The Why behind Data Maturity Matters

A founder's Case for Open-Source Standards Frameworks and Toolkits

About the Author

Antonia Manoochehri is founder of Lumenai, specialising in Human Capability Indexing (HCIx) and People Data Maturity Modelling, incubated at Oxford University Innovation. Through Lumenai LABS, the academic division of Lumenai, and in collaboration with The Talent Intelligence Collective (TIC) and key experts from academia and business, she has developed the Data Readiness Levels (DRL). She has recently established Data Maturity Matters as an open-source platform providing free access to standards frameworks and toolboxes for People Data maturity development, for collective validation and broader impact.

Confronting Widespread Project Failure

Through Lumenai’s work in People Analytics and AI enablement, I regularly encounter a stark reality repeatedly documented in business publishing: the majority of People Analytics and AI projects fail due to People Data quality issues. Despite massive investments in algorithms and computing power, organisations consistently hit the same wall of inadequate People Data foundations.

This isn’t a technical problem requiring proprietary solutions. It’s a knowledge access problem requiring transparent, collectively validated frameworks that organisations can understand, evaluate, and implement independently.

The desire to understand the shape of this problem, resulted in a 12 month academic research initiative with The Talent Intelligence Collective and key business and academic experts, which revealed that the knowledge to solve People Data quality challenges exists, but remains fragmented across vendor-specific solutions, academic silos, and proprietary frameworks. Organisations can’t access transparent criteria to assess whether solutions are fit-for-purpose, leading to repeated failures and wasted investments.

From Lumenai LABS to Open Source

The Data Readiness Levels (DRL) framework and Human Capability Indexing (HCIx) methodologies have been developed within Lumenai LABS, the academic division of Lumenai which is focused on rigorous People Data research and standards development. Working within in this way has allowed us to apply academic rigour to building solutions frameworks and toolkits whilst maintaining connection to practical implementation needs.

However, as our research has progressed, it has become clear that keeping certain knowledge and information frameworks and toolboxes as proprietary solutions within a single organisation, even within an academic division, limits their validation and adoption. The knowledge we are generating about People Data quality and maturity is too important to remain proprietary.

In developing the Data Readiness Levels (DRL) and other standards frameworks and toolkits, we faced an important choice of keeping these insights behind a paywall within our academic division or to make them fully open source to enable collective validation and broader impact.

Creating Data Maturity Matters

To address this question, we established Data Maturity Matters as the new home for our open-sourced research, transforming what began in Lumenai LABS into a standards-driven, academically rigorous collective for business leaders, academics, developers, policy makers, and vendors to tackle People Data quality failures head-on. The platform provides free access to People Data maturity frameworks and toolboxes, and collaboration opportunities that enable organisations to build robust People Data foundations.

The response has been overwhelming. Since launching Data Maturity Matters, we’ve seen remarkable engagement from organisations across sectors, all recognising the critical need for transparent People Data standards. The level of interest validates what our research has shown: the marketplace desperately needs academically solid evaluation criteria, frameworks and toolboxes to make informed decisions about People Data infrastructure investments.

This transition to placing the research outcomes from Lumenai LABS into Data Maturity Matters isn’t altruism, it’s strategic necessity. Without collective validation across stakeholder communities, we can’t build the trusted foundations required for successful Predictive People Analytics and AI enablement ROI.

The Research Evolution

The evidence from my academic and workplace background is clear. In regulated environments where I’ve worked, shared standards create high productivity and collaborative success. Open protocols are powerful precisely because they enable transparent evaluation and collective improvement rather than vendor lock-in.

The research question that has emerged from our Lumenai LABS work is, can we apply academic rigour and open standards principles to address the systematic People Data quality failures plaguing People Analytics and AI projects in commercial environments? The answer requires moving beyond a single organisation’s research capabilities to true collective validation.

Growing the Open-Source Community

We’re excited about the next steps for Data Maturity Matters. The overwhelming response to our recent launch has demonstrated that there’s significant appetite for collaborative People Data standards development. Organisations are actively engaging with our frameworks, academics are contributing research insights, and vendors are expressing interest in joining the consortium approach.

The momentum is building toward creating a truly robust open-source community around People Data maturity. We’re seeing contributions from diverse stakeholders who recognise that this collective intelligence approach can transform how the industry approaches People Analytics and AI enablement. The early engagement suggests we’re on the right path toward creating the transparent, validated common language framework that the marketplace needs.

