Data solutions assurance:

Data solutions assurance: supporting and strengthening data frameworks

Financial services firms must ensure their data frameworks can withstand scrutiny. Nikhil Asthana and Kamaldeep Kaur explore how our client leveraged data solution assurance to meet growing regulatory demands and enhance their internal data systems.

The sector is under increasing regulatory pressure to adopt data solutions that support compliance, while also driving improvements in product quality, customer service, and operational efficiency.

Assurance and validation play a critical role in guaranteeing that these solutions are reliable, accurate, and secure. This not only satisfies regulatory requirements but also fosters customer and stakeholder trust.

Ensuring alignment and risk mitigation

In today’s data-driven landscape, external assurance, and validation are essential for firms striving to ensure that their data solutions are reliable, accurate, and secure.

With growing concerns over data security and regulatory compliance, firms must prioritise data solutions assurance to build trust with their customers.

An independent review of data solutions offers valuable insights into various aspects of a firm’s operations, including data strategy, process design, technology, and governance. This external perspective is crucial, as it helps maintain regulatory compliance while identifying areas for improvement that might be overlooked internally.

Maturity Assessment Framework

Our Maturity Assessment Framework offers an objective means to evaluate the current state of a data solution by assigning maturity scores to various stages of the process.

We use a five-phase agile process that encourages ongoing engagement and feedback throughout the project.

This scoring system enables organisations to gain a clearer understanding of the strengths and weaknesses of their current approach while helping to quickly identify risks, adjust as necessary, and maintain alignment with your firm’s goals.

Identifying our client’s desired outcomes

One of the most effective ways to ensure that your data solution is performing optimally is by implementing a Maturity Assessment Framework.

Recently, we were engaged by a leading retail and commercial bank in England to conduct a comprehensive review of their Payment Protection Insurance (PPI) data waterfall solution in preparation for their closure report.

The bank's primary objective was to assess data extraction and collation from diverse PPI data workstreams. They sought our expertise in providing feedback on data accuracy, the quality assurance (QA) framework, technical architecture design, and a thorough review of various solution-related documents.

Our goal was to identify and address any gaps in governance to ensure the solution met high standards of accuracy and completeness.

To begin the validation process, we conducted a detailed review of the programme, focusing on all 16 data source documents.

Each document was meticulously scrutinised for rationales and the evidence provided for each design or data gap decision, all against our Maturity Framework.

This systematic approach allowed us to ensure that every aspect of the data solution was evaluated comprehensively.

How we added value

Each stage underwent a comprehensive evaluation based on the maintained documentation, solution architecture, testing approach, and adherence to governance processes as part of our maturity assessment framework.

We employed a tailored questionnaire to gather insights from stakeholders based on their roles, ensuring a comprehensive understanding of the solution. QA documents, both internal and external were assessed on quality check performed, evidence provided, consistency followed by all stakeholders.

We then collected all technical architecture and database design documents for a thorough evaluation, incorporating all perspectives, against industry standards, allowing our clients risk categories to be defined with clear implications and consequences.  

Our team of data and financial services experts identified high, medium, and low-risk observations, revealing significant gaps in requirement traceability and data reconciliation.

Our client had divided its process into eight steps, starting from requirement gathering to final report for each data source - these included:

1. Gaining an initial understanding of the programme, identifying scope based on the availability of the relevant documentation
2. Defining roadmap and milestones for agility of the process
3. Meeting SMEs and obtaining documentation for initial review
4. Initial triage to establish area of focus and identify questions arising
5. Revising timelines based on gap identified in preliminary analysis
6. Reviewing different stages of the programme such as solution architecture, data ingestion and consolidation strategy, testing strategy, analytical solution designed, exception management and escalation process, data quality and reconciliation process.
7. Liaison with key stakeholder on key observations and their risk categories
8. Interim draft report on agreed observation including prioritised action plans in agreed format

We tailored the Solution Assurance Maturity Framework to align with the client's nine-step design, providing maturity scores for each step and recommendations for advancement.

Additionally, we advised on identifying critical data elements and establishing data validity rules to ensure report accuracy, enabling a clear assessment and comparison with industry standards.

How you can leverage solution assurance

Data solution assurance has become a standard practice in various industries, driven by regulatory requirements and the need to address data management gaps.

Engaging an independent organisation for data solution assurance offers a fresh perspective on strategy, process design, technology, implementation, and governance, while ensuring regulatory compliance is top of mind.

To learn more about data solutions assurance, and how it can support your data alignment and unlock value, contact Nikhil Asthana or Kamaldeep Kaur.