10.3 Effective Executive Summaries

LEARNING OUTCOMES:

By the end of this section, you should be able to:

Define executive summaries, noting their important features.
Determine which details of the modeling process would be most important to include in an effectively written executive summary.
Write an executive summary that includes actionable advice based on the results of the model.

The executive summary serves as an essential first impression, succinctly transforming complex technical analyses into an accessible and engaging synopsis for an audience of varied backgrounds. The summary should begin with a clear introduction to the business problem or research question that the project addresses. This introduction must be crafted in a manner that is comprehensible to a nontechnical audience, aiming to capture their interest by emphasizing the potential impact of the findings.

An executive summary should provide an overview of the data sources utilized and the methodologies applied, focusing on the novelty or relevance of the approach. In the case of a technical data science report, the use of advanced machine learning techniques or unique data-gathering methods should be highlighted, avoiding overly technical language. The core of the summary is the presentation of key findings and insights, articulated in a manner that is both lucid and actionable. Instead of merely presenting statistics, the emphasis should be on their interpretation, relevance to the initial query, and their practical implications in the business or research domain. The conclusion should be a persuasive call to action, suggesting further research or the practical application of the model, designed to encourage the reader to engage with the full report and explore the depth of the project's contributions.

What Details Need to Be Included?

An executive summary is a concise, standalone document that encapsulates the essence of a data science report. This summary is the first element that readers encounter, and it plays a critical part in setting the tone for the entire document. The structure of the executive summary is designed to guide the reader through a logical progression of ideas. It typically begins with a clear statement of the problem or research question, setting the context for the report. This is followed by a brief description of the data sources and methodologies used, highlighting any innovative or advanced techniques employed in the analysis. The summary then succinctly presents the key findings and insights derived from the data, focusing on those that are most relevant and impactful. Complex data and technical details are distilled into understandable terms, ensuring that the summary remains engaging and informative without being overwhelming.

The executive summary concludes with recommendations or conclusions drawn from the analysis. These recommendations need to be presented clearly and persuasively, emphasizing their relevance and potential impact. The summary is also characterized by its brevity and clarity, typically spanning no more than a page or two, ensuring that it can be quickly read and understood. The language used must be straightforward and jargon-free, catering to the diverse backgrounds of the report's readership. Overall, the executive summary serves as an effective tool for communicating the value and implications of a data science report, bridging the gap between technical analysis and strategic decision-making.

Presenting Actionable Advice

Actionable advice in an executive summary provides specific recommendations and guidance to practitioners related to how the results can be used to share information to build knowledge or support decisions. The actionable advice should not be directive but presented as concepts to consider or even venues to share the information. Depending upon the objective(s), topic(s), and level of detail, one may recommend specific divisions, departments, or units in the organization where the report may be best utilized; the advice should be clearly linked to the data and analysis, offering concrete steps that can be implemented to achieve desired outcomes. Consider the order of the suggestions based on their perceived impact and feasibility within the organizational landscape (e.g., culture, politics, priorities).

For example, suppose that you have made a model for home prices. Your analysis may suggest the following actionable advice: “Homes with recent renovations show a significant increase in market value. Focus on updating kitchens, bathrooms, and other key areas to maximize return on investment.” By providing well-founded, practical suggestions, the executive summary not only informs but also empowers individuals to make informed decisions.

Executive Summary Example

Below is an example of an executive summary report in Python.

Note


    !pip install fpdf
    from fpdf import FPDF
    
    # Define a custom PDF class that inherits from FPDF
    class PDF(FPDF):
      def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.set_margins(25.4, 25.4, 25.4) # Set 1-inch margins (25.4 mm)
        
    # Define the header method
      def header(self):
        self.set_font('Arial', 'B', 12) # Set the font for the header
        self.cell(0, 10, 'Executive Summary', 0, 1, 'C') # Add the header text
    
    # Define a method to add chapter titles
      def chapter_title(self, title):
        self.set_font('Arial', 'B', 10) # Set the font for chapter titles
        self.cell(0, 10, title, 0, 1, 'L') # Add the chapter title
        self.ln(2) # Adjust the line break for single spacing
    
    # Define a method to add chapter body text
      def chapter_body(self, body):
        self.set_font('Arial', '', 10) # Set the font for the body text
        self.multi_cell(0, 5, body) # Add the body text with decreased line spacing
        self.ln(2) # Adjust the line break for single spacing
    
    # Define a function to create the executive summary
    def create_executive_summary(title, sections):
      pdf = PDF() # Create a PDF object
      pdf.add_page() # Add a page
      pdf.chapter_title(title) # Add the main title
      for section_title, section_body in sections.items(): # Loop through sections
        pdf.chapter_title(section_title) # Add section title
        pdf.chapter_body(section_body) # Add section body text
      pdf.output('executive_summary.pdf') # Generate and save the PDF to current directory 
    
