (requirements), a relatively linear process, and a tangible deliverable — the software. However, this same lifecycle is poorly suited for data science, which has a very broad scope, a creative and often chaotic process, and a relatively intangible deliverable — knowledge and insight.

The Cross Industry Standard Process for Data Mining (CRISP-DM)

The Cross Industry Standard Process for Data Mining (CRISP-DM) lifecycle, which is used for data instead of software, is considerably more flexible than the waterfall model. It also has six phases, as shown below. The various phases aren't necessarily sequential, and the process continues after deployment, because learning sparks more questions that require further analysis.

CRISP-DM works much better for data science than does SDLC, but, like SDLC, it is still designed for big-bang delivery — deployment. With either model, the data science team is expected to spend considerable time in the early stages — planning and analyzing (for software development) or organizational understanding (for data mining). The goal is to gather as much information as possible at the start. The team is then expected to deliver the goods at the end.

For a data science team to be flexible and exploratory, they can't be forced to adopt a standard lifecycle. A more lightweight approach is necessary to provide the structure necessary while allowing the team to be flexible and shift direction when appropriate.

The Data Science Life Cycle (DSLC)

The fact that traditional project lifecycles are not a good match for data science doesn't mean that data science teams should have complete operational freedom. These life cycles are valuable for structuring the team's activities. With a general sense of the path forward, the team at least has a starting point and some procedures to follow. A good lifecycle is like a handrail; it's there to provide support, but it's not something you need to cling to.

The approach that seems to work best for data science teams is the data science life cycle (DSLC), as shown below. This process framework, based loosely on the scientific method, is lightweight and less rigid than SDLC and CRISP-DM.

Like the two project life cycles presented earlier in this post, DSLC consists of six stages:

  1. Identify: the roles or key players, such as customers, suppliers, or vendors.
  2. Question: the data. In other words, ask questions about the identity of the key players; for example: Which influencers are most responsible for persuading others to purchase our products? or What customer behaviors predict a probable purchase?
  3. Research: the data to find answers to the questions or to challenge any assumptions about the players and their characteristics, circumstances, or behaviors. Research may, for example, focus on correlation or cause and effect.
  4. Results: Create your initial reports to communicate and discuss early findings with the team. These are quick and dirty reports shared only among team members and perhaps a few others involved in the project that may trigger additional questions and research or even convince the team to change direction.
  5. Insight: After several rounds of questioning the data, researching, and reporting, your team steps back to identify any insights the team gained from the process.
  6. Learn: Bundle the team's insights to create a body of organizational knowledge. It is at this point that the team develops a story to tell and uses data visualizations to support it. This new knowledge is what really adds value to the rest of the organization. If you tell a compelling story, it may change the organization's overall strategy or the way it conducts business.

Looping through Questions

DSLC isn't always or even usually a linear, step-by-step process. The data science team should cycle through the questions, research, and results, as shown below, whenever necessary to gain clarity.

Some organizations that have strong data science teams already follow this approach. For example, the video subscription service Netflix used this approach to create their hit series “House of Cards.” They had 33 million subscribers at the time. Their data science team looked at what customers were watching, ratings of shows, what plots viewers liked, and the popular actors (Kevin Spacey was very popular at the time). Netflix determined that political shows were very popular and hired Spacey. Then they modeled the new show on the popular British version of the program.

The Netflix team used data science to develop the idea for the show. They created a predictive model based on analysis of viewer demand. They worked to cycle through questions, research, and results. They then created a story of what their customers would like to see. That story became an actual story that turned into a hit television program.

This cycle of question, research, and results drives insights and knowledge. The data science team loops through these areas as part of the larger DSLC. Remember to not think of this lifecycle as a waterfall process. Instead, think of it as a few steps to start and then a cycle in the middle to churn out great stories at the end.

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