Simple Tables 7.4: making data easier to understand — in more languages

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I’m part of the team behind Simple Tables at Simpleasyty, and over the last few months we’ve been receiving an increasing amount of feedback from teams using Confluence in very different environments. Some of that feedback has been about features, some about usability, and some about something much more fundamental: language.

When we started building Simple Tables, the original problem looked deceptively simple: tables are everywhere in Confluence, but as soon as the amount of information grows, they can become surprisingly difficult to read, maintain and use.

That observation has shaped almost every release since then. Simple Tables has gradually moved from making tables easier to display towards making structured information easier to work with inside Confluence. Today, teams can import structured data, assign data types to columns, search and filter information, group records, calculate new values, aggregate columns, create pivot tables and improve how large datasets are presented without having to move that information into a separate spreadsheet or reporting tool.

Version 7.4 continues in that direction, but it also marks another step for us: Simple Tables is becoming a more international product.

For the first time, the Simple Tables interface is available in English, German and Japanese, automatically following the language selected in Confluence.

Why German and Japanese?

There is a tendency in software to treat localization as something that comes much later: first build the product, then grow, and eventually translate it. Our experience over the last few months has made us look at that sequence differently.

German and Japanese were not selected simply because they were next on an internal localization roadmap. We added them because companies and people working with Simple Tables specifically asked us for them. Those conversations made us look more closely at the scale of the markets behind those requests, and the numbers are significant.

Germany has approximately 3.2 million enterprises employing 38.3 million people, according to the German Federal Statistical Office (Destatis). The European Commission's 2025 SME Country Fact Sheet estimates that around 2.57 million German businesses are SMEs, representing 99.6% of enterprises in the non-financial business sector and employing almost 17.9 million people, or around 58.4% of its workforce.

Japan presents a similarly striking picture. According to Japan's Small and Medium Enterprise Agency, there were approximately 3.365 million SMEs in Japan in the latest enterprise census published by the agency, representing 99.7% of all Japanese businesses.

Those numbers help put localization into perspective. German and Japanese are not simply two additional entries in a language selector. Behind those languages are millions of companies and tens of millions of people working in organizations where documentation, processes, project information and structured data are part of everyday operations.

This is particularly relevant in the Atlassian ecosystem. Atlassian serves more than 300,000 customers across more than 200 countries and territories, including more than 80% of the Fortune 500. When the underlying platform is used globally, the ecosystem around it increasingly has to think globally as well.

For us, German and Japanese are therefore the beginning rather than the end of localization in Simple Tables. Instead of translating the product into dozens of languages simply to increase a number on a feature list, our intention is to continue prioritising the languages that customers and partners actually tell us they need.

Localization is also about more than understanding what a button says. People who spend several hours every day in Confluence repeatedly interact with concepts such as columns, filters, formatting, grouping, dates, numbers and configuration options. Removing small pieces of unnecessary cognitive friction can make a tool feel substantially more natural, particularly when it becomes part of an everyday workflow.

A percentage should look like a percentage

Localization may be the most international change in 7.4, but much of the release is focused on something equally basic: making data easier to understand at a glance.

A value such as 72 contains information, but a value displayed as 72% communicates its meaning more clearly. Add a simple visual progress bar and, in many situations, the reader can understand the state of that value without having to consciously process the number first.

Version 7.4 introduces a dedicated Percentage number format, using a 0–100 scale, configurable decimal places and an optional progress bar displayed alongside the value. Importantly, this is a presentation layer rather than a transformation of the underlying information, so numeric values remain available for operations such as sorting, calculations and reporting.

We have extended the same idea with value-based percentage bars. Editors can define thresholds and display values using familiar red, amber and green states. The thresholds can represent situations where a higher number is positive, such as completion, SLA achievement or utilisation, or situations where lower values are preferable, such as defect rates, risk levels or response times.

That changes the way a large dataset can be read. In a project portfolio containing fifty rows, for example, a reader no longer has to inspect every percentage individually to understand where attention may be required. The overall pattern in the data becomes visible much earlier.

This is the kind of visual improvement we are interested in adding to Simple Tables. The objective is not to make tables more colourful for its own sake, but to reduce the amount of effort needed to interpret the information they contain.

Making Simple Tables feel more native to Confluence

Another theme running through 7.4 is consistency. If an app is going to be used frequently inside Confluence, we believe its visual language should feel familiar rather than introducing an entirely separate design system.

Colors are one example. Table colors, column colors, percentage bars and Enum colors now use the familiar 21-color Confluence palette. Editors can select those colors directly or allow elements such as percentage bars to inherit the accent color of the table theme.