    # Content of the executive summary
    title = 'Applying Data Science Techniques to Detect Diabetes'
    sections = {
      'Purpose of the Study': 'This research study aimed to analyze patient health data and physical characteristics to detect diabetes using a classification decision tree model. The primary goal is to identify key predictors of diabetes and develop an accurate model for early detection and diagnosis. The study utilized a dataset comprising health metrics (i.e., cholesterol, glucose, HDL cholesterol, systolic BP, diastolic BP) and physical characteristics (i.e., sex, age, height, weight, BMI, waist, hip) of patients. A classification decision tree model was employed to analyze the data, leveraging its ability to handle both numerical and categorical data, interpretability, and effectiveness in identifying significant predictors of diabetes.',
      'Findings': 'The study found that glucose levels, BMI, and waist circumference are the most significant predictors of diabetes using a classification decision tree model. This model achieved high accuracy (91%) in detecting diabetes, indicating its effectiveness in correctly identifying both diabetic and nondiabetic patients. While sex and age were included, they had less impact compared to the health metrics. The research highlights the potential of this model to improve early diabetes detection and suggests further refinement and integration into clinical practice, alongside public health initiatives, to manage obesity and glucose levels for diabetes prevention.',
      'Recommendation': 'Based on the findings of the study, we recommend integrating the classification decision tree model into clinical practice to enhance early detection and diagnosis of diabetes. By utilizing this model, health care providers can identify at-risk individuals more effectively, enabling timely intervention and management. Additionally, further research is encouraged to refine the model and explore the inclusion of other relevant health metrics and demographic factors to improve its predictive accuracy. The findings should also inform public health initiatives aimed at reducing obesity and managing glucose levels, thereby contributing to diabetes prevention and overall population health improvement.'
    }
    
    create_executive_summary(title, sections)

The resulting output will look like:

Figure 10.4

Note

Additional Resources

Many useful articles and books have been written about technical writing and communicating data science findings. A brief list is included here, and we encourage readers to continue their learning about this important phase of the data science process:

Bettes, S. (2019). Audience. In M. Beilfus, S. Bettes, & K. Peterson (Eds.), Technical and professional writing genres: A study in theory and practice. OKState. https://open.library.okstate.edu/technicalandprofessionalwriting/chapter/chapter-2/

Brownlee, J. (2021, February 1). Sensitivity analysis of dataset size vs. model performance. Machine Learning Mastery. https://machinelearningmastery.com/sensitivity-analysis-of-dataset-size-vs-model-performance/

Hotz, N. (2024, April 5). 15 data science documentation best practices. Data Science and AI Project Management Blog. https://www.datascience-pm.com/documentation-best-practices

Intelligent Editing. (2021, June 9). How to understand your audience in technical writing. PerfectIt. https://intelligentediting.com/blog/how-to-understand-your-audience-in-technical-writing

Kazakoff, M. (2023, May). Delivering the facts. The Actuary. https://www.theactuary.com/2023/05/04/delivering-facts

Marino, V., & Dragan, D. (2018, July). Science communication with diverse audiences. Adult Development & Aging News. https://www.apadivisions.org/division-20/publications/newsletters/adult-development/2018/07/sharing-research-effectively

You are working in a business analyst role on a team that provides analytical support for a marketing department at a large grocery retailer. Sales are currently decreasing, and the marketing director needs to provide a data-informed strategy to support sales growth. Your group’s objective is to build a classification model that maximizes profit for the upcoming marketing campaign aimed at selling a new line of food items. The goal is to develop a classification model that can be applied to the entire customer base to identify and target customers most likely to purchase the new items, thus increasing the campaign's profitability. Additionally, the marketing director is interested in understanding the characteristics of customers who are inclined to buy these specific new items.

Part 1

Explore the ifood-data-business-analyst dataset and use summary tables and/or data visuals to provide insights to better understand the characteristics of the sample respondents and summarize the customer segmentation based on their behaviors. Collaborate as a group; for example, one team member could work on descriptive statistics while another member could create graphs and charts, and another member may write explanations to accompany the data and visuals.

Part 2

Create a classification model to identify approaches and factors that can maximize the profit of the upcoming marketing campaign. Be sure to validate and test your model. Report on the measures of fit and justify why your model is effective.

Part 3

Write a 1-page executive summary for the marketing director with your findings and recommendations. Include strengths and weaknesses of your model and modeling process.

Willems, J. P., Saunders, J. T., Hunt, D. E., & Schorling, J. B. (1997). Prevalence of coronary heart disease risk factors among rural blacks: A community-based study. Southern Medical Journal, 90(8), 814–820. https://doi.org/10.1097/00007611-199708000-00008