Financial analytics is becoming an increasingly significant factor in various industries such as the banking sector, insurance or investment management, and the healthcare industry. Numerous companies and organizations use…
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What Is an Analysis Report?
In the context of business, there is a report involving quantitative and qualitative data. Data interpretation comes to play here so that the majority can understand any complex data. First of all, analysts are not the only ones who analyze such reports; anyone can interpret quickly with the help of analytical reporting tools. One example is the analysis report. This business report aims to scrutinize, analyze, and evaluate company strategies, processes, and more based on analytic results. Besides statistics, presentations cover various examples, such as historical, predictive, SWOT analysis, and many more.
Did you know that a 2005 study called “Closing the Delivery Gap” concluded that 80% of businesses believe that they displayed excellent customer service? Yet, only 8% of customers agreed to that statement.
Meanwhile, the American Journal of Managed Care reported that the average medical visits spent by most people are around 121 minutes.
On the other hand, Statista conducted a study that the estimated sales for fashion retail were $257.3 billion in 2019.
Why Are Analysis Reports Essential?
Analysis reports have many benefits, just as they have many analytical examples and applications. Do not limit its form to one type of business only since presenting graphs, stats, and related models can be relevant to different industries. For example, hospitals use analytical reports for their studies. The AJMC research survey concluded that the average minutes spent by people in medical visits reach 121 minutes.
On another note, these reports help make decisions. For example, a business might realize their sales have decreased from the data shown. Therefore, they decided to develop smart marketing plans to help boost their sales after the report. That example gives a lesson that interpreting data is not the endgame. The goal is to come up with wiser decisions and strategies to benefit the company afterward. More so, findings encoded in tables, diagrams, or analytical reports can be useful for future researches. Scientists and researchers might take interest from the facts while crediting your report.
Where to Apply Analysis Reports
As we mentioned before, other industries depend on analytical reports. And it helps to recognize where you can apply that report, so you need not limit using it to one situation only. Plenty of applications is equivalent to plenty of opportunities. Without further ado, here are some examples of where to use your analysis report:
How to Make an Analysis Report
Prioritize effective communication in writing an analytics-style report. While you report, make sure that the audience understands. Clarifying things is necessary, as well. Instead of bringing more questions to raise after the report, keep it to the point where you provide more answers. And how do you create the analysis report? Just follow these simple steps:
Step 1: Lessen Technical Writing
When writing analytic reports, it is best to write simply. It is a misconception that reports should only provide visual data, graphs, and organizational charts. Words must exist. Presentations tend to have a lot of technical and business terms—aim to lessen it. Using words that are easy to comprehend and brief is welcomed. First, write an introduction by providing a summary of what the whole analysis report is about. This part lets you introduce the report, so audiences receive the gist immediately about the topic to discuss. If you used too many technical terms or flowery words that could be simplified, then simplify those to deliver the message smoothly.
Step 2: Use the Right Visual Organizers
Of course, analysis reports still include those graphic organizers or infographics for the presentation. However, be sure to use the right tools. Maybe you used the Venn diagram template to showcase statistical data. Venn diagrams are more useful for comparing factors rather than presenting statistics. Thus, a bar graph template would have sufficed than that diagram. Keep in mind that utilizing the wrong tools already indicates that your data interpretation will be doomed. Choose the visuals that are relevant and appropriate instead.
Step 3: Indicate the KPIs
Adding KPIs help everyone understand what aspects were analyzed in the first place. For example, a company that tracks how many visits they receive for their website can make use of visits, new visits, page views, bounce rate, and average time on site for KPIs. What matters is that the appropriate KPIs are present. Furthermore, who you present the report to must understand what every KPI means. Maybe others got confused between two labels of visits. Therefore, clarify that one visit is for the total website visits received while the other one is for the new visits.
Step 4: Expound on Data Interpretation
The critical analysis begins in interpreting and explaining the entire data. Let individuals understand the context. Start by mentioning what legends mean from the symbols and colors used in the chart. Also, this segment is your chance to provide the meat of your report. If people see bar charts from the report, then what? Expound by saying that the monthly sales from the manufacturing business increased or decreased, perhaps. A tip is to input clear descriptions of whatever you did, why, and how it happened. Provide the background and answer the essential questions to complete this.
Step 5: Observe an Organized Narrative
Much like literary analysis, there should be an organized flow to the narrative. Telling a story is another way to explain the analysis report. Following a narrative or plot makes it easy to comprehend since details are well-arranged. In handling this, come up with a sequence regarding what to present first until the last part. There is no need to keep the report poetic, though. Just observe a flow by adapting storytelling to develop a more effective output.
Step 6: Include Practical Recommendations
As you present the results and data, never simply end there. Give beneficial suggestions that help the industry. If sales are down, then what could be the best solution to boost it next time? The point is not to repeat every failure that happens. And if the results are positive, aim to maintain it. But make sure to measure the impact of the result to the recommendation. Otherwise, you might end up using useless solutions that could not change the data for the better.
FAQs
What is the normal process for an analysis report?
Data analysis reports, done by most analysts, consist of five steps. Begin with defining the questions. Those will eventually be answered in the report. Next, prepare measurement priorities. That segment contains KPIs, goals, and objectives. Third, gather the data which should be analyzed. Thus, analyzing such data marks as the fourth step. Lastly, interpret the results which conclude the report.
What are the types of data analysis?
Mainly, there are four types of data analysis. They are the descriptive, diagnostic, predictive, and prescriptive analysis. Each example contains unique features that will help you come up with an efficient report. Learn their differences and use them in the right applications.
What is an example of an analysis?
The analysis breaks down a particular subject into specific components. And you eventually discover what each element does or how they are related to each other. One typical example of that is analyzing the effects of supply and demand in a logistics important business document.
Presenting a graphic organizer, visual representation, or any analytical tool paints a picture of what the data is about. Meanwhile, analytical reports explain the full picture. Thus, interpreting the data should not be out of the process. Sharing the whole gist and message is the target in reporting rather than giving bits of takeaways only. Thanks to an analysis report, you can design or structure a detailed and complete report of any business-driven data.