Data analytics is one of the most useful elements you could integrate into your business model. Analyzing the abundance of data your business generated over time to make more informed decisions is essential for corporate success in 2021.
According to recent findings, 90% of enterprises say data analytics are pivotal to their digital transformation initiatives; however, 88% of data goes ignored by companies. Making good use of platforms such as BioRegistra by Seamfix and subsequently writing well-structured data analysis reports can fundamentally change the way your business develops. Let’s explore how you can do so, as well as the benefits of relying on data analytics in 2021.
The Benefits of Good Data Analysis Reports
Before we dive deeper into creating data analysis reports, let’s tackle the “why” behind such an initiative. According to published data, 56% of organizations relying on analytics experienced more effective decision-making, and 51% achieved better financial success following data analytics implementation. For all its worth, only 3% of employees are able to use analyzed data quickly, while it takes hours or days for 60% of others.
This makes breaking down gathered data into legible, easy-to-access data reports a must. Even something as simple as organizing your content production pipeline or managing your website ads can be analyzed, extrapolated, and presented through succinct, approachable writing. As such, the practical advantages of writing data analysis reports can be broken down into:
- Proactive participation of your coworkers in decision-making
- Better targeting and personalization of your products/services
- Streamlined moment-to-moment operations for all employees
- Lowered risk of poor business decisions and investments
- Improved detection of workflow flaws and growth opportunities
Steps to Write Good Data Analysis Reports
- Break down your Stakeholders
In order to make good use of your data analysis reports, you need to understand that different professionals will see the reports differently. However, instead of writing several reports based on different reader profiles, your reports should cater to everyone at once. Your stakeholders can generally be broken down into three groups:
Primary audience (coworkers and clients) – these readers will go through your reports top-to-bottom, ask you to clarify certain points, and refer to your report for their own work
Secondary audience (company executives) – executives are often too busy to read through extensive data reports and rarely read through more than your analysis results and conclusions
Tertiary audience (your own supervisor/editor) – this reader profile tends to act as a quality control manager and an assistant in making sure that your reports are well-written per company standards
Regardless of how experienced you are as a data analyst or content writer, high quality of writing should be a priority in your reports. Having access to proofreading services can also help improve your writing skills and ensure that your reports are legible, scannable, and error-free. Take different readers into account in your writing, and the reports you create will be that much more useful to everyone in your company.
- Outline the Data Analysis Report
It’s good practice to standardize your data analysis reports’ format as soon as possible to allow easier indexing and storage for later. Before you write down any tangible data, you should create an outline for your reports going forward. Luckily, data analysis reports set the right expectations through their name already. The sections you should stick to when creating your reports are:
Introduction – where you present the short summary of your report, the questions you address, and present the readers with a table of contents to refer to
Body – this is the primary element of your report, and it can be broken down into several segments, including:
- What data did you gather and in what time interval?
- Which methods did you use to analyze the data?
- What are the results of your analysis (presented as visualized data)?
Conclusion – where you present your own conclusion on the report, address the questions presented in the introduction, and offer potential future analysis and questions to ask
While you can modify and scale your data analysis reports’ format, these segments should serve as a baseline for your writing. Once you start creating reports, you will quickly spot potential improvements and bottlenecks to iron out before settling for a standardized data analysis report format.
- Write your Professional Opinion
As we’ve mentioned, the conclusion of your data analysis report should be reserved for observation, review and opinions. Given that you are the one writing the reports, you should offer your professional opinion on the data you’ve collected from an objective standpoint. Don’t embellish your findings or try to skip reviewing the data – it is important that you advise readers on what to do with the data.
It is expected that there will be those in your company who disagree with your reports, analysis methods or writing expertise. Ask them for their take on the data, what they would do differently and how you can improve your reports for the future. Writing data analysis reports is a technical process, and any feedback you receive will go a long way in making you a better data analyst.
- Make Smart Use of Visuals
Visualized data is one of the best ways to communicate your findings to people unfamiliar with data analysis or empirical research. Graphs, charts, infographics and other forms of visual content with your data at their core will work wonders to ensure that your reports are understandable.
Aim for a balance of visual and written data in your reports but lean toward the latter. While visuals are useful, they shouldn’t be at the center of your reports and instead serve as supplementary materials used for clarification and quick reference.
- Address Terminology in the Appendix
Depending on the industry you operate in, your data analysis reports may need additional clarification and instructions on how to be read properly. This is where appendixes come into play as additional sections which you can write into your reports following the conclusion. Content typically written into the appendix includes:
- Data you’ve discovered but which wasn’t central to your primary data analysis
- Tables and graphs related to your findings but not created by you
- Detailed technical descriptions and clarifications of abbreviations and terminology
- List of tools and parameters you’ve used to analyze the data
You can also use the appendix to clarify certain terms, address why you used certain analysis methods, and offer additional reading sources for your readers. Making good use of appendixes is extremely important if you work with coworkers or clients unfamiliar with the intricacies of reading empiric data themselves.
It’s no small feat introducing a new data analysis method into your business pipeline and having coworkers accept it. The process will undoubtedly be an uphill battle, but you can use the arguments we’ve outlined above to turn the tides in your favor.
Ask your coworkers for feedback as you write and find a system that suits everyone’s workflow to find a good silver lining. Once you strike a balance of technical data analysis writing and easy-to-read formatting, data reports will quickly become commonplace in your company.
Author’s bio. Jessica Fender is a copywriter and blogger with a background in marketing and sales. She enjoys sharing her experience with like-minded professionals who aim to provide customers with high-quality services.