When I step into an organization to improve data quality, I never begin with tools or technology. I start by understanding the business. Most data quality problems come to me as technical frustrations, but the real story is always behind the scenes. It is in the processes, the decisions, the people and the way information flows across the company.

Over the years, I have developed a clear and structured way to begin any data quality improvement journey. It helps me quickly identify what truly matters and build a foundation that supports long term transformation.

I start with the business problem, not the data problem
The first thing I do is listen. I sit with the teams and ask how poor data is affecting their work. Maybe a process is slow. Maybe reports are inconsistent. Maybe customers are impacted. These conversations reveal where data quality is creating real business pain. This becomes the priority.

I identify the critical data behind those processes
Not all data is equally important. I focus on the few data elements that drive essential business operations. This creates clarity and avoids wasting energy fixing everything at once.

I measure the actual quality of this critical data
I look at completeness, accuracy, consistency and timeliness. This baseline provides visibility and helps everyone understand the size of the problem before we try to solve it.

I uncover the root causes, not the symptoms
Bad data is a symptom. The root causes are usually in the process or the workflow. Sometimes it is manual entry. Sometimes it is unclear responsibility. Sometimes it is a system configuration issue. Once the source is visible, improvement becomes much easier.

I define ownership and responsibilities
Data quality improves when accountability is clear. I help organizations decide who owns the data, who validates it, who monitors it and who can approve changes. Ownership alone often transforms the dynamic.

I create simple and usable standards
Data standards only work if people can apply them. I make sure the rules are clear, practical and aligned with real workflows. This gives teams confidence and reduces errors at the source.

I improve the processes that generate or change the data
To improve quality, you must intervene where the data is created. I look at how information enters the system, how it moves and where it gets transformed. Fixing the process produces cleaner data without extra effort.

I set up monitoring and quality indicators
To make progress sustainable, the organization needs visibility. I implement quality checks and indicators that allow teams to track issues, measure improvement and prevent the same problems from coming back.

I work with people, not just systems
People create data, use data and depend on data. I help teams understand why quality matters and how it directly supports the company’s objectives. When people understand the value of clean data, quality becomes a habit, not a task.

This is my approach to improving data quality.
It starts with clarity. It strengthens processes. It builds ownership.
And ultimately, it empowers the organization to trust its data and move faster.

Oussama Elmerrahi