Organizations use Data Science to discover valuable insights and then apply those insights precisely to grow their business or mitigate their losses. Such applications might also 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:
• Communicate these insights to the right decision-maker, stakeholder, or system.
• Convince decision makers to trust the insight and accept its implications. If decision makers lack this trust, then they will likely ignore the recommendation, and fall back on “the way we’ve always done things.”
Typically, a host of unanswered questions resides in the minds of Decision maker counter the data-driven insights resisting to act on the conclusions of the data science team because:
• Don’t know the skills of the data scientist: Does the person who created this insight know what they are doing? Do they understand business risks as well as they understand their models?
• Don’t trust data science tools: Did the data scientist depend too much on software in creating this result? Did the data science team just use black box tools that auto-magically produced an answer without an understanding of the business?
• Don’t have confidence in the development process: Did the data scientist consider all reasonable approaches to the problem? Was there any way for someone else to review what was done, and know how things changed over time?
• Don’t understand what the results mean: What is this insight actually telling me? How does it apply to what I do? What factors does it reflect? Is it really better than what we have done before? Could I get fired for acting on this result?
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.
To deliver insights that the decision makers and other stakeholders trust and actually use, it is recommend adopting a Serious Data Science approach. For this, the scientist must have the appropriate training and tools to find insights relevant and valuable. And he/she shall communicate these insights to the stakeholders in a way to build trust and understanding.
Below are the key elements which will help the team meet these challenges.
• Widely-used open source software: The best way to make sure the team has the training to use a data science tool properly is to use the tools they already know. Millions of data scientists around the world learn data science using open source languages, such as R and Python. While some may argue which language is best, both have tremendous strengths and are trusted platforms.
• Comprehensive data science capabilities: To be confident team will find the best approach to any particular question; they need a wide range of analytic approaches readily available to apply and compare. Powered and validated by huge, ever-expanding communities and package libraries, the R and Python ecosystems ensure your team will always have the broadest range of tools for their analyses.
• Process transparency via code: Code allows others to inspect how a problem was first solved, and how that solution matured over time. Unlike point-and-click solutions where the history of how the analysis evolved is hidden beneath a pretty (inter)face or a spreadsheet where the logic is strewn across countless different cells, code explicitly describes what steps lead to the results. Further, code can be peer-reviewed and audited by third parties for further assurance of correct behavior.
• Understanding through visualizations: Just as a picture is worth a thousand words, a great visualization can explain a thousand lines of code. Visualizations help stakeholders understand complex data science insights and build confidence in the results. Interactive tools such as Shiny, PowerBi, Tableau etc. allow data scientists to create visualizations that can improve the understanding of a data scientist’s work while spurring engagement from stakeholders.