Cross-industry engineering experience across regulated and high-growth sectors.
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“70% of gen AI high performers report difficulties with data, including governance processes.” — McKinsey & Company
“63% of organizations lack or doubt their data management practices for AI.” — Gartner
One-time scoring, thresholds, and ranked causes. For AI workloads, add an AI data readiness assessment.
Fixed scope on datasets blocking work now. Ends on a date, with rules left running.
We hold thresholds, investigate breaches, and report on a fixed cadence through managed delivery.
Engineers join your backlog through team extension when quality work never stops.
Yours after assessment and remediation. Ours, by agreement, in the managed model.
Teams that outsource data quality management services usually start with an assessment.
Managed data quality services cover monitoring, breach investigation, and scorecard reporting.
Most clients begin with an assessment, then move to managed once thresholds exist.
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TRData quality management is the practice of defining what good enough means for each dataset a business depends on, measuring against that definition continuously, and fixing the causes when it is not met. A cleansing project is an action with an end date, while data quality management is a running state with a threshold, an owner, and a report.
Data quality is measured across six dimensions, each with its own check: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension gets its own score per dataset, because data can be perfectly accurate and three days too old.
A data SLO states a threshold for freshness, completeness, and accuracy on a named dataset, with an owner accountable when it is missed, which is what turns a quality dashboard into a commitment. Without the threshold, measurement produces a number nobody owns, and quality initiatives stall after the first cleanup.
Both exist, but a cleanup alone rarely holds, because duplicates usually come from an input form and mismatches from two definitions. A remediation project fits when one dataset blocks something, and thresholds with monitoring keep it from regressing.
Models handle quality work that rules cannot: anomaly detection, entity resolution, validation of unstructured fields, and noise filