Experts Explained: Breaking Down Data Walls with Infor EPM

Your business just invested in Infor EPM, a robust performance management solution, but you are still puzzled with many questions in your mind.

Perhaps you want to know how EPM stores, manages, and processes data with its ‘cubes”? Or maybe you are wondering how EPM is different from traditional SQL Star Schema and what benefits each brings. How about that integrating different data sets issue that is waiting to be solved?

Read more:(Almost) Everything You Need to Know About Infor EPM

You have come to the right place! TRG has compiled this comprehensive guide, drawing on our team’s deep expertise to provide clear, concise answers to your most common queries.

Read on and learn with us!

‘I experience problems when integrating disparate data sets.’ 

Solution for Cloud SAAS IOS

Businesses can opt to load data into the Infor OS (IOS) Data Lake (a cloud platform that is capable of collecting/ingesting data from various sources to a single, centralised repository). This central database serves as a data feed for the organisation’s existing financial and non-financial enterprise systems.

EPM can directly interface with the raw data in the Data Lake. This enables the extraction and loading of data into its dedicated staging database, thereby facilitating subsequent Extract, Transform, Load (ETL) processes.

Read more:Don’t Let Your Data Lake Become a Data Swamp – Here’s What You Can Do

Solution for On-premise Infor EPM

On-premise EPM offers built-in data storage, which is based on SQL Server. Users can utilise SQL Server tools to consolidate disparate data into the EPM staging database. This approach also allows users to directly interface EPM with disparate sources.

Regardless of the deployment model, users can load data into EPM cubes using EPM scripting, a function that provides data access directly from the Infor OS Data Lake or other accessible relational data sources. Once the data is efficiently loaded into EPM Cubes, associated dimensions can be updated for future analytical requirements and dimensional configurations.

Watch our on-demand webinar:Budgeting – From Manual to Agile

‘What are the benefits of using EPM over a traditional SQL Star Schema?’

Understanding the Structure of EPM Cubes

EPM cubes are made up of various dimensions, each representing a specific category or collection of similar items, such as account codes, department codes, time, or dates. The data is loaded against an intersection point of those items.

Within these cubes, actual data values are stored at the intersections of these different dimensions. For instance, a particular data point might represent the “Sales Revenue” (from the “Account Codes” dimension) for the “Marketing Department” (from the “Department Codes” dimension) in “January 2025” (from the “Time Dates” dimension).

Read more:How Raymond James Financial Slashed 50% of Its Reporting Time

These cubes become a significant advantage of Infor EPM – data is loaded into memory much faster than accessing it via a SQL star schema. Having the data readily available in memory can help accelerate analysis and reporting processes.

Storing data in an EPM cube is incredibly efficient, allowing businesses to store extensive amounts of data spanning many years within a relatively small digital footprint (only a few megabytes). This is possible due to how dimensions are managed.

Within each dimension, each unique data point (or “member”) can only exist once. Once it is defined, it can be referenced by multiple data points in a cube.

Automatic Aggregation in EPM

EPM takes advantage of each member’s uniqueness to enable automatic data aggregation (data roll-ups) in many different ways.

For example, using the period dimension.

In the example above, the figures for OCT 2025, NOV 2025 and DEC 2025 are all ‘aggregated’ to the 2025_Q4 member, and ultimately, aggregated to the 2025 member. This automatic aggregation creates a consolidated figure for the parent member (2025) based on its child members (the individual months).

Additionally, EPM also allows users to create alternative aggregations/ roll-ups within a dimension, offering different perspectives on the data without duplicating the underlying information.

For example, a “Year-to-Date” (YTD) aggregation:

In this example, the data is displayed in different aggregations without expanding the dataset. The base-level member (monthly values) only exists once and only consumes the memory once. From a computational point of view, the value is only calculated once for the engine in a dimensional aggregation.

Star schemas, on the other hand, have to read the dataset based on the index in a SQL table, then reference it in separate data tables. While SQL databases use indexing and caching mechanisms to optimise performance, users still lose significant time associated with data retrieval and processing for each aggregation request.

EPM Reporting

EPM provides robust reporting features, allowing users to build their own role-based dashboards that can bring together data from disparate sources while also allowing them to drill down to the transactional level.

This is possible through a thick client tool* called Application Studio with a specific set of features for designing and building reports as well as navigation menus and other interactive components (e.g., widgets).

Read more:Why Infor EPM & SunSystems Are Such a Dynamic Duo for Finance Leaders

Within Application Studio, users can access the “databases”, which are pre-configured as part of the data connections in EPM Administration. This allows users to utilise different data types and database connections or configure a data source to connect to an Infor EPM database. This allows for multiple EPM applications to be integrated into one seamless reporting solution, i.e., centralising them into one single, unified hub to gain an overview across the business.

The solution also provides tools needed to configure relational sources to multiple database types.

The final part of the data journey is the ability to load files into EPM and process them as required.

Any data type can be attached to EPM and is used for reports. With CSV and Excel reports, EPM can process the data stored within these files to load into the staging database and then organise them into EPM Cubes.

Cases in point:

For example, you are loading CSV-based budgets as an attachment. The system can then automatically map and load this data into the EPM system, significantly reducing the manual effort and time involved in this process.

Or perhaps you want to attach quotes, which are taken from an asset management system, into EPM. Same process as in the previous example, EPM automatically maps, loads, and makes these files immediately viewable for any approvers. Furthermore, the quotes can be compared against the actual spending recorded in the system, allowing for detailed variance analysis.

Finally, data can certainly be exported from EPM to be sent to the Infor Data Lake, and from there, it can be further exported to other reporting/ financial management systems as required.

*Note: A thick client tool is a type of application that performs processes directly on the user’s device instead of a remote server.

Got a question and want TRG’s experts to answer? Share any queries you might have via our Idea page here. Don’t forget to subscribe to our bi-weekly newsletter to be among the first to receive business and IT insights!