Data Reporting: Methods, Examples, and Best Practices
Turning raw data into valuable insights and crisp ideas is at the heart of any data-driven organization, and data reporting is an important link in this process. Good data reports effectively organize information, highlight essential correlations and KPIs, and provide stakeholders with a solid basis for data-driven business decisions.
In this article, we’ll explore types and methods of data reporting. We’ll also see some real-life examples of various reports, and discuss the best practices and tips on how to automate reporting.
What is data reporting?
This is the process of gathering data from various sources, analyzing it, and presenting the findings in a concise form. Typically, the purpose of such reports is to allow decision-makers to assess the situation and come up with specific data-backed action points. Reports can take various forms – from simple tables and graphs to automatically updated dashboards that show key metrics in real time.
Data visualization is an important part of data reporting. Information, presented in a visual format, is more compelling and easier to work with. Another major trend is automated data collection. As the number of data sources keeps growing, updating reports manually is often counterproductive and too time-consuming if you need to do this regularly. Automation allows you to streamline this process and make data management and data reporting seamless and effortless.
Why is data reporting important?
When performed effectively, data reporting brings many tangible benefits, such as:
- Enabling data-driven decision-making. When all the latest data from across the organization is instantly available in one place, this creates ultimate transparency. Through data analysis, stakeholders can explore the situation from various angles and take action exactly where it’s needed.
- Monitoring performance. Be it tracking business performance, analyzing cash flow, or looking into employees’ performance on certain projects – efficient data reporting is crucial. Monitoring performance is important for accessing current progress and adjusting strategy accordingly.
- Building forecasts. Data reporting allows you to analyze historical data, blend it with information on expected changes, and build projections for the future. This is a great way to assess the feasibility of new initiatives or prepare in advance for an increase in customer demand or an upcoming business challenge.
- Enhancing efficiency. Quality reports allow professionals to identify the best-performing strategies, as well as define areas of improvement. This helps optimize processes, use funds efficiently, and allocate resources proportionately to what a task requires.
- Improving collaboration. Presenting and discussing reports allows everyone to be on the same page. If you share the information on crucial KPIs and actionable insights across the organization, it helps to better align different departments and coordinate their efforts.
Data reporting methods
There are various methods of organizing data for reporting purposes, which result in building different types of reports. Which method to choose depends, first of all, on the goal of the report, its audience, and the type of data you need to present. Let’s explore the main methods.
| Data representation method | Description |
| Tabular report | Information in such a report is presented in the form of a table. This is a handy format to organize large amounts of numerical data – for example, in financial reports. Tabular reports are often created in spreadsheets, although other options are also possible. |
| Narrative report | These reports mainly consist of textual descriptions containing detailed analyses of a specific topic and explanations of the key findings and takeaways. Narrative reports are common for academic research, market research, and so on. |
| Graphical report | Such reports are the opposite of narrative ones – they mostly contain visual elements and include little text. Information is typically presented with graphs, scorecards, and various charts (such as bar charts, pie or donut charts, Gantt charts, and so on). This is useful when you have a lot of various data types to present. |
| Dashboard | Dashboards are similar to graphical reports as they include a variety of visual elements. Besides graphs and charts, they can also contain tables, scorecards, and other elements. Dashboards are typically interactive and often self-updating, which allows you to monitor data in real-time. |
| Infographics | Infographics are similar to graphical reports as they also represent data mostly with visual elements. The difference is that infographics are not limited to using charts or graphs and often include illustrations, icons, roadmaps, etc. This often requires the work of a designer, so such reports are often visually compelling. |
In addition to this, regardless of the data representation type, reports can be:
- Static. As the name suggests, data in static reports does not change and cannot be manipulated. Once it’s included in a report, it always remains the same.
- Interactive. Such reports allow the reader to work with the data – filter it, sort, extract it for custom calculations, explore it from different angles, and so on.
- Self-updating. If you create an automated report, data presented in it can be updated automatically as the latest information from your data sources will be continuously channeled into your report.
As we’ve already mentioned, this can be applied to any report type – for example, a tabular report can be static, or it can be interactive and/or self-updating, depending on how it’s built.
Main types of data reporting
There are many types of data reporting. We’ve summed up the most common of them in the table below.
| Reporting type | Description |
| Real-time reporting | Reports of this type reflect any changes in data instantly or near-instantly. This is a great option for working on projects that require continuous monitoring. |
| Ad-hoc reporting | In this case, custom reports are built on request when the need arises – for example, to look into an unexpected problem or opportunity. |
| Regular reporting | This type of reporting, on the contrary, implies a planned activity performed at regular intervals – for example, weekly, monthly, or yearly reports. |
| Descriptive reporting | Such reports are typically focused on presenting data on a process or project in as much detail as possible to capture the complete picture. |
| Prescriptive reporting | In general, reports of this type are better suited for business needs as they are focused on making data actionable. The goal of such a report is to provide data analysis and help define further steps that should be taken. |
| Predictive reporting | These reports are typically based on historical data and involve using machine learning algorithms and building predictive models to identify future trends. |
These types can overlap – for example, you can perform regular descriptive reporting or ad-hoc predictive reporting.
