DATA MANAGEMENT CAPABILITY ASSESSMENT AND BENCHMARKING FOR A GLOBAL INSURANCE AND FINANCIAL SERVICES ORGANIZATION

Client background

A global insurance and financial-services organization engaged Element22 to take a practical step toward scaling analytics and AI. The objective was not to “improve governance” in the abstract. It was to ensure the organization could consistently provide the right data to the right teams and platforms, with the quality, lineage, and controls needed to support business priorities and emerging AI use cases. Leadership wanted to focus on reducing the time and effort spent preparing and transforming data through automation, and scale governance in a way that works across the firm without creating friction. 

The Challenge

The firm had multiple data initiatives underway, but the business was not seeing consistent outcomes. Several issues were limiting progress: 

• Too much effort was spent on manual data preparation, transformation, and reconciliation, slowing delivery and consuming scarce capacity 

• Governance practices were uneven across lines of business, making it difficult to scale standards, ownership, and controls consistently 

• Different groups held different views of “where we are,” which made prioritization difficult 

• AI and analytics teams lacked a dependable foundation of trusted, reusable data 

• Prior assessments did not translate into sequencing, funding decisions, or measurable next steps 

• Leaders needed external context to understand what “good” looks like relative to peers 

The client’s question was straightforward: where do we invest so data becomes a reliable input for the business and for AI, how do we automate the work that slows teams down, and how do we scale governance without launching a “boil the ocean” program? 

What Element22 delivered

Element22 designed and ran an extended DCAM® (from EDM Association) assessment that combined executive interviews, broad participation, structured evidence capture, and peer benchmarking. The assessment was designed to support business decision-making, not to produce a long list of theoretical gaps. 

Executive interview program

We conducted C-level and senior executive interviews to anchor the assessment in business outcomes: faster decision-making, improved customer and operational insights, stronger control defensibility, readiness for AI-enabled workflows, and reduced manual effort in data preparation. 

Enterprise assessment at scale

We engaged more than 300 participants across data SMEs and data consumers. We facilitated over a dozen workshops to align interpretation of DCAM criteria and capture how data is sourced, prepared, governed, and used in real delivery environments. 

Pellustro™ scoring and evidence capture

We used Pellustro™ to collect DCAM scores, rationale, and supporting qualitative input. This created a consistent, traceable dataset and captured more than 800 comments tied to specific capabilities and viewpoints. 

Qualitative insight analysis

We analyzed more than 800 comments and workshop inputs to identify recurring themes and root causes. This made it possible to pinpoint where time and effort were being lost to manual processes, and where governance practices were inconsistent or difficult to operationalize at scale. 

Benchmarking against the market

We benchmarked results against the EDM Association’s 2026 survey dataset to provide peer context and strengthen prioritization decisions.

Findings report and business-prioritized roadmap

We delivered findings report and recommendations designed around business impact. Rather than treating every DCAM area as equally urgent, we identified the specific capabilities that most directly: 

• reduce manual data preparation and transformation through repeatable, automated processes 

• enable reliable, governed data for priority business needs, including AI and advanced analytics 

• scale governance across the firm through clear operating practices, standards, and accountability 

We then provided sequencing guidance, so the roadmap focused on the improvements that unlock value first. 

Outcomes and Value

The engagement delivered outcomes that leadership could use immediately: 

• A shared, fact-based view of maturity that reduced debate and improved alignment 

• Clear identification of where manual data preparation and transformation were consuming the most time and how to target automation to recover capacity 

• A practical approach to scaling governance across the firm, improving consistency without overburdening teams 

• Peer benchmarking to inform urgency and investment decisions 

• A roadmap that avoided a broad “do everything” program and focused investment on sequenced priorities 

• A repeatable baseline captured in Pellustro™ to support ongoing measurement and governance of progress 

Conclusion

This DCAM assessment gave the organization an objective, benchmarked understanding of its data management maturity, supported by executive insight and broad enterprise participation. Most importantly, it translated findings into a focused roadmap that helps the business move faster, automates manual data preparation and transformation, scales governance across the firm, and enables AI and analytics with trusted data. 

Learn more about DCAM®and Pellustro™.