Unlocking the Power of Data Storytelling for SAP Professionals: A Comprehensive Guide

In the ever-evolving landscape of data science, one principle remains timeless: the art of storytelling. We have all encountered those dreaded PowerPoint presentations, riddled with bullet points that march down the screen like a battalion, each one more monotonous than the last. The audience's eyes glaze over, minds drift, and the crucial data insights are lost in a sea of text.

Yet, we are inherently drawn to stories. A well-crafted narrative—whether in a movie, book, television show, or podcast—captures our attention and imagination. So why should data presentations be any different? As data professionals, we must transform raw data into compelling stories that resonate with our audience. This article delves into the essential steps to achieve this transformation, from identifying the story and understanding your audience to gathering and vetting data, and finally, choosing the right tools for the job.

Identifying Your Story

Every great data story begins with a clear question or hypothesis. What insights are your users seeking? Consider a company that provides services and equipment: their questions might range from, “Which equipment requires the most servicing?” to “Is there a correlation between equipment type and parts replacement?” Meanwhile, the finance department might be more interested in predicting cash-on-hand, and sales could be focused on customer churn.

Once you identify the core question, you can begin to weave a narrative around it. This involves not just presenting data, but interpreting it, finding the connections, and understanding the implications.

Understanding Your Audience

Knowing your audience is crucial. Generally, your audience can be divided into three categories: executives, business professionals, and technical professionals.

• Executives: These individuals need the big picture. They prefer concise, high-level summaries that highlight key insights and strategic implications.
• Business Professionals: This group includes super users and business analysts who understand the processes in detail and can digest raw tabular data.
• Technical Professionals: These are your data science and analytics colleagues who require detailed technical explanations and are interested in the methodologies and models used.

Tailoring your presentation to the needs and expectations of these different groups ensures that your message is not only heard but understood and appreciated.

Gathering and Vetting Data

With your story and audience in mind, the next step is finding the data to support your narrative. This process can be challenging, especially when data is incomplete or hard to find. When sourcing data, consider the following:

• Reputability: Is the source credible? Use trustworthy sources like Data.gov, ProPublica, the US Census Bureau, and GapMinder.
• Completeness: Is the data complete? Missing data can skew results and weaken your story.
• Correlation and Redundancy: Are there features that are overly correlated? Remove or combine redundant data points to avoid misleading insights.

Applying the Right Tools

The toolkit for data storytelling is vast. While advanced techniques like machine learning and deep learning are powerful, sometimes simple tools like dashboards are sufficient to tell the story. Here are some key tools and approaches:

• Advanced Reporting and Dashboards: Often, a well-organized dashboard can effectively communicate your findings. Tools like Power BI and Tableau are excellent for creating interactive and dynamic visualisations.
• Machine Learning and Deep Learning: Use these when your story requires predictions or uncovering hidden patterns in the data. However, avoid using complex models unnecessarily.
• Programming Languages: Python and R are invaluable for data manipulation, analysis, and visualisation. They offer a plethora of libraries and packages that can handle everything from basic analysis to advanced machine learning.

Embracing the Art of Storytelling

Even the most technical data deserves a bit of storytelling flair. Learning from master storytellers like Ira Glass can elevate your presentations. His advice on crafting narratives can help you structure your data story in a way that is engaging and memorable.

A Practical Guide for SAP Professionals

For SAP professionals, the challenge often lies in bridging the gap between the technical world of data science and the practical world of business operations. Here are some steps to make that transition:

1. Ask Better Questions: Shift your perspective to think like a data scientist. Instead of merely fulfilling report requests, consider what additional insights could be drawn from the data. For instance, if a finance professional requests an overdue report, think about analysing patterns in late payments, predicting future payment behaviour, or rating customers based on their payment history.
2. Use Better Tools: SAP developers typically work within the confines of ABAP and SAP GUI. However, incorporating tools like Python, R, Power BI, and Tableau can vastly enhance your ability to analyse and visualise data. Learning these tools can help you create more dynamic and insightful reports.
3. View SAP as a Data Source: Rather than being confined to SAP’s built-in reporting tools, see SAP as a data repository that can feed into more flexible and powerful analytics tools. Extract data from SAP using BAPIs, OData, or even direct database access when necessary, and process it in your tool of choice.

Extracting Data from SAP

Getting data out of SAP can be daunting due to its complexity. Here are some methods:

• BAPIs (Business Application Programming Interfaces): These are predefined functions that can retrieve data from SAP without the need to understand the complex underlying database structure.
• OData Services: These allow for HTTP connections to SAP data, offering a flexible and scalable way to access data using RESTful APIs.
• Web Services and Direct Database Access: While less recommended due to complexity and potential risks, these methods are available when other options are insufficient.
• Screen Dumps to Excel: Sometimes the simplest solution is the best, especially when dealing with specific screens known to the end user.

Roles and Responsibilities in Data Science

In the real world, especially in smaller companies, you might find yourself wearing multiple hats. Whether you are a data analyst, engineer, architect, or scientist, be prepared to handle various aspects of data science from extraction and storage to analysis and presentation. Understanding the landscape of these roles helps you better navigate and apply data science principles effectively within your organisation.

Summary

Data storytelling is an art that combines technical skills with narrative flair. For SAP professionals, this means leveraging both your deep understanding of business processes and the powerful tools of data science. By asking better questions, using the right tools, and embracing storytelling techniques, you can transform raw data into compelling narratives that drive business insights and decisions.

Whether you are building a simple dashboard or developing a complex machine learning model, the goal remains the same: tell a story that resonates with your audience. This approach not only enhances your ability to communicate data insights but also positions you as a key player in your organisation’s data-driven journey.

Embrace the power of data storytelling and unlock the full potential of your data. The future of data science in SAP is not just about numbers and charts; it’s about the stories those numbers can tell.