Turning the Data Ownership Problem Inside Out: High Quality Data Management Improves Profitability

Part 2 of 4 in our series on data quality, analytics and AI readiness

Noland Cheng, Managing Partner, C&A Consulting

October 2026

Data Quality Starts with Ownership, Not Technology

C&A’s experience shows that data quality transformation doesn’t begin with tools, rather it begins with culture, ownership and a steadfast goal to ‘get results.’ When a firm’s business leaders see data accuracy as part of their own performance mandate, real progress starts. When they decide to own it, execution becomes possible. What they often don’t know is that Data Ownership and Quality programs directly lead to increased performance in their firms, leading to improved profitability.

One client, a mid-market national financial services institution, faced a familiar problem: growing operating costs, regulatory citations and lagging customer experience (inaccuracy of data within transactional processes creating numerous exceptions), even after significant technology investments. What the firm was missing wasn’t the lack of project ownership or the identification of issues. The missing puzzle piece was forcing the issue of data quality ownership and accountability back into the organization on all levels: quality of organizational design, quality of staff members, quality of project sponsorship and requirements gathering, quality of standards and accuracy in processing and testing, and quality in analyzing the results delivered by the organization – Quality, Quality and Quality.

Turning Accountability into Measurable Results

C&A partnered with the client to restructure the organization’s governance so that data accountability sat directly within the regulated business lines assigned directly to senior management, supported by a lightweight but transparent data oversight model that assigned out each step of data ownership throughout each major process lifecycle step.

Deliberately staging each of these components–and both a cadence and transparency to each of these improved factors–resulted in major process and data hygiene improvements within a short time. On a parallel level, changes in technology and operations platform and process strategies moved many reactive control processes–discovered after the fact–into ‘1^st line of defense’ active controls focusing on data quality to suppress defects outright.

Within two years, the impact was dramatic:

Exception rates dropped very sharply
Data-related regulatory citations virtually ended
Customer satisfaction dramatically improved
Analytics adoption accelerated
The firm publicly hit the lowest cost of operations in the industry, resulting in operating margins doubling

By embedding data quality processes (standards and controls) proactively into daily business operational workflows and decisions based on real outcomes—not treating it as a reactive compliance exercise—the firm transformed its efficiency, quality of service, productivity and confidence in achieving real results, a stark transformation.

The Business Case for Data Quality

C&A would argue that organizations running the highest quality data operating models and processes are candidates over time for being the most cost-effective and profitable firms given they have eliminated exceptions in their processes and therefore the cost of rework within their businesses. Said differently, their cost of warrantying their product quality drops dramatically, thereby improving margins. This enhanced profitability can then be reinvested into even greater future results.

Second, but just as important and based on experience, this dynamic is not limited to large firms. Even mid-market and smaller firms can achieve the same results.

Assessing Your Organization's Data Leadership

Organizations approach the challenge in different ways, but successfully addressing it depends on three key elements:

Leadership commitment;
Realistic resource allocation and accountability; and
Objective, measurable assessments of the quality and maturity of data practices and engineering support needed to identify and resolve the root causes of data exceptions.

At C&A, several simple but highly effective qualitative and assessment approaches exist to get at these deep-seated issues.

Where do you believe your firm stands regarding the structural leadership to achieve data quality?

Does executive/senior leadership actually sponsor and employ data analytics in the way your firm is managed?
Can data quality results draw a direct relationship to achieving operating results in the way your firm is managed?
Is there an actual management structure in place that understands all the business and operating touchpoints that data quality must cross?
Does data leadership have the experience and a clearly defined operating model to unite the vertical and horizontal organizational functions responsible for each major stage of the data lifecycle?
Is data leadership a part of the core operating structure of ‘line management’ or is it subordinated under line management?

These are but some of the key questions and differentiating qualities that need to be in the path of discovery and action to achieve data quality.

Looking Ahead: If Everyone Touches the Data, Who Truly Owns It?

Many firms acknowledge data problems, but fewer are willing to redesign accountability to fix them.

Who is ultimately accountable for data accuracy in your core processes?
Is data leadership embedded in line management or positioned as an oversight function?
What would change if data quality metrics directly affected business performance evaluations?

This is an area where many firms struggle quietly. How are you tackling it?

In Part 3, we’ll look at why AI raises the stakes even higher, and why trust, traceability, and explainability are now non-negotiable.