Black Box

Does Your Data Science Lack Credibility?

In a recent post, we defined three key attributes of a concept we call Serious Data Science: Credibility, Agility and Durability. In this post, we’ll drill into the challenge of delivering credible insights to your stakeholders, and how to address that challenge.

Ultimately, organizations use data science to discover valuable insights and then apply those insights intelligently. Such applications might include making a better decision, improving a process, or otherwise changing how things are usually done. However, to make this happen, the organization must do at least two things:

Typically, a host of unasked questions underlie a decision-maker’s seeming resistance to data-driven insights. They might not act on the conclusions of a data science team because they:

All these questions and doubts contribute to stakeholder hesitation, especially when they feel that they, not the data scientist, will ultimately be held responsible for the result. Fortunately, there are ways to overcome these obstacles.

How Can You Deliver Credible Insights?

To deliver insights that your decision makers and other stakeholders trust and actually use, we recommend adopting a Serious Data Science approach. To do this, your team must have the training and tools to find insights that are relevant and valuable. And, your team must communicate these insights to other stakeholders in your organization in a way that builds trust and understanding.

Here are the key elements which will help your team meet these challenges:

Heather Nolis, Machine Learning Engineer at T-Mobile, and Jacqueline Nolis, Principal Data Scientist at Nolis, LLC, recently spoke at rstudio::conf 2020 about how they used Shiny to share their machine learning models drove engagement and built trust with their business stakeholders.

Serious Data Science: Credible, Agile, and Durable

These elements of Serious Data Science—trusted tools, comprehensive capabilities, flexibility, and transparency—will all help your team deliver insights that are more likely to be accepted by decision makers and actually have an impact. Next week, we will focus on Agility, and how your team can not only develop apps quickly but also regularly share those results with stakeholders to create a consensus, so you can make sure you are Getting to the Right Question.

Serious Data Science is:

Credible Agile Durable
  • Uses widely deployed and trusted tools
  • Includes comprehensive data science capabilities
  • Offers flexibility through the use of code
  • Provides transparency through visualizations and code
  • Employs existing knowledge and analytic investments
  • Allows rapid development and iteration
  • Scales well for enterprise and production use
  • Empowers your business stakeholders
  • Provides reusable, reproducible code and results
  • Delivers relevant, up-to-date insights
  • Supports and is supported by a vital open source community
  • Avoids vendor lock-in

Figure 1: Being credible is one of the crucial elements of a Serious Data Science platform.

Learn More about Serious Data Science

If you’d like to learn more about Serious Data Science, we recommend the following in addition to our previous posts in this series: