Clearly articulate the business problem the company is facing.

Communicate with data scientists about specific data needs and understand the underlying quality of the data.

Draw appropriate conclusions to the business problem based on the data and make recommendations on a timely basis.

Present their results to individual members of management in an accessible manner.

Developed analytics mindset: Know when and how data analytics can address business questions.

Data scrubbing and data preparation: Comprehend the process needed to clean and prepare the data before analysis.

Data quality: Recognize what is meant by data quality, be it completeness, reliability, or validity.

Descriptive data analysis: Perform basic analysis to understand the quality of the underlying data and its ability to address the business question.

Data analysis through data manipulation: Demonstrate the ability to sort, rearrange, merge, and reconfigure dat in a manner that allows enhanced analysis. This may include diagnostic, predictive, or prescriptive analytics to appropriately analyze data.

Statistical data analysis competency: Identify and implement an approach that will use statistical data analysis to draw conclusions and make recommendations on a timely basis.

Data visualization and data reporting: Report results of analysis in an accessible way to each varied decision maker and his or her specific needs.