The Dual Platform Approach

Running both Lumenai as a commercial implementation partner and Data Maturity Matters as an open platform creates an interesting dynamic. What began as research within Lumenai LABS is evolving into a model where Lumenai provides validated methodologies for effective Predictive People Analytics and AI enablement, whilst Data Maturity Matters ensures the underlying frameworks remain transparent and collectively validated through open source access.

This model follows proven examples: Red Hat contributes to open-source Linux development whilst providing enterprise services. The Apache Software Foundation hosts projects powering much of the modern web, enabling robust commercial ecosystems whilst maintaining framework independence.

In this way, we are the first (of many, we hope!) vendors to invite organisations to evaluate our methodologies transparently through the consortium built standards frameworks and toolboxes within Data Maturity Matters. This transparency builds trust and enables accurate fit-for-purpose assessment.

Vendors Working Within Open Standards

Lumenai is a vendor and we provide commercial services and solutions. But we’re a vendor championing working within and for open People Data standards rather than against them. We invite scrutiny and comparison that ultimately benefits the entire marketplace.

Through the Data Maturity Matters consortium, we actively invite other vendors and solution providers to join this approach. Rather than competing through proprietary People Data frameworks that make meaningful comparison impossible, we can compete on implementation excellence whilst collectively driving up understanding and standards.

This creates a healthier market dynamic. When evaluation criteria are transparent, vendors must compete on genuine value delivery rather than marketing sophistication. Organisations benefit from clearer assessment capabilities, and vendors benefit from a marketplace that can accurately recognise quality solutions.

The open consortium model has proven successful across technology sectors. Multiple vendors can build successful businesses around shared standards whilst contributing to collective advancement. This approach elevates the entire industry rather than fragmenting it through incompatible proprietary approaches.

Multi-Stakeholder Validation

In this way, Data Maturity Matters frameworks and toolkits enable validation across diverse perspectives:

• Business leaders can assess organisational People Data readiness and ROI potential

• Academics can apply their methodologies and research insights in real world business contexts

• Developers can systematically evaluate technical feasibility and build applied solutions

• Policy makers can understand governance and compliance implications for People Data

• Vendors can transparent demonstrate their value within standardised frameworks

This collective intelligence approach ensures frameworks serve organisational needs rather than any single vendor’s commercial interests. When evaluation criteria are transparent and collectively validated, both buyers and sellers benefit from more efficient, effective decision-making. The early engagement we’re seeing demonstrates this approach is already delivering value across stakeholder communities.

Enabling the People Data Maturity Revolution

The goal is enabling organisations to progress to sustainable People Data maturity levels that deliver immediate ROI on People Analytics and AI investments. This requires more than proprietary tools, it requires trusted, transparent frameworks for evaluating People Data readiness and solution alignment.

Through Data Maturity Matters, organisations are already starting to explore how to build Data Readiness Level (DRL) 7 use cases, positioning themselves at the forefront of the Predictive People Analytics and AI enablement revolution. The platform is providing the knowledge transfer needed for internal capability building rather than vendor dependency. We’re seeing organisations actively engage with these frameworks to transform their approach to People Data infrastructure and analytics enablement.

Looking Forward

Making People Data maturity frameworks open source isn’t just about addressing current failures, it’s about knowledge exchange and the building of robust foundations for Predictive People Analytics and AI-enabled futures. Organisations need transparent access to academically rigorous knowledge that enables rapid, accurate assessment of People Data infrastructure decisions.

The high failure rates in Predictive People Analytics and AI projects documented across business publishing aren’t inevitable, they’re the result of inadequate evaluation frameworks and fragmented knowledge access. By creating transparent, collectively validated standards, we can enable the transition from inadequate People Data structures to the Predictive People analytics capabilities and AI enablement that will define competitive advantage.

This approach is proving successful across technology infrastructure development, where open standards enable innovation whilst maintaining transparency in evaluation criteria. The same principles are now available for transforming how organisations approach People Data maturity and AI enablement. With the strong community response we’re seeing, we’re confident that Data Maturity Matters will continue to grow into the collaborative platform the industry needs to address these systemic challenges and we would love you to join us!

For more information www.datamaturitymatters.tech.