Understanding Pension JSON Data: What Business Users Need to Know

You may encounter pension data delivered in JSON files as a financial services professional (such as an asset manager, fiduciary manager, LDI manager, pension administrator, pension scheme analyst, etc). These JSON files contain a range of detailed information, including, but not limited to, pension scheme liabilities (the obligations to pay pensions) and investment allocations (how the scheme’s assets are invested). This guide will help you interpret such data without needing to write code, using accessible tools and step-by-step examples. We’ll explore what JSON is, how to view it in tools like Excel Power Query and online converters, how to trace specific values (like participant-level liabilities or scheme-wide asset totals) in a typical JSON structure, and how to cross-check the data for consistency and sense. By the end, you should feel more confident in reading pension JSON data – promoting data literacy and bridging the gap between IT and business, a mission embodied by platforms like WTP Data Lab.

Why Pension Data Comes in JSON Format

JSON (JavaScript Object Notation) has become a standard format for data exchange in many industries, including pensions. But what exactly is JSON? Simply put, JSON is a text-based data format that uses a structured layout of key–value pairs (like field names and their values) and arrays (ordered lists of values). It is designed to be both human-readable and easy for machines to parse (JSON). In JSON, an “object” is denoted by curly braces { } and contains a set of named fields and values (which can be numbers, text, booleans, or even other nested objects or arrays), while an “array” is denoted by square brackets [ ] and contains a list of values. These structures can be nested to represent complex data relationships. For example, a pension scheme object might contain an array of participant objects with fields like name and liability.

Why are pensions using JSON now? One big driver is the push for standardised, frequent data exchange. For instance, recent regulatory changes such as the Netherlands’ Wet toekomst pensioenen (WTP) – the Future of Pensions Act – mandate more granular and frequent data sharing between pension funds, asset managers, and administrators. The industry responded by adopting common data standards (like the SIVI All Finance Data model) that use JSON to transmit pension information. This means that detailed pension data (covering everything from fund assets and investment returns to participant details and liability values) is now being packaged into JSON files for interoperability (The Future of Pensions Act (WTP) Explained – An Introduction for Pension Professionals – WtpDataLab). JSON is favoured because it’s lightweight, flexible, and widely used in web and data applications – so using it helps different IT systems talk the same language when exchanging pension data.

Similarly, the UK Pensions Dashboards Programme, a landmark initiative aimed at providing individuals with a comprehensive digital overview of their pension savings, mandates the use of JSON schema for data exchange between pension providers and the dashboards. Compliance with these JSON-based standards is compulsory for pension providers in the UK, highlighting the format’s significance in this major program.

For business stakeholders, however, a raw JSON file can look bewildering at first – full of curly braces, quotes, and brackets. The good news is that you don’t need programming skills to make sense of it. With the right approach, JSON can be viewed much like a set of structured tables or an Excel workbook. In the next sections, we’ll introduce tools and techniques to view and interpret JSON easily, focusing on the key information you care about (liabilities and investments) and ensuring the numbers add up correctly.

Viewing JSON Data Without Coding

You might wonder how to open a JSON file if you’re not a developer. Fortunately, there are user-friendly options to convert JSON into a tabular format that’s easy to read and analyse:

Online JSON-to-Table Converters (WTP Data Lab and Foreranger)

An accessible approach is to use a web-based JSON converter, which instantly visualises the JSON structure in your browser. A prime example is the JSON File to Table Converter provided by WTP Data Lab (also available via Foreranger). This tool is designed specifically to help non-technical users make sense of complex JSON data by turning it into clear tables (JSON file to Table Converter – WtpDataLab). To use it:

Open the Converter: Navigate to the JSON to Table Converter on WTP Data Lab’s website (accessible at wtpdatalab.com/json-file-to-table-converter or foreranger.com/json-to-table-converter). You’ll see a simple interface with an input field.
Load Your JSON: You can either paste the raw JSON text into the input box or click “Upload” to select a JSON file from your computer. There’s no need to adjust any settings – once you provide the JSON, the tool will process it automatically.
View the Tables: The JSON structure is instantly transformed into a set of tables on the screen. Each major object or array in the JSON becomes its own table, with properly labelled columns for each field (JSON to Table Converter – Foreranger) (JSON to Table Converter – Foreranger). For example, if your JSON has a section for “participants”, that will appear as a table listing each participant as a row, and columns for each data field (ID, name, liability, etc.). Another table might represent “assetAllocations”, listing each asset class and its value. The converter preserves the hierarchy by grouping related tables and using clear headers (often naming them after the JSON keys) to show how they relate. You can usually expand or collapse nested tables if needed to focus on certain levels of detail.
Copy or Export (if needed): If you want to take the data into another application, the tables are easy to copy and paste. The tool formats them in a way that you can paste directly into Excel or a document, retaining the table structure. This means you can use the online viewer for a quick inspection and then move the data into a spreadsheet or report if required.

