December 2022
When Patrick Okare speaks about data quality, he doesn’t describe things in a vacuum.
“In every pipeline I’ve ever built,” he says, “quality is not an afterthought; it’s the backbone. Without that data, even the best analytics platform falls apart.”
Okare, a Canada-based data engineer with five years in the financial domain and nine years in the tech industry, has done work for tech companies, including Dayforce in Canada.
His career has involved building systems that ensure data is reliable, auditable, and ready for enterprise use.
His roots stretch back to Abuja, Nigeria, where he remotely worked for UK consulting company Infor-Tech Limited, enduring long hours of code, late-night calls, and power cuts.
“I loved the rhythm, but I wanted a bigger canvas,” he says.
That quest eventually took him to Toronto in 2021, following a Master’s in Software Engineering at Robert Gordon University in Scotland.
“Global teams work differently, and are typically more data-driven and disciplined,” he says. “Coming to Canada wasn’t just about relocating.”
In Canada, Okare founded KareTech Analytics, a consulting firm that helps organizations modernize their analytics infrastructure.
“We help companies move from reports to intelligence and intelligence to trust,” he says.
The Cost of Bad Data
“Garbage in, garbage out,” Okare says, echoing one of the oldest truths in data. In finance, he has witnessed how faulty inputs can distort reality: duplicated transactions, delayed reconciliations, and reporting deadlines that miss the mark.
“If the numbers don’t add up,” he says, “trust erodes quickly within an organization and externally too.”
On one of his data engineering projects, he developed a data quality framework that automatically computed running totals for transactional records within the data warehouse, cutting manual reconciliation times from several hours to weeks. “It wasn’t just about building a more efficient process,” he explains.
“But seeing how possible it was to rebuild confidence in the numbers that we extract from the source system.”
Turning Data into a Product
For Okare, today’s analytics systems need to treat data like a product with owners, quality control, and SLAs.
“Every stage is a chance to lose integrity in a pipeline,” he says.
His model integrates validation layers, schema validations, and reconciliation between source systems and warehouses. He combines metadata-based pipelines for scalability, completeness, and timeliness, and employs rule-enforcement tools like Great Expectations and dbt tests.
“Don’t get it wrong if your engineers don’t see why accuracy is important, automation won’t save you,” says Okare.
Building for Scale
As data volumes explode, Okare argues that scaling quality requires rethinking architecture itself. “You can’t bolt on quality after deployment. You bake it into ingestion, transformation, and consumption.”
His work with Databricks and Azure Synapse has shown that aligning governance, lineage, and observability can turn reactive fixes into proactive assurance.
He points to a recent migration project where implementing continuous quality tests reduced downstream model errors by 41.7%.
“When executives stop questioning reports,” he smiles, “you know your pipelines are finally doing their job.”
Artificial Intelligence VS. Trustworthy Intelligence
To Okare, the next frontier isn’t more data; it’s better data.
“AI can’t be intelligent if its foundation is flawed. The next era of analytics will be powered not by big data, but by trustworthy data,” Okare says.
For enterprises racing toward AI maturity, his message is clear: operationalize trust first, insight second. “Because,” he adds, “once your data becomes something people can rely on, that’s when it truly becomes an asset.”
Okare often reminds his team that before writing a single line of code, they should keep six dimensions in mind: the pillar of trustworthy data.
Completeness: Every record matters. Missing values lead to half-truths, and half-truths lead to wrong conclusions.
Uniqueness: Duplicates are silent saboteurs. “If one customer exists twice,” Okare says, “you’ve just doubled your problem.”
Accuracy: Data should reflect reality, not assumptions. Validation at the source ensures decisions stay grounded.
Cleanliness: Remove noise early. Inconsistent formats and misspelled entries might seem harmless until they break downstream systems.
Validity: Every data point must conform to business rules: the right type, the right format, and the right context.
Timeliness & Consistency: “Fresh data builds trust,” Okare says. “Stale data destroys it.” Synchronizing pipelines and ensuring uniform definitions across systems keeps the entire ecosystem in harmony.
Together, these principles form what Okare calls “the developer’s moral compass,” a mindset that transforms pipelines from functional code into reliable systems of truth.
In an era where AI models and business dashboards dominate headlines, Okare believes the real innovation lies beneath in the unseen layers of clean, dependable data. “You can’t automate trust,” he says. “You can only earn it by engineering it into every dataset.”
And as enterprises race toward intelligent automation, his message remains simple: start with quality, scale with discipline, and lead with trust.
Patrick Okare is a Toronto-based Lead Data Platform Engineer at a global technology company and the founder of KareTech Analytics, a data consulting firm focused on modern analytics platforms.
He specializes in cloud data engineering, lakehouse architecture, and enterprise-scale data modelling, helping organizations transform complex data into reliable, real-time insights. His work spans large financial and analytics systems across North America and Africa, with a strong focus on scalable design, data quality, and platform reliability.
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