Building effective reports in Impact Hub starts with clear thinking, not complex visuals. Strong reports focus on answering specific questions, using the right data, and presenting information in a way that’s easy to understand and act on.
The best practices below are designed to help new and experienced users build reports with confidence while avoiding common pitfalls.
Start with a simple question
Before opening Builder, decide what you want the report to answer.
Good starter questions include:
How many participants enrolled this year?
Which services were provided most often?
How many sessions took place each month?
Starting with a clear question makes it easier to choose the right dataset, visual type, and filters—and helps keep reports focused.
Understand the basics of datasets
Each dataset represents structured data from an Apricot Data Standard and includes:
Rows, which represent individual records (such as a participant or service)
Fields, which describe those records (such as dates, programs, or outcomes)
Because Data Standards can include multiple forms, datasets may contain repeated records. For example, one participant may appear multiple times if they received multiple services. This is expected, but important to consider when counting people versus events.
Choose the right dataset
When selecting a dataset for a report:
Confirm it includes the forms and fields needed to answer your question.
If you’re unsure what a dataset contains, review the source Data Standard in Apricot.
Look for fields tied to the anchor form, which typically represents your primary record (such as a participant or case).
Choosing the right dataset early prevents confusion later.
Focus on a few core visual types
You don’t need to master every chart type to build effective reports. Start with these three:
KPI or summary cards
Best for simple totals, such as:
Number of participants
Total sessions
Total referrals
Bar or column charts
Best for comparing categories, such as:
Services by type
Participants by program
Outcomes by status
Line charts
Best for showing change over time, such as:
Monthly enrollments
Sessions per month
Quarterly trends
If you’re unsure which visual to use, start with a bar chart—it’s flexible and easy to interpret.
Use filters thoughtfully
Filters help narrow data to what matters most.
Common filters include:
Date ranges
Program or service
Demographics
Active versus inactive status
Apply filters early, keep them simple, and avoid stacking too many filters that may conflict or confuse results.
Use field wells as a mental model
In Builder, visuals are created by placing fields into different roles:
Value – what you’re measuring or counting
Group / Dimension – how data is broken down
Filters – what limits the data
A beginner-friendly pattern for counting people:
Place a category field (such as Program Name) in Group
Place a metadata ID field in Value
Use Count Distinct as the aggregation
This approach produces clearer, more reliable results.
Understand aggregations at a high level
Aggregations describe how data is summarized.
Common options include:
Count Distinct – counts unique people or cases (often the safest choice)
Count – counts all rows, such as services or sessions
Sum – adds numeric values like hours or dollars
Average – calculates a mean
When reporting on people or unique records, Count Distinct is usually the best starting point.
Build one visual at a time
To avoid feeling overwhelmed:
Start with one visual that answers one question
Apply filters
Validate the results
Add additional visuals gradually
You don’t need to build a full dashboard all at once. Reports often improve through iteration.
Use clear and understandable field names
If a field name is unclear:
Review the original form in Apricot
Ask the Data Standard administrator for clarification
Rename calculated fields in Builder to clearly describe what they represent
Clear naming makes reports easier to understand, share, and maintain.
Practice and experiment
Builder is designed for exploration. You can undo changes and experiment freely without affecting:
The underlying dataset
Apricot data
Learning by doing is often the fastest way to build confidence.