It may sound like a relatively small change, but visual consistency matters when a page combines native Confluence content with information rendered by an app. Using the same palette reduces unnecessary differences between the two and makes the resulting page feel more coherent.

Dates have received similar treatment. Date columns can now use a familiar Confluence-style date pill, helping imported or structured information sit more naturally alongside native Confluence content.

Across a table containing dozens or hundreds of values, those repeated visual conventions can make information significantly easier to scan.

We have also introduced conservative automatic recognition of percentage and currency values in consistently structured data. Simple Tables supports several ways of bringing information into a table, and 7.4 reduces some of the manual configuration required when the structure of that information is clear.

The word “conservative” is deliberate. Automatic detection is useful only when it can save work without silently changing the meaning of the information. Where data is ambiguous, we prefer not to guess.

Large tables make small usability decisions important

Many real-world Confluence tables look very different from the small datasets normally used in product screenshots. Operational tables can contain many columns, many rows and a surprising amount of configuration.

At that scale, relatively small interface decisions become much more noticeable.

In version 7.4, we have redesigned part of the Columns editor so that only one column configuration panel is expanded at a time. Its compact header remains visible while the editor scrolls through the settings for that column. On tables with many fields, this reduces scrolling and makes it clearer which column is currently being configured.

We have also added Fit content as a column width option. Simple Tables can calculate an appropriate width using the relevant header, cell or total value instead of requiring editors to repeatedly experiment with manual sizes.

These are unlikely to be the kinds of features that appear in a product headline, but they matter once someone has configured ten, fifteen or twenty columns. Product maturity is not only about adding major capabilities. It is also about removing small pieces of friction from tasks that people repeat every day.

From tables to structured information

Looking back at the evolution of Simple Tables, we have noticed a broader pattern ourselves.

We started by focusing primarily on how tabular information was presented. Over time, the product has expanded into data types, searching, sorting, grouping, aggregations, calculated columns, imports, Excel and JSON support, filtering, pivot tables and increasingly sophisticated ways of working with information.

The common denominator is becoming less about the table itself and more about structured data inside Confluence.

That distinction matters because organizations use Confluence for far more than traditional documentation. It is used for project management, product specifications, engineering knowledge, compliance processes, inventories, operating procedures, decision records and many other workflows. A large proportion of that content contains information with an implicit structure: an owner, a status, a business unit, a review date, a system, a risk classification, a cost or a priority.

Once information has a consistent structure, it becomes much easier to work with. It can be filtered, grouped, compared, calculated and reported on. That is increasingly the territory in which Simple Tables has been evolving.

It has also led us to another problem that we have been exploring.

We are working on something new

While building Simple Tables and talking to teams using Confluence at scale, we repeatedly encountered the same underlying challenge: organizations often have valuable structured information distributed across hundreds or thousands of pages, but defining that structure consistently and maintaining it over time can be difficult.

A project page may require an owner, status, business unit, risk level and review date. A security page may need a system owner, classification, control status and next assessment. A product page may require a completely different structure.

With a handful of pages, those patterns can be managed manually. Across hundreds or thousands of pages, the problem starts to look much more like data management.

That is one of the areas we have been exploring in another Simpleasyty project called Simple Metadata.

We are still working on it, so this is not intended as a product announcement yet. The underlying idea is to explore how reusable schemas and typed metadata can make structured information in Confluence easier to define and manage at scale.

There is a natural relationship between that problem and what we have learned from Simple Tables. One is concerned with defining and maintaining structured information; the other is increasingly concerned with helping people explore, understand and report on it.

We will share more once there is more to show.

What we learned from 7.4

For us, version 7.4 is useful not because it contains one enormous headline feature, but because several of its changes point in the same direction.

Percentages become easier to interpret. Thresholds make patterns visible earlier. Colors and dates feel more consistent with Confluence. Large datasets require less configuration work. Imported information can be understood more intelligently. And German- and Japanese-speaking teams can now use Simple Tables in the language they already use in Confluence.

Taken together, those changes reflect the direction we are trying to follow: making structured information more useful without making the experience more complicated, and using visualisation where it helps people understand data rather than simply decorating it.

With more than 300,000 organizations using Atlassian products around the world, and with markets such as Germany and Japan representing millions of businesses between them, localization and usability increasingly become part of the same conversation: how do we make tools feel natural for the people who actually use them?

Version 7.4 has given us a few more answers to that question, and plenty of new things to think about for the next releases.

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Data sources: German Federal Statistical Office (Destatis), Structural Business Statistics; European Commission, 2025 SME Country Fact Sheet — Germany; Japan Small and Medium Enterprise Agency, enterprise count based on the 2021 Economic Census; Atlassian FY2025 Form 10-K and Atlassian customer information.