The tool or set of tools used for reporting is the technological backbone of any reporting system, encompassing databases, data warehouses, data lakes, Extract, Transform, Load (ETL) processes, and reporting software, all while accommodating the needs of different users, from data scientists to business executives.
Since the early 90’s, Excel was the undisputed king of analytics. With the rise of “big data” and the emergence of increasingly sophisticated business insights (BI) tools, such as Tableau, PowerBI, Thoughtspot, and a host of new entrants each year, there are now more efficient methods of managing and analyzing data.
There are shortfalls of using raw data and Excel. So, what are the features and functionalities we should expect our modern reporting tools to do?
There are a variety of technical and important features that happen behind the scenes of analytics tools and should primarily be managed by your reporting partner. Examples include data preparation, data modeling, security, user administration, and authentications. Tool performance and accessibility also fall into this bucket of functional features as we would like to assume that your content will load when you need it and in a timely manner.
That said, at a high-level, the ease and availability of the following foundational capabilities are what you should look for in an effective reporting tool:
Find: The first and simplest, but I believe the most important, is that users must be able to find standard content quickly and easily. Having a library full of incredible dashboards is of little use if the user cannot find what they need. It can be as simple as a clear structure of folder hierarchy, something more complex like the use of tags or groupings, or my favorite, which is using synonyms and keywords that allow users to enter a few words into a search box to find all related content.
Customize: Next is a user’s ability to customize content. Let’s say your BI tool’s library of standard content has a report that meets 95% of your needs. If the tool does not allow you to customize that last 5%, the default is then to extract and build (which we’ve already demonstrated should be avoided). The customization may be something simple like preferring a stacked column over a pie chart or maybe adding in a company logo and colors. It could also be more complex like custom grouping and lookups, custom formulas, or what-if scenarios. By allowing customization, users can stay directly connected to the data and maintain the benefits of working within the tool.
Automate: The purpose of technology is to help simplify tasks. An analytics tool is no different and should offer automation capabilities. Some people ‘love’ logging into a BI tool and spending hours digging into the insights. Others do not even want to look through a report. For these ‘non-user’ users especially, automation helps to make analytics more enjoyable and digestible. The easiest way to do this is to automatically deliver reports, insights, and recommendations directly to them. Your reporting tool should allow users to set a schedule and forget it, with the desired content summary sent directly to their inbox. Additionally, your tool should be able to set recurring or threshold-based alerts, so you don’t even need to read a report.
The tool should monitor your key performance indicators (KPIs) and alert you if a target has been hit or missed. For example, it should send me an alert when 90% of my monthly carbon budget has been reached. Or, if there is a sudden percentage change in my online adoption. Additionally, the tool should intelligently know which data each recipient has access to. Meaning, if I need to send a report to multiple users with different levels of data access, the tool should be intelligent enough to automatically modify the report, sharing only relevant data to each recipient.
Interact: The ability to interact with content is a must. Our dashboards and reports should tell stories. Much like the “choose your own adventure” books many of us grew up reading (or reading to our kids), there are many different paths we can take while on our reporting journey. Core interactable functionality includes the ability to drill down, show underlying data, implement dynamic filters, and apply brushing and linking (cross filtering) just to name a few of the basics. Essentially, if we see something that interests us, we should be able to easily delve into it for more insights and details.
Collaborate Data extraction often leads to working in silos. Your tool should allow for content to be shared, viewed, managed, and edited by numerous people and simultaneously in real-time. Collaboration in reporting tools enhances communication, decision-making, and overall team effectiveness, leading to better outcomes for organizations. Whether it’s tracking financial data, monitoring progress, or analyzing performance metrics, collaborative reporting tools play a vital role in achieving your program and business goals.
What’s next?
A successful analytics framework should help you discover, interpret, and communicate opportunities and challenges for your program, employees, and business. This begins with ensuring you have access to Data that is complete, relevant, accurate, and timely. But data on its own holds little value, especially if it’s unorganized. The power of data lies in structuring it into meaningful Content to illuminate patterns, anomalies, and trends—often revealing answers to previously unrecognized problems. Creating this content should never require you to start from scratch and should always be customizable. We may all be looking for similar answers, but we may not all process information the same way. This is where having a flexible tool is a must. Tools are the basis for facilitating various user interactions from finding to customizing content, automating tasks for efficiency, and enabling collaboration.
Once you have established the effectiveness of your data, content, and tool, you can enhance your analytics framework through more sophisticated features such as AI-powered insights and predictive modeling—many of which CWT is already exploring and testing in our own BI platform today.
See also the 2 other blog posts in this series about travel reporting:
A step-by-step guide to turning content into actionable insights
Fine Figures: How to get the most value out of your travel data
