The Data Your Retirement Platform Already Has

A $48 Trillion Industry Running on Instinct

The retirement industry generates more participant-level financial data than almost any other sector in financial services. And almost nobody is using it.

Every day, retirement recordkeeping platforms process contribution allocations, payroll feeds, investment transactions, distribution requests, loan payments, and beneficiary updates for tens of millions of Americans. Behind those transactions sits an extraordinarily rich data set: participant demographics, salary histories, savings rates, asset allocations, employer match structures, vesting schedules, plan design configurations, and behavioral patterns spanning years or decades.

This data exists. It is collected. It is stored. And in most organizations, it goes almost entirely unused for anything beyond transaction processing and regulatory compliance.

Metric	Value	Source
US retirement assets	$48.1T	ICI, Q3 2025
Americans in 401(k) plans	92M+	GAO, 2024
Total DC plan participants	140M	PLANSPONSOR, 2025

Plan sponsors default to bare-minimum match formulas — typically 50% of 6% — because nobody is showing them data-driven alternatives. Participants save at whatever auto-enrollment default was set years ago, because nobody is analyzing whether that rate is actually adequate. And recordkeepers compete on fees and platform features rather than on measurable participant outcomes, because the data to demonstrate those outcomes sits untouched in operational databases.

The retirement industry has a data execution problem. Not a data collection problem. Not a data storage problem. An execution problem.

The firms that figure out how to use the data they already have — to optimize plan design, improve participant outcomes, and demonstrate measurable value to sponsors — will define the next era of retirement services. Everyone else will be processing transactions while the industry moves past them.

The Government Is Pushing Money Into the System

SECURE 2.0 — signed into law in December 2022 — introduced 92 separate provisions affecting retirement savings, with implementation dates spanning from 2023 through 2033. This is not a one-time regulatory event. It is a decade-long structural expansion of the retirement system.

• Mandatory auto-enrollment. Starting 2025, new 401(k) and 403(b) plans must automatically enroll participants at 3–10%, with annual auto-escalation of at least 1% up to 10%. More participants, more contributions, more data, more platform demand.
• Small employer tax credits. Employers with up to 50 employees can receive tax credits covering 100% of plan startup costs for the first three years.
• Student loan matching. Employers can now treat student loan payments as elective deferrals for matching purposes. This requires new data flows between payroll, student loan servicers, and recordkeeping platforms.
• Roth catch-up requirements (2026). Higher-earning participants age 50+ must make catch-up contributions on a Roth basis.
• Emergency savings accounts. Plans can now offer pension-linked emergency savings accounts, requiring new account types, contribution logic, and withdrawal processing capabilities.
• Expanded part-time worker eligibility. Part-time workers with 500+ hours over two consecutive years must be allowed to participate.

And yet most firms are treating SECURE 2.0 as a compliance exercise: implement each provision by the deadline, update the plan document, move on. Very few are asking the strategic question: what does this data tell us about how to design better plans, engage participants more effectively, and demonstrate more value to sponsors?

Where the Data Goes to Die

If retirement firms have access to such rich data, why aren't they using it? The answer isn't laziness or indifference. It's structural.

The data is siloed. In a typical retirement operation, participant data lives in the recordkeeping platform. Payroll data lives with the employer or payroll provider. Compliance testing runs in separate tools. Participant communications are managed through another system. Plan documents are maintained elsewhere. Investment data comes from custodians and fund companies. None of these systems were designed to talk to each other — and most still don't.

The platforms are legacy. Over 800 billion lines of COBOL code remain in daily production use across financial services. 43% of core banking systems are built on COBOL, and retirement recordkeeping platforms share this infrastructure lineage.

The budgets are consumed by maintenance. Enterprises allocate 60–80% of their IT budgets to maintaining legacy systems. When keeping the lights on consumes most of the budget, data strategy becomes a slide in a planning deck rather than an operational reality.