One of the key benefits of the WTP Data Lab converter is that it’s built with business users in mind. In fact, it explicitly lists “Business Users: Make sense of JSON data without technical knowledge” as one of its purposes. There’s nothing to install or configure – it’s a zero-friction way to decode a JSON file. Whether you’re analyzing an API response or a data export from an admin system, the converter gives an instant visual breakdown of the JSON content. It essentially serves as a bridge between the raw IT format and a human-friendly view, which is ideal for pension professionals who want to quickly get to the numbers.

Example: JSON Snippet of Pension Data (Liabilities & Investments)

To cement these concepts, let’s walk through a simplified example of pension scheme data in JSON format and how to interpret it. Below is an anonymised JSON snippet representing a pension scheme. (In a real scenario, the file could be much larger, but this example is kept small and generic for clarity.)

{

“schemeName”: “Sample Pension Plan”,

“totalLiabilities”: 800000,

“totalAssets”: 800000,

“liabilitiesByStatus”: [

{ “status”: “Active”, “value”: 500000 },

{ “status”: “Retired”, “value”: 300000 }

“participants”: [

{ “id”: “1001”, “name”: “Alice”, “status”: “Active”, “liability”: 500000 },

{ “id”: “1002”, “name”: “Bob”, “status”: “Retired”, “liability”: 300000 }

“assetAllocations”: [

{ “assetClass”: “Bonds”, “value”: 500000 },

{ “assetClass”: “Equities”, “value”: 300000 }

]

}

In this JSON structure:

Top-level keys give high-level totals and identifiers: “schemeName” is the name of the pension scheme, “totalLiabilities” is the sum of all liabilities (in whatever currency or unit this scheme uses, here 800,000), and “totalAssets” is the total market value of assets (here also 800,000).
liabilitiesByStatus is an array that breaks down the total liabilities by participant status. In this case, it shows two categories: “Active” members have a combined liability of 500,000, and “Retired” members have 300,000. (500,000 + 300,000 = 800,000, which matches the totalLiabilities – a good sign the data is internally consistent.)
participants is an array of individual participant records. Each participant object has an id (identifier), a name, a status (which corresponds to the categories above), and a liability value – presumably the actuarial liability or account balance attributable to that person. In our snippet, we show two participants: Alice (Active) with 500,000 liability, and Bob (Retired) with 300,000 liability. In a real file, this array might contain hundreds or thousands of participants, each with their own liability values.
assetAllocations is an array detailing how the scheme’s assets are allocated. Here we have two asset classes: “Bonds” valued at 500,000 and “Equities” valued at 300,000. The sum of these is 800,000, which matches the totalAssets. This could represent, for example, that the fund is invested 62.5% in bonds and 37.5% in equities (percentages we could derive if needed).

Reading the above JSON in raw form, we can already pick out important information, but it’s much easier to interpret when converted into tables. Let’s imagine using the JSON to Table Converter on this snippet. We would get a couple of tables: one for participants and one for asset allocations (and possibly a single-row table for the top-level totals, though we can easily see those totals from the snippet).

Participants Example Table 1 — Table 1: “Participants” table extracted from the JSON. Each row represents a participant with their attributes. Here we see two participants – Alice and Bob – and their respective liability values. The table format makes it simple to scan IDs, names, statuses, and numeric liabilities. In a large scheme, you could scroll through many such rows or use filters (in Excel) to find specific individuals or subsets (e.g., all “Retired” members). Notably, if we sum the Liability column for all listed participants (500,000 + 300,000 in this example), it equals the totalLiabilities value of 800,000, which we see in the JSON’s top-level data. This provides a quick check: the individual liabilities indeed add up to the reported total.

Asset Allocations Table 2 JSON — Table 2: “Asset Allocations” table extracted from the JSON. This shows the scheme’s investments by asset class. Each row is an asset category with its value. The Bonds and Equities here sum to 800,000, which matches the totalAssets figure. In a more complex case, you might have many asset categories (e.g. cash, real estate, various fund holdings), but the idea is the same – you can verify that the breakdown adds up. If the JSON also provided percentage allocations or target ranges, those could be cross-checked as well. But even with just absolute values, a business user can easily see the asset mix and totals in this tabular view.

