Most teams treat Birst like a simple reporting tool. They log in, open Visualizer, click around, and consume the results without giving much thought into how they were generated. And when something seems off, they turn to the dashboard, the data, or the tool itself.
But Birst is not a single, flat reporting layer application. It is a stacked system consisting of security rules, subject areas, data modeling relationships, and row-level filters that all operate beneath the surface (it’s pretty robust!)
And yet, users often only interact with the top layer, but it’s the layers underneath that quietly determine what data they can see, how dashboards behave, and why two people looking at the same report may not see the same results.
The issue is not that Birst is complicated. It’s that users are rarely taught how these layers work together and lack the proper skillset to fully take advantage of the application.
Below are five things most users don’t know about Birst that shape every dashboard they open and every report they build, and why training plays a key role in helping organizations leverage these types of reporting tools.
1. Users See the Data Birst Is Configured to Show Them
Many users assume that if two people open the same dashboard or run the same report, they will always see the same numbers. In many organizations, especially those that do not use row-level security, this is exactly the case. Everyone receives a unified, consistent view of the data.
However, Birst can be configured to tailor data visibility based on a user’s role, business unit, or responsibilities.
When those settings are in place, Birst scopes the underlying dataset to match what each user is allowed to see, and dashboards will naturally reflect that access. Nothing in the dashboard itself has changed; only the user’s permitted view of the data has.
This difference is not a sign of inconsistency or malfunction. It is the system working as designed, enforcing the organization’s governance rules behind the scenes.
The takeaway here is that when users are not aware this configuration exists, they may misunderstand what they are seeing or assume something is missing.
This is just one example of how training helps users maximize application use. Within Birst, users must recognize when their visibility is intentionally scoped and when they are looking at a shared, enterprise-wide dataset. They don’t know what they don’t know!
With greater application understanding, dashboards remain a reliable source of truth, and users can interpret results with more confidence and fewer false alarms.
2. Subject Areas Dictate Which Fields Logically Belong Together
Business users often believe Subject Areas within Birst are simple folders that help organize fields. In reality, they are curated business lenses that determine which measures and attributes should be used together.
Subject Areas group fields by underlying process, such as Accounts Payable, Sales, Inventory, or Procurement, and they present versions of those fields that are safe to combine.
When a user grabs a field from one Subject Area and tries to combine it with something unrelated from another area, the report may return data, but the joins may not follow the business logic users expect.
This often leads to discrepancies between dashboards and ad-hoc reports and fuels the perception that “nothing matches.” Subject Areas are there to prevent that problem, but they only work if users understand what each one represents.
3. Users Don’t Understand the Difference Between Birst Logic & ERP Logic
Many users assume Birst mirrors every calculation, field definition, and data relationship exactly as it appears in the core ERP. But Birst’s semantic layer may use different logic for rollups, time periods, aging buckets, fiscal calendars, invoice statuses, or performance metrics.
This leads users to compare a Birst KPI directly against a transactional inquiry and assume something is wrong when the two don’t match perfectly.
What they are really seeing is the difference between a transactional system designed for processing and an analytic system designed for summarizing and trending.
Without training, users may think Birst is inaccurate when it is actually aligning the data to a reporting-friendly structure.
Once users understand how Birst creates analytic logic, they become much better at interpreting results—and much less likely to assume mismatches are errors.
4. Visualizer Won’t Prevent Users From Building Incorrect Reports
Visualizer protects users from technically invalid combinations. If a field cannot be joined to the current context, Birst grays it out. If a measure belongs to a fact table with no shared grain, the user simply cannot select it.
In other words, Visualizer prevents impossible joins and incompatible structures.
But Visualizer cannot protect users from logical mistakes. A user can stay entirely within a valid Subject Area, select fields that technically belong together, and still build a report that misrepresents business details.
For example, they might choose a measure that summarizes differently than they expect, filter on an attribute that narrows the dataset in unintended ways, or pair a metric with a dimension that changes the meaning of the result. All of this is allowed because it is technically valid, yet it can still lead to contradictions between dashboards and ad-hoc reports.
The lesson here? Users must be cautious and have a complete understanding of the attributes they’re using when creating a report.
This is yet another example of when hands-on training becomes essential. Users need to know why a metric behaves the way it does, what each Subject Area represents, and how filters or grain affect the outcome.
Without that context, Visualizer follows instructions faithfully, but the results may tell the wrong story. With scenario-based training, users learn how to recognize when a report is drifting away from the underlying business logic even if the system isn’t stopping them.
5. Birst Security Shapes the Entire Analytics Experience
Most users believe security only controls whether they can log in. In Birst, security determines much more than that. It defines which Subject Areas appear in Visualizer, which dashboards show up in the catalog, which rows of data a user can retrieve, and which columns are even visible to them.
Security can also limit which features they’re allowed to use, whether they can drill into KPIs, and which filters apply automatically behind the scenes. When users do not understand this, they attribute unexpected behavior to bad data or broken dashboards.
When they do understand it, they realize Birst is enforcing data governance in a very intentional way.
This concept applies across the broader Infor ecosystem as well, which is why we continue to emphasize the long-term value of ERP training as a foundation for user adoption.
Getting More From Birst
Once organizations recognize that Birst is a layered system instead of a simple reporting tool, the entire analytics experience becomes clearer. Users stop assuming discrepancies signal bad data. They recognize when security is shaping their view. They stop comparing numbers across users with different access. They stop building reports that contradict the underlying model. And most importantly, they finally trust what they see.
If your organization relies on Infor Birst, giving users this foundational understanding is one of the most impactful steps you can take toward more accurate, consistent reporting.
Our upcoming webinar explores these concepts in more detail, focusing specifically on how Birst security influences user experience, report behavior, and data visibility.
Register below to gain a clearer picture of how Birst works beneath the surface and how to set your users up for success.
Birst Reporting & User Experience FAQ
1. Why might two users see different results in Birst?
Different results only occur if the organization intentionally uses row-level security or role-based visibility. When those settings are enabled, Birst personalizes the data each user is allowed to see. In organizations that do not use row-level security, all users see the same dataset. Understanding how access is configured helps users know what the expected behavior should be.
2. Why do dashboards sometimes behave differently for different users?
Dashboards include logic that adapts to a user’s permissions, such as which tiles appear, which drill paths are available, or which filters are shown. When access differences exist by design, these variations are normal. Training helps users understand what is standard behavior versus what may require investigation.
3. Why do ad-hoc reports sometimes differ from system dashboards?
Dashboards typically use curated definitions and vetted Subject Areas, while ad-hoc reports give users more flexibility. Even if Birst blocks technically incompatible fields, users can still create valid combinations that reflect a different business logic than the dashboard. This is where training becomes essential, because accuracy depends on understanding which Subject Area supports which process.
4. Does Visualizer prevent incorrect reporting logic?
Visualizer prevents technically invalid joins by graying out incompatible fields, but it cannot prevent logical mistakes. A report might be technically valid yet still misrepresent the business if filters, measures, or attributes are interpreted incorrectly. Training teaches users how to recognize and avoid these situations.
5. Why doesn’t Birst always match ERP screens exactly?
ERP applications are designed for transaction processing, while Birst is designed for analysis and trending. Measures, date logic, aging rules, fiscal structures, or status definitions may differ between the analytic layer and transactional screens. This does not mean the data is wrong; it means each system is serving a different purpose. Training helps users understand these distinctions so they interpret results correctly.