Data reporting examples
Now, let’s take a look at some real-life examples of data reporting. In this section, we’ll focus mainly on dashboards as this is arguably one of the most useful reporting types for business needs. This type of data visualization allows you to present data in the most concise and actionable way. It’s also a perfect format for monitoring performance and tracking progress.
Sales leads report
This is an interactive self-updating dashboard that allows you to analyze where your leads are coming from and to track the dynamics of your sales process. This visualization is a useful tool that can help manage your sales effort, build accurate predictions, and make data-driven decisions.
As this report is interactive, you can zoom in to explore the data in granular detail from various angles. For example, if you click on a specific area on the map, the other graphs will adjust accordingly to reflect the information related to this particular region. You can also select different time ranges for the report.
View the interactive version of the Sales leads dashboard in PowerBI.
This dashboard self-updates automatically without any manual effort. This is possible because it was created with the help of Coupler.io, a popular data analytics and automation platform. It allows you to collect data across multiple sources, gather it in one place for analysis, and turn it into actionable insights. In this case, Coupler.io was used to connect various data sources to a spreadsheet, which, in turn, is connected to this dashboard in PowerBI. Coupler.io has an Automatic data refresh functionality – thanks to it, fresh data from all the linked data sources is regularly loaded to the destination file on a custom schedule.
You can use Coupler.io to build such self-updating dashboards yourself – or, if you need help with creating an advanced visualization or with deriving insights from your data, you can use Coupler.io’s data analytics consultancy services. As a result, you will get a tailored solution so that your team can use it to make data-driven decisions on a daily basis.
Customer service report
This visualization allows you to monitor customer support team performance and workload. This dashboard was created by Coupler.io’s data analytics team. It’s fully interactive, and all the metrics update automatically according to the specified schedule. You can switch between different channels and time periods to dive into detail. A visualized report like this one can be built to monitor the performance of any team.
View the interactive version of the Customer service dashboard in Looker Studio.
Ecommerce KPI report
This visualized report is designed for tracking an ecommerce store’s main KPIs via omnichannel reporting. It’s a great way to analyze sales funnel metrics and conversion dynamics in order to determine where the most conversions come from and how the strategy could be improved. This report is also interactive and self-updating, which allows monitoring performance in near-real time. When the latest data from across multiple sources is easily available in an easy-to-understand visualized format, this provides a great foundation for timely and informed decisions.
View the interactive version of the Ecommerce KPI report in PowerBI.
Ad campaigns report
Another example is part of a marketing report focusing on ad campaign performance. This dashboard gives you a concise overview of ad campaigns across different channels – in this case, LinkedIn Ads and Google Ads. However, Coupler.io, which was used to create this report, allows you to connect over 60 various apps as data sources to power such visualizations with a stream of fresh data. As for the marketing apps, it also supports Google Analytics 4, Instagram Ads, Facebook Ads, and others.
When analyzing or presenting your ad data, you can switch between different channels, campaigns, products, time periods, and more.
View the interactive version of the Ad campaigns report in Looker Studio.
Other data reporting examples
See more report examples on Coupler.io’s website in the Actionable dashboards section.
How to automate the data reporting process – a practical example
Automation is a crucial part of a successful reporting process. From having a full-scale custom reporting system to using simpler tools, automation provides an enormous efficiency boost.
Here are some benefits of automating the reporting process:
- Saving time – when your reporting process is automated, you save a significant amount of time because you don’t need to collect ever-changing data across multiple sources to update your reports manually.
- Enabling continuous monitoring – with auto-updated reports, you always have access to the latest data and can closely monitor projects or processes in near-real time.
- Ensuring regularity – automation allows organizations to streamline their reporting process and perform reporting regularly without any manual work.
- Efficient data management – with automation, you can effortlessly collect data across your company and bring it to one place for analysis. This allows you to actually use the data you accumulate on a daily basis.
In our example, we will show you how you can easily automate your dynamic reporting process with the help of Coupler.io. We’ve already mentioned this data analytics and automation platform when we talked about the self-updating dashboards. Coupler.io’s data integration solution can also be used to automate other types of reports, such as tabular reports in spreadsheets, for instance. The tool’s functionality allows you to automatically populate your reports with fresh data from 60+ business apps, such as Salesforce, HubSpot, Pipedrive, WooCommerce, Artable, Shopify, and many more.