From these tables, an LDI manager might immediately observe the liability split between actives and retirees and consider how the asset mix (e.g., heavy in bonds) aligns with those liabilities. An asset manager or fiduciary manager could quickly note the current allocation and possibly compare it to an investment policy. A pension scheme analyst might use the participant-level table to identify the largest liabilities or to feed into further analysis (like calculating average liability per member, etc.). The key is that the JSON, once in table form, is no longer an IT-centric artefact – it’s a straightforward data table ready for analysis.

Tracing Specific Values in a JSON Structure

When dealing with a large JSON file, you’ll often want to find specific information – for example, “What is the liability value for a particular participant?” or “Where is the total assets figure stored?”. Here are some tips for tracing values in the JSON (assuming you’ve loaded it in a viewer or Excel as described above):

Use Meaningful Keys: JSON keys are usually named descriptively. In our example, keys like “totalLiabilities”, “participants”, or “assetAllocations” clearly indicate what data they hold. So, if you’re looking for liability-related data, scan the JSON (or the list of tables in the converter) for keywords like “liability” or “liabilities”. Likewise, terms like “asset”, “investment”, or specific asset classes (e.g., “Equities”) hint at investment data.
Search Textually: If you have the JSON open in a text viewer or the code view of the online tool, use the search (find) function. Searching for a participant’s name or ID (if you know it) can jump you right to that person’s record in the JSON. For example, searching for “Bob” in the raw JSON would locate the entry for Bob and show his liability value. In the WTP Data Lab converter, you could switch to the “Code View” (which shows the JSON text in a nicely formatted way) and use your web browser’s find function (Ctrl+F) to do this.
Navigate the Hierarchy: JSON is hierarchical. If you’re exploring manually, identify the section (object) that likely contains the value. For instance, total scheme-wide values often appear at the top level or in a summary section. Participant-specific values will be under a participants array. In the converter’s output, you might first look at a high-level “Scheme” table (if provided) to see totals, then click into the “Participants” table for individual details.
Cross-References: Sometimes, data is repeated or referenced in multiple places. For example, our JSON had a breakdown by status and also individual entries with status tags. If you wanted to trace all liabilities for “Retired” members, you could either sum the relevant category in liabilitiesByStatus (as given) or filter the participants table by status = Retired and sum their liabilities. Both should yield the same result (300,000 in our case). If they don’t, that’s a clue something is off in the data!
Follow Documentation or Standards (if available): If the JSON follows an industry standard like SIVI’s AFD 2.0, it might come with documentation of each field. For example, a field might be named something like accruedBenefitAmount or marketValue as per the standard. Understanding these definitions can guide you to the values you need. In the absence of formal docs, rely on the self-descriptive names and context.

In practice, using the table view from the converter is the easiest way to trace values because you can visually scan and use filters. For instance, suppose you want to check a specific participant’s liability: in the online tool, you could copy the participants’ table into Excel or a Google Sheet and apply a filter on the Name column to find that person. The tables allow you to manipulate the data just like any spreadsheet – sort by liability to see the largest obligations, filter by status, etc. This empowers non-developers to drill into the data with confidence.

Checking Data Consistency and Making Sense of the Numbers

Having the data in hand is one thing – interpreting and validating it is the next. Here are some sanity checks and analysis tips to ensure the JSON data “makes sense” in a pensions context:

