Being a data analyst sets a precedent for your work. However, many of your coworkers or clients will often have little-to-no knowledge of how to read data analysis reports in their raw form. This is why levelling the playfield and using commonly-accepted terminology and formatting is important for the sake of accessibility of your data analysis reports.
That said, you will be required to apply your people skills to write a well-structured report, which anyone will be able to understand with ease. But how do you do that without compromising the quality of scraped data and your analysis of collected information? Let’s take a look at some of the practical tips you can apply to your data analysis report writing and the benefits of doing so.
Benefits of Writing Well-Structured Data Analysis Reports
Before we dive into how you can improve your data analysis report writing process, let’s discuss the benefits of taking a more systematic approach. Data analysts are in a unique position of acting as intermediaries between raw data and their colleagues who specialize in a variety of other fields.
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Web designers, copywriters, and project managers will all look to you for guidance when it comes to data analytics. Ensuring that your reports are legible, well-structured, and formed by using data scraper solutions is important for the sake of data quality. Some of the major benefits which manifest from informative and planned data report writing include:
- Increased efficacy of internal team communication
- Improved productivity and coworker agency with data report implementation
- Data analysis scalability and better focus on specific data elements
- More informed decision-making processes and lowered margin for production error
- Structured data storage opportunities for future data analysis and scraping
Advice on Data Analysis Report Writing
Decide on your Data Report’s Goals
The first order of business in regards to making sure that your data analysis report is legible is to simply contact your coworkers or managers. What are the primary goals of your data report from their perspective?
If you are working in web development, your coworkers might want you to scrape data from popular websites and extrapolate it for research purposes. Or, you may be asked to analyze past customer behavior on your platform in order to improve the UX or content of your website. Writing data analysis reports without clear outcomes is not a productive solution, and you will most likely have to revise your reports needlessly as a result.
Consider your Readers’ Expertise
Who are you writing the data analysis report for, and how will it serve your company? Depending on how well-versed in data analysis your coworkers are, you may have to scale the complexity of your reports up or down.
This applies to the vocabulary, abbreviations, and KPIs you use to showcase your findings. If your report will be used in-house, it’s best to set a strict terminology for your reports going forward in the form of an appendix. This will make it easy for both junior and senior staff to use your scraped data without consulting you on how to read the report.
Include Data Visualization Elements
Speaking of accessibility, you should always include visualization elements in your data analysis report to make the document more dynamic and enjoyable to read. Visualization elements such as charts, graphs, diagrams, and pie charts which illustrate your findings will further legitimize your findings in the eyes of readers.
You can include screenshots of what you focused on during your data analysis to make the report more understandable. Don’t limit your analysis to writing and numeric data – expand your creative reach by also creating complementary visualized data for your readers’ convenience.
Proofread & Edit Before Submission
Failing to proofread your own writing can lead to huge consequences and miscommunication between you and your coworkers. Given how data analysis reports rely on abbreviations, empiric research, and inherently niche terminology, you should always proofread your writing before submission.
Writing tools such as Hemingway Editor and Grammarly offers comprehensive editing and formatting features for your convenience, so take time to use them. Make it a habit to read your own data reports before you submit them for everyone to use during web development and related activities.
Stick to a Template Going Forward
Once you write your first comprehensive data analysis report, you will have a much easier time replicating the process as you move forward. You should create a template for data reports which suits your coworkers’ expectations and your own workflow to speed up the process in the future. Typically, data analysis report structures consist of:
- Introduction (with the report’s summary, main findings, and table of contents)
- Body (primary data sources, data analysis method, the analysis itself, and the results)
- Conclusion (main observations and thoughts on the analyzed data, new research possibilities)
- Appendix (terminology explanation and additional resources the reader can follow up on)
While this type of data report may seem too academic, it will undoubtedly make your job as a data analyst much easier and more structured. Once you establish professional expectations with your colleagues in regards to the format of your reports, writing new documents will become more straightforward and enjoyable.
Ask for Second Opinions & Feedback
Lastly, you should always ask for feedback and comments from your coworkers when it comes to submitted data analysis reports. They will look at your writing from a more objective standpoint and be able to advise you on how to improve your data analysis approach.
You should also try to come in contact with other data analysts who will be able to review and comment on your work for the sake of professional development. Do the same with your documents and revisit older reports to spot how you’ve evolved your writing style over time. Learn from your mistakes and keep trying to make the best out of the tools and raw data you have available.
The best way to know whether or not your data analysis reports are successful is to look at your business’ performance.
Look for ways to apply the above-mentioned advice to your data analysis in ways which will coincide with your coworkers’ expectations. Once you settle into analyzing data, you will quickly spot improvement opportunities worth pursuing as you develop your skills as a professional data analyst.
Linda Ferguson started her career in a local company as a content writer 5 years ago. Now, she is the CEO of https://subjecto.com/ thanks to her determination and complete dedication to work. Linda has always been passionate about academia and writing. Besides her busy work schedule, she manages to find time for attending conferences that keep her up to date with the latest news in the industry.