Five Practices to Improve the Value of Data Science

Improving the Value of Data Science | 5
conclusion. The scientific method plays a critical role in understanding any data,
irrespective of their size or speed or variety.
The scientific method follows these general steps (see Figure 3):
1. Formulate a question or problem statement
2. Generate a hypothesis that is testable
3. Gather/Generate data to understand the phenomenon in question. Data can
be generated through experimentation; when we can’t conduct true experiments,
data are obtained through observations and measurements.
4. Analyze data to test the hypotheses / Draw conclusions
5. Communicate results to interested parties or take action (e.g., change
processes) based on the conclusions. Additionally, the outcome of the scientific
method can help us refine our hypotheses for further testing.
When I map the three data science skills against the five steps of the scientific method,
it's clear why data science skills are so important in extracting insight from data (see
Figure 4). Proficiency in each of the three data science skills is required to successfully
implement the scientific method as a way to get insights from data. Business knowledge
is necessary to help formulate the right questions, generate hypotheses, gather data and
communicate results. Technology/Programming skills are needed to gather/generate
data and analyze data/test hypotheses. Finally, Statistics/Math skills are necessary to
gather data, analyze data/test hypotheses and communicate results.
Figure 4. You need all three data science skills when you adopt the scientific method
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