Do totals match their components? Always verify that any total provided equals the sum of the parts you can find. For example, compare totalLiabilities to the sum of all participants’ liability values. In our example, they matched perfectly (800k). If you found a discrepancy – say participants sum to 790k but totalLiabilities says 800k – you’d want to investigate why. It could be a data issue, or perhaps the total includes something not listed in the participants (e.g., a reserve or an adjustment). Similarly, check that asset allocation categories add up to the total asset figure. This cross-check is a basic validation that often catches errors or omissions in data. In a well-structured JSON (especially one following a standard), these should align by design.
Check value ranges and outliers: Look at the distribution of values. Does each individual liability value fall in a realistic range given what you know about the scheme? For instance, if most participants have liabilities in the tens or hundreds of thousands, a single entry showing “5,000,000” should draw your attention – is this a special case (perhaps a high earner or a mistake in decimal placement)? For investments, if one category unexpectedly has a zero value or an extremely high value, confirm if that’s expected (maybe the scheme temporarily has no cash allocation, or perhaps an asset class was misclassified). Using the sorting feature on columns is helpful – sort liabilities descending to see the largest obligations or sort asset values to see the smallest allocations.
Verify logical relationships: In pensions, certain numbers are logically related. For example, if the scheme is a Defined Benefit plan, you might compare total assets to total liabilities – this gives a sense of the funding level (e.g., assets equal 100% of liabilities in our sample, indicating full funding in that hypothetical scenario). If assets were much lower than liabilities (say 80%), that could indicate an underfunding issue – something a stakeholder would note. If the JSON provided both market values and some sort of “smoothed” or actuarial values, differences between them should make sense given the context (perhaps due to different valuation dates or methods). Ensure that any percentages (like allocation percentages or liability splits) either sum to 100% or are in expected proportions.
Consistency across data points: If the JSON has multiple sections, ensure they don’t contradict each other. Our example had a liabilitiesByStatus array and also individual status on each participant. If, say, a participant was marked “Retired” but their liability was somehow counted under “Active” in the summary, that would be a red flag. Likewise, if there was a field for total number of participants and it didn’t match the count of entries in the participants array, something would be off.
Date and currency checks: Often, JSON data will include dates (e.g., valuation date) or currency units. Make sure you note the effective date of the data – liabilities and asset values are as of when? If you have JSON files from multiple periods, you would compare the right ones. Also be mindful of units: some data might be in dollars, pounds, or euros (the JSON might not explicitly show a currency symbol). If it’s a multinational context, confirm which currency or if values are, say, in thousands. Generally, documentation or filenames might hint at this (for example, a file might be named “Scheme123_2024-12-31.json” indicating valuation as of end of 2024).

Performing these checks transforms you from just a data consumer to a critical analyst – you’re ensuring the data is reliable enough to base decisions on. It’s analogous to how you’d review an Excel report or a valuation summary: you’d ensure totals match subtotals, and numbers align with expectations or prior knowledge. JSON is just another format containing that information. With practice, you’ll get a sense of what each field represents and which values should reconcile with each other.

Bridging IT and Business: Embracing Data Literacy with Tools like WTP Data Lab

The shift toward JSON for pension data exchange is part of a broader digital transformation in the industry – and it presents an opportunity for business-side professionals to enhance their data literacy. Instead of waiting for IT departments to translate data into reports, tools like the ones we discussed enable you to access the raw data directly and interact with it. This empowers asset managers, LDI managers, and analysts to validate figures, explore details, and gain insights on the fly, making the decision-making process more agile and informed.

WTP Data Lab, in particular, positions itself as a bridge between the technical and business worlds in pensions. By providing user-friendly JSON tools and educational resources, it helps demystify the new data formats introduced by regulations like WTP. For example, a complex SIVI-standard JSON file that might have been only understandable to a programmer can now be visualized by a pension analyst in tables and charts with a few clicks. The heavy lifting (parsing nested structures, handling syntax) is done behind the scenes so you can focus on the substance – the liabilities and investments reflected in those numbers.

Conclusion

Interpreting pension-related JSON files is very achievable for non-developers once you break the task into steps: convert the JSON to a readable format, identify the key figures (liabilities, assets, etc.), and then apply your domain knowledge to analyze and cross-check the data. By leveraging WTP Data Lab JSON Converter (and extending your analysis by moving data into Excel), you turn a once intimidating JSON file into a set of familiar tables. From there, it’s just like working with any financial data – ensuring totals align, spotting trends or outliers, and asking informed questions. As the pension industry increasingly embraces data standards and digital communication, these skills will only grow in importance. Embracing tools that foster transparency and understanding will not only save you time but also enhance trust in the data you’re using for critical decisions.

Armed with this knowledge and toolkit, you – the pension professional – can confidently navigate JSON data and extract the insights you need. In doing so, you’re participating in the broader trend of data-driven pension management, where clarity and accuracy of data lead to better strategies and outcomes. And remember, you’re not alone in this journey: platforms like WTP Data Lab are there to support and continuously bridge that gap between IT and business, so that data format is no barrier to informed action. The era of “JSON for Asset Managers” and “pension analytics for all” is here, and it’s opening up new possibilities for analysis and collaboration in the pensions sector. Happy data exploring!

Sources

JSON to Table Converter – WTP Data Lab/Foreranger tool description (JSON file to Table Converter – WtpDataLab) (JSON file to Table Converter – WtpDataLab)
WTP Data Lab Journal – Future of Pensions Act (WTP) and JSON data standards (The Future of Pensions Act (WTP) Explained – An Introduction for Pension Professionals – WtpDataLab) (The Future of Pensions Act (WTP) Explained – An Introduction for Pension Professionals – WtpDataLab) (The Future of Pensions Act (WTP) Explained – An Introduction for Pension Professionals – WtpDataLab)
JSON.org – JSON format introduction and structure (JSON)