Here’s how you can automate your data reporting process using Coupler.io:
- Once you sign up, you can create your first importer on the My importers tab. It’s very simple and only takes a few minutes, no tech knowledge is needed.
- Select the app you want to extract data from and specify where you want to transfer your information. In this example, we will export data from Google Analytics 4 to Google Sheets. As we’ve already mentioned, you can extract data from dozens of different sources and transfer it to Excel, Google Sheets, or BigQuery.
- Then, connect your GA4 account and specify what metrics should be included into your report. If you want to export data from another app, this step will be similar. You will need to connect your account and select the data type to extract.
- Once this is done, you can connect the destination file and specify where exactly your data should be transferred.
- Next, you can set up a custom report scheduling for the automated updates. Specify the preferred update interval (from every month to every 15 minutes), the time range, and the days of the week when you want Coupler.io to transfer fresh data from the selected source to your destination file. For example, for a weekly report, you can schedule an update for every Friday at 9:00 am – and the report with fresh data will be ready every week without any manual effort. If you use this spreadsheet to power a self-updating dashboard and want to monitor your project closely, you can schedule an update for every 15 minutes.
- Then, run the importer, and Coupler.io will transfer your data automatically. Here’s an example of raw data exported from GA4 to Google Sheets:
After this, you can create a new sheet for your report and connect it to this sheet with row data. If you want to combine data from multiple sources, you can use the same flow to transfer this data to separate sheets and then connect them to your report.
If needed, this tabular report in a spreadsheet can be used to build a self-updating dashboard using Excel’s or Google Sheets’ native functionality. Alternatively, it can be connected to PowerBI or Looker Studio for creating advanced visualizations.
If you need help with visualizing or analyzing your data, you can contact Coupler.io’s data analytics consultancy team.
Data reporting best practices
Now, let’s explore some of the best practices that can help you improve data reporting and make it more effective.
- Focus on the audience
When you are planning to work on a report, it’s best to begin with analyzing the audience and their goals. Then, you can structure your report according to what your audience needs. For example, C-level executives will likely be interested in a high-level overview of your project, while people working on your team might need much more detailed information to see how things are going and what needs to be adjusted. Asking for feedback is also a great way to learn what the target group might need and how to improve your reporting.
- Use automation
The process of collecting information and updating your regular reports might be tedious and time-consuming when performed manually. With automation, the speed, efficiency, and quality rapidly increase. As digital transformation gains momentum, businesses that fail to implement automation will be falling behind the competitors who leverage it successfully. Using automation for reporting data can be quite simple and affordable, so this option is worth considering, regardless of the scale of your business. In the previous section, we have already explained how you can easily automate the reporting process with simple-to-use tools.
- Visualize your data
Arranging your data into a visual story is a very effective approach. It helps your audience quickly understand what the main correlations are, grasp the gist at a glance, and focus on what matters most. Various graphs, charts, tables, as well as comprehensive dashboards, make working with data easier and improve collaboration. It’s even better if you make these visualizations interactive to enable filtering data and zooming into particular details.
- Make it a regular activity
Of course, reporting data is valuable even if you only perform it from time to time. But if you want to make your organization data-driven, you will need a solid basis for informed decisions. Data that is incomplete or simply not fresh enough may result in giving a distorted picture and lead to wrong or untimely decisions. If the process of collecting and analyzing data is regular or, better yet, continuous, this will provide decision-makers with the ultimate transparency they need to gain actionable insights.
- Use templates
Even though you might need to prepare custom reports for some purposes, many reports can be standardized. Ready-to-use templates let you save time and implement a unified approach across the organization. This improves collaboration and helps keep everyone on the same page. You can find a lot of various templates online, but it’s usually best to create a set of custom templates in-house.
- Select meaningful data
Even if everything about your project seems worthy of attention, there’s no need to include all the data you have. As we’ve already mentioned, you should consider the needs of your audience. What decisions do they need to make after reading your report? What information might they need for this? Answering these questions will help you select relevant metrics and make your report concise, clear, and effective.
How to improve data reporting?
The tips, best practices, and examples provided in this article can help you streamline the data reporting processes in your organization. One of the main takeaways is that automation and data visualization are an important part of this process. So, if you want to improve data reporting, it’s strongly recommended to use graphical reports, build dashboards, and make them self-updating with the help of automation. This will ensure timely access to fresh and accurate data, which is necessary to enable data-driven decisions.
Another way to improve your reporting further is by asking for feedback. As every report must meet the needs of its audience, requesting their opinion can give you valuable ideas on how to make your reports more helpful and meaningful.
One more option is requesting professional help. For example, Coupler.io’s data analytics consultancy team can assist you with creating analytical reports and advanced visualizations, taking your data reporting to the next level.
