Dashboard filters, anyone?
It’s a known fact that dashboard filtering is essential to good data management.
As someone who works with data for a living, you know that you don’t always need to see everything simultaneously. It can even be overwhelming or distracting trying to analyze specific data when you’re sifting through a bunch of irrelevant (to that particular task) data points.
You can use several types of dashboard filtering processes for data management and analysis, and it can be confusing to know which is the best option for you.
That’s where this guide becomes helpful. Learn more about the value of filtering features and the differences between three primary types of filtering — associative, cross-filtering, and global — below.
The right filtering features can completely transform your data management and analysis approach. After all, it allows you to narrow the focus of the whole dashboard in just a few quick clicks.
You can use filtering for various data types, including the following:
You can also create unique filters for any particular type of data you need to analyze regularly.
With filtering features, you can personalize a dashboard to fit your unique needs and preferences. This personalization often helps you be more productive since you can access critical information instantly.
The following are some other benefits of dashboard filtering:
Dashboard filtering helps you shorten the process of reviewing and analyzing data. It can also help you create new sub-datasets or modify existing datasets before importing them.
Professionals can use data filtering to keep sensitive information safe. For example, they can create different filters that change the appearance of the dashboard and the type of information other users have access to.
Dashboard filtering makes it easier for users to interact and engage more closely with their data. By narrowing their view to focus on the most relevant pieces of information, they can gather more insights from the remaining data and avoid overwhelm or accidentally missing something essential.
Dashboard filtering works for many professionals in different industries and situations. The following are some scenarios in which a dashboard filter would be valuable:
Say a company is trying to provide 15 different business units with relevant data.
One option for this company is to create one dashboard with simple access controls.
This approach would result in 15 different dashboards and dozens of reports to sift through. Furthermore, if a report needed to be changed, someone would have to go in and manually make the change on each dashboard.
Another option is to offer one master dashboard with a drop-down filtering tool.
This tool would allow users to select the appropriate business unit and access all the reports with data relevant to that particular unit. You could also incorporate additional filters for key metrics and specific date ranges.
Imagine you run a nationwide company with businesses in 15 different states.
If you wanted to quickly see how different branches of your company compare to one another, you could use dashboard filtering to narrow information by factors like geography and location.
Filtering allows you to access relevant data sooner and get a better understanding of how each business is performing, as well as how their performance affects the company as a whole.
This information will also help you make more informed choices about each branch moving forward.
As you can see, dashboard filtering is handy in many situations and for many professionals. However, the type of filter you use is just as important as the act of filtering itself.
Three common types of filtering are associative, cross-filtering, and global filtering.
Curious about which one is right for you? The critical differences between these types are explained below to help you decide what’s most beneficial to your organization.
Imagine you have a dashboard featuring eight different charts or data tables.
With the associative filter option, you could click on one bar of one chart, and the seven remaining charts would update based on that one click. You can instantly see the relationship of data between the selected value in all the other charts.
The data analytics software company Qlik is responsible for developing the concept of associative filtering. They also include a special Associative Engine in their products, allowing you to use this option during your data management processes.
Qlik claims that its associative filtering option offers the following benefits:
When you use Qlik’s associative engine and other tools, all relationships are created automatically by the Qlik Sense feature. Qlik Sense also automatically adjusts data models based on adjustments the user makes (such as removing or adding tables).
While many other products describe a similar behavior to what is described above, Qlik, the patent holder, is the sole vendor with Associate Filters, as the logic and intelligence is built in their Associative Engine.
So what about other products you’ve seen with similar behavior?
They are using cross-filtering style dashboards. We’ll explain those next.
Many vendors, such as Power BI and Looker Studio, have attempted to develop their own version of Qlik’s associative filtering feature with cross-filtering. The end result from cross-filtering is similar to that of associative filtering. If you click on one element of a chart, the same parameters get applied to the same component of other charts.
Many data analytics tools offer cross-filtering features. Some of these tools provide automatic relationship detection similar to Qlik. However, they also still require the user to carry out some manual tasks.
For example, a user may have to create secondary relationships between different tables. They might also need to override connections for multi-directional cross-filtering.
Cross-filtering also doesn’t work when widgets that were generated from data models don’t have any relationships with each other.
Global Filtering, also known as Value Filtering, is a more straightforward approach to filtering.
Imagine the dashboard with eight charts or tables from above. With a global filter feature, you could select a value in a filter, and any of the eight charts you have would update based on the value you picked.
Global filtering delivers the same efficiency as cross-filtering and associative filtering. The main difference is that these charts aren’t connected, nor do they communicate with each other. However, all charts still filter based on the values you choose in the global/value filter.
You can create simple filters based on one criterion, like name, date, or account number. You can also create more complex filters based on multiple criteria, such as a specific date and account number.
Global filtering is a simple, manageable, and effective data management process. It allows you (and other users) to have complete control over your data and how it’s presented.
This control enables you to conduct analyses more efficiently and quickly evaluate the most relevant data for a particular task.
Global filtering can also save you from running into some of the most common complaints people make regarding associative and cross-filtering.
For example, some users complain that associative filtering doesn’t work well with large data sets and that the unique query language is more complicated than other programming languages.
As for cross-filtering, some users have also noticed that when they try to filter by too many factors (especially when it comes to using bi-directional cross-filtering), their program slows down and interferes with their productivity. This slowdown goes against one of the primary benefits of dashboard filtering, which is increased efficiency.
Short answer: global filtering.
Allow us to explain how and why.
DashboardFox offers the ability to create and apply filters and saved views to your data, which allows users to quickly and easily analyze specific subsets of data.
Filters can be created by selecting fields from the data and setting the desired filter criteria for each field. For example, you could filter a sales report by a specific date range or filter a customer report by a specific category.
Saved views allow users to save a specific set of filters and settings to easily recreate a custom view in the future. This feature is especially useful for users who regularly analyze the same subsets of data.
Once a filter or saved view is applied, the dashboard will dynamically update to show only the data that meets the specified criteria. This allows users to more easily identify trends or anomalies in their data.
Overall, DashboardFox’s filter and saved view features can help users save time and make more informed business decisions by allowing them to quickly analyze specific subsets of data that are relevant to their needs.
But we’re never saying we won’t add a cross-filtering feature. We realize it’s a power analysis feature. As a customer-driven organization, if it’s a feature you want, add it to our roadmap and let’s discuss; we’ll put a priority on it.
Why else is DashboardFox a good choice compared to other BI tools?
DashboardFox offers a one-time payment scheme, which spells convenience to you.
DashboardFox’s one-time payment scheme is perfect for businesses that want to control their expenses. It provides a one-time expenditure that eliminates the need for continuous subscription payments, which can save businesses money in the long run.
By offering this option, DashboardFox provides a personalized approach that caters to the unique needs and preferences of different businesses.