Every dimension tends to accumulate items that you've stopped using, like old vendors, renamed accounts, or other leftovers from past mappings. Over time, these can pile up, crowding the lists your team selects from and slowing down your entire workspace.

Now you can clear them out in seconds. Abacum spots the items that aren't used anywhere and removes them in one pass. This means your vendor list shifts from a long, cluttered column to a concise list of the vendors you actively work with.

Faster everywhere, because lean dimensions load and calculate quicker across every table, report, and input. And safe, because it offers only genuinely unused items, and removes nothing until you say so.

Learn more about how to Clean up dimensions

Finding the right variable in the Library is now faster than ever. The top bar has new filters that let you narrow a long list down to exactly what you need.

⚡What's new

You can now filter your variables directly from the top bar of the Library. Click + Add filter and choose to filter by Variable, Model, or Active dimension, then pick the values you care about. The list updates instantly, so you can jump straight to the variables behind a specific model or dimension.

Each filter appears as a chip you can edit or remove at any time, and you can stack several filters together to zero in on precisely the variables you want to work with.

⚙️ How it works

Filters combine with AND logic, so a variable is shown only when it matches every active filter. For example, filtering by Model = Revenue and Active dimension = Region returns just the variables that live in the Revenue model and use the Region dimension. 

✨ What's new

The headline is the Variables library: a cross-model view of every variable you can access, in one searchable list. For each variable you see its name, the model it lives in, and the logic behind it, so you can find what you need and understand how it is calculated without opening one model after another. Looking for the variable behind a number in a report? Search it by name and, with one click, jump straight into that variable in its model.

→ What’s next? This is just the start. Next up for the Variables library is a side drawer that opens any variable to show more of its details in one place, without leaving the list.

📈 Impact

• Find any variable fast across every model you can access, from one list.
• Understand the logic at a glance, for the version you choose.
• Onboard faster with a single place to see what exists across the organization.
• Prioritize for AI by flagging the variables that matter most.

⚙️ How it works

Open the Library and select the Variables tab. Search by name as you type, switch the version in the toolbar (Actuals by default) to control the logic shown, and open any variable in its model. You only ever see variables from models you have access to.

You can now trace where each value in a Dimension came from, directly on the dimension page.

A new Dataset sources column shows, for every value, which dataset (or datasets) introduced it. Before you clean up or debug a Dimension, you can always tell where a value originated.

• When a value is fed by more than one dataset, all of them appear on the same row.
• If an unexpected or unassigned value shows up, you can see at a glance which dataset brought it in.
• Filter the Dataset sources column by one or more datasets to focus on a single source, or select Other sources to surface values that weren't introduced by any dataset.

Learn how to trace dimension value sources

Inconsistent data slows everything down, Abacum now fixes it automatically. AI Data Cleaning detects and normalizes raw data values in your datasets, so your data is analysis-ready before you start modeling.

How to get started:

1. Go to the Dataset Manager, open a dataset, and navigate to the Viewer tab
2. Hover over the column you want to clean and click the three-dot menu (⋮)
3. Select AI Data Cleaning — a new column will appear and process in the background
4. Once complete, map the cleaned column to a dimension to use it in your models

👉 Learn how AI Data Cleaning works in the Academy

We’ve introduced three new formulas in Dataset Transformations to help you clean, standardize, and reuse your data more efficiently:

🧩 Clean JSON or list data in one step

From [{"first_name":"Mark","last_name":"Roberts"}] to Mark

You can now extract specific values from JSON objects or lists stored in a column. No complex workarounds, string manipulation, or external preprocessing required.

👉 Learn more about how to use EXTRACT_FROM_OBJECT

🔤 Convert text to upper or lower case

From mark roberts to MARK ROBERTS

Quickly convert any text field to uppercase or lowercase. Perfect for cleaning IDs, aligning formats across systems, and ensuring consistent comparisons and VLOOKUPs across your datasets.

👉 Learn more about how to use UPPER and LOWER

The Input Table is a new planning block that lets you manage manual inputs directly inside Spaces, making updates faster, more intuitive, and far more flexible.

Here’s what you can do with it:

• Edit inputs without leaving the Space: No more pop-up panels. Update values inline while keeping actuals and other versions visible for clearer planning.
• Control timeframes for each version: Set them as fixed or allow them to update automatically as new actuals come in.
• Adjust and save multiple variables at once: Update all inputs in a single table, with performance improvements that make large edits faster and smoother.
• ➕ Improvements you’ve been waiting for: Hide irrelevant columns, show/hide rows that already have actuals, manage all input types in one place, and more.

How to get started:

1. Go to your Space
2. Type / to open the Space blocks menu
3. Select Input Table to start adding and editing your inputs

Want to dive deeper? Check out the full guide in our Academy.

With this release (and more improvements coming soon), Spaces now support a more complete, flexible planning workflow for both finance teams and budget owners.

What's New?

You can now easily rename dimension items across the platform, including versions, modules, and tables/spaces!

How Does it Improve Your Experience?

This feature greatly improves the experience of renaming dimension items from the dimensions page, preventing errors, and eliminating the need for manual updates and complex workarounds when renaming items in use. Edit dimension items autonomously and with confidence from a central location, ensuring consistency throughout the platform's core resources: dimensions, datasets, tables/graphs, filters, variables' formulas, and version inputs.

Understanding the utilization of dataset columns has historically posed challenges. Some dataset columns were "locked" due to their use in dimensions, measures, or transformations, hindering adjustment—a common struggle for users like you.

What's new? With our latest update, we've implemented a solution. You now have the ability to check outgoing and incoming dependencies on your datasets and columns through the info panel in the dataset sidebar. Initially, you'll receive a summary of usage, followed by a more detailed view of dependencies.