The talent understands operations, not analytics. The average COBOL programmer is 55–58 years old. Approximately 10% of the COBOL workforce retires annually.

The culture defaults to compliance. Retirement operations have always been compliance-driven, for good reason. EBSA recovered $1.384 billion through enforcement actions in FY 2024. But the result is a culture where "using data" means "passing the nondiscrimination test" — not "analyzing whether our plan design is actually helping participants retire."

Introducing the RDEQ Framework

To help retirement organizations understand where they stand, we developed the Retirement Data Execution Quality (RDEQ) framework — a structured assessment of how well a firm uses the data it already has.

RDEQ is not a technology maturity model. It is not a checklist of tools or platforms. It is a measurement of data execution — the degree to which data actually drives decisions, outcomes, and competitive advantage across five critical areas of retirement operations.

How RDEQ Scoring Works

Each of the five RDEQ categories contains 10 specific capabilities. Each capability is scored on a maturity scale:

Score	Level	What It Means
0	No capability	We don't do this at all
3	Ad-hoc	We do this sometimes, manually, or for some plans
5	Developing	We have a process but it's not standardized
8	Established	We do this consistently with defined processes
10	Optimized	We do this proactively and it drives decisions

Maximum RDEQ score: 500 (5 categories × 10 capabilities × 10 points). A score of 400 represents a realistic ceiling for a well-run organization. A score of 250 represents the threshold for "established and operational" across the board.

The Five RDEQ Categories

1. Plan Design Intelligence — Max 100 pts

Are you using data to optimize plan structures — or copying what you did last year?

Most firms set a default enrollment rate, a standard match formula, and a default investment option years ago and never revisit them. The data to make better decisions exists in their own systems — participation rates by demographic, opt-out patterns after auto-enrollment, savings adequacy projections, peer benchmarks. It just isn't being used.

Capabilities scored: auto-enrollment rate optimization · match formula benchmarking · auto-escalation effectiveness tracking · investment lineup optimization · QDIA selection using demographics · SECURE 2.0 provision adoption analysis · fee structure competitiveness · loan/hardship pattern analysis · plan design peer benchmarking · vesting schedule optimization.

2. Participant Outcomes & Engagement — Max 100 pts

Can you tell a participant whether they're on track to retire — or just tell them their balance?

67% of private-sector workers have access to a DC plan, but only 53% actively participate. That 14-point gap represents millions of Americans who have a retirement plan available and aren't using it. Among those who do participate, most are saving at whatever default rate was set when they enrolled — often 3%, which is almost certainly not enough.

Capabilities scored: retirement readiness scoring · savings adequacy gap analysis · behavioral segmentation · personalized communications · multi-channel engagement tracking · distribution pattern analysis · beneficiary data completeness · missing participant identification · demographic-targeted interventions · satisfaction measurement & action.

3. Sponsor Intelligence & Retention — Max 100 pts

Are you using data to demonstrate value to plan sponsors — or waiting for the RFP?

The recordkeeper landscape has shrunk from approximately 400 providers to about 50 in the past 15 years. Consolidation is accelerating because scale is becoming essential. In this environment, retaining plan sponsors is existential — and the primary reason sponsors leave is feeling like their plan is just a number.

Capabilities scored: plan health dashboards · peer benchmarking reports · fiduciary risk identification · sponsor satisfaction tracking · revenue/profitability analysis · sponsor attrition prediction · cross-sell opportunity identification · custom reporting & visualization · regulatory readiness reporting · committee-ready materials from data.

4. Compliance & Regulatory Execution — Max 100 pts

Are you using data proactively for compliance — or just reacting to deadlines?

This is where most retirement firms score highest — because compliance is mandated. But even in compliance, most firms operate reactively: test when required, file when due, correct when caught. Proactive compliance — early-warning nondiscrimination analysis, real-time monitoring dashboards, automated error detection, regulatory change impact modeling — is rare.

Capabilities scored: NDT automation & early-warning · Form 5500 preparation efficiency · SECURE 2.0 implementation tracking · regulatory change impact analysis · audit trail completeness · error detection speed · compliance monitoring dashboards · correction tracking & trending · forfeiture management · plan document amendment tracking.

5. Data Infrastructure & Accessibility — Max 100 pts

Is your data foundation integrated and accessible — or are you running analytics on spreadsheets exported from a mainframe?

This is the foundation category. If recordkeeping, payroll, compliance, communications, and investment data sit in separate systems with no integration layer, every other RDEQ category is constrained by default. You cannot build participant-level retirement readiness scoring if the data to power it requires manual exports from three different systems.

Capabilities scored: cross-system data integration · data quality monitoring · business user analytics access · real-time data availability · structured + unstructured integration · data governance policies · security & access controls · API adoption (SPARK guidelines) · scalability of infrastructure · cloud-native analytics capability.

Where We Expect the Industry to Score

Based on 15 years of working inside retirement platforms, our experience-based estimate of industry-average RDEQ scores looks like this:

Category	Estimated Industry Average
Compliance & Regulatory	~50 / 100
Data Infrastructure	~38 / 100
Sponsor Intelligence	~32 / 100
Participant Outcomes	~28 / 100
Plan Design Intelligence	~25 / 100
Estimated industry average total	~173 / 500

If these estimates are even directionally correct, the implications are significant. The retirement industry is operating at roughly a third of its data execution potential. The highest-scoring category — compliance — still represents only half of what's possible, and it's the one area where firms are essentially forced to use data by regulation.

The categories with the most business value — plan design intelligence and participant outcomes — score lowest. These are the areas where data-driven decisions could measurably improve retirement readiness for millions of Americans and create genuine competitive differentiation for the firms that invest in them.

What High RDEQ Firms Do Differently

The firms that score well on data execution share a common set of behaviors:

• They treat data as a product, not a byproduct. In high-RDEQ firms, data is curated, integrated, and made accessible as a strategic asset. The recordkeeping system is not just a transaction engine — it's a data platform.
• They invest in integration before analytics. The most common mistake is buying an analytics tool before connecting the data sources.
• They use data to serve sponsors, not just report to them. High-RDEQ firms send plan health assessments, peer benchmarks, and proactive recommendations — not just quarterly statements.
• They measure participant outcomes, not just activity. Account balances are not outcomes. Retirement readiness is an outcome.
• They build compliance forward, not backward. With 92 SECURE 2.0 provisions rolling out over a decade, high-RDEQ firms model the impact of each provision before it takes effect.
• They pair domain expertise with technical execution. A data scientist who doesn't understand vesting schedules will build the wrong model. A plan administrator who can't query a database will never get beyond spreadsheets.

A Practical Starting Point

Improving RDEQ is not a single initiative or a technology purchase. It is a sustained shift in how a retirement firm treats the data it already possesses. That said, there are practical steps firms can take now:

1. Assess your current RDEQ honestly. Score your organization against the 50 capabilities in this framework. Don't inflate the numbers.
2. Pick one category to improve first. Attempting to raise all five categories simultaneously is a recipe for stalling.
3. Invest in integration before analytics. If your recordkeeping, payroll, and compliance data are not integrated, fix that before building dashboards.
4. Build cross-functional teams. Data execution in retirement is not an IT problem or a business problem. It's both.
5. Use SECURE 2.0 as the catalyst. With 92 provisions rolling out over a decade, SECURE 2.0 is the best forcing function the industry has for improving data infrastructure.
6. Partner for depth, not just capacity. Improving RDEQ requires people who understand both retirement operations and modern data infrastructure.

The retirement industry sits on one of the richest data sets in financial services. The government is actively expanding the system. The technology to use this data strategically exists today.

The only question is which firms will use it — and which will keep processing transactions while the opportunity passes them by.