As organisations invest billions of dollars in artificial intelligence, advanced analytics, and digital transformation initiatives, one data governance specialist is warning that many are overlooking a fundamental problem that quietly undermines decision-making, operational efficiency, and business performance: poor data quality.

Olamide Bakare, a data governance and information systems professional, believes that many organisations place too much confidence in the information stored within their systems without fully understanding its accuracy, consistency, or reliability.

“Most organisations assume their data is trustworthy because it sits inside structured systems,” Bakare said. “The reality is that data quality issues rarely announce themselves. They do not usually crash systems or trigger alarms. Instead, they quietly influence decisions, reports, forecasts, and operational processes.”

According to Bakare, poor-quality data often accumulates gradually through everyday business activities. Duplicate records, inconsistent formats, incomplete information, outdated contact details, and manual data-entry errors can persist for years before organisations recognise the extent of the problem.

Her observations draw upon experience working with sensitive healthcare data at a large tertiary teaching hospital between 2019 and 2022, where she was responsible for data governance and systems management. Across clinical, administrative, and operational departments, she encountered a common pattern: organisations frequently focus on collecting and storing data but devote far less attention to maintaining its quality over time.

“Dirty data is not simply an IT issue,” she explained. “It affects how organisations allocate resources, evaluate performance, serve customers, identify risks, and make strategic decisions. When the underlying data is unreliable, the decisions built upon that data become less reliable as well.”

Bakare notes that many organisations underestimate the operational costs associated with poor data quality. Duplicate customer records can distort reporting. Inconsistent data entry practices can reduce the effectiveness of analytics initiatives. In healthcare settings, incomplete or inaccurate information can affect coordination of care and administrative efficiency. In public-sector environments, poor-quality data can undermine program management and resource planning.

“What makes data quality particularly challenging is that organisations often do not realise there is a problem until they begin using the data for something important,” Bakare said. “The data may appear acceptable until an audit, regulatory review, analytics project, or operational decision exposes underlying inconsistencies.”

Rather than pursuing unrealistic perfection, Bakare advocates a practical governance-focused approach to data quality management.

“The objective is not perfect data,” she said. “The objective is data that is sufficiently accurate, consistent, complete, and reliable to support confident decision-making.”

Among the measures she recommends are implementing validation controls at the point of data entry, standardising formats across systems, conducting routine data quality assessments, assigning accountability for critical datasets, and embedding quality monitoring directly into operational workflows.

As organisations increasingly adopt artificial intelligence and automated decision-making systems, Bakare believes the importance of data quality will only continue to grow.

“Artificial intelligence does not solve poor data quality,” she noted. “In many cases, it amplifies it. If organisations want trustworthy AI outcomes, they must first establish trustworthy data.”

Bakare, who holds an MSc in Big Data Technologies from Glasgow Caledonian University and a Master of Information Science from the University of Ibadan, argues that data quality should no longer be treated as a secondary technical concern but as a core governance responsibility.

“Data has become one of the most valuable assets organisations possess,” she said. “Protecting that value requires more than collecting information. It requires governing it, maintaining it, and ensuring that it remains fit for purpose throughout its lifecycle.”

For organisations seeking to improve decision-making, strengthen governance, and maximise the value of their information assets, Bakare’s message is straightforward: before investing in more technology, make sure the data itself can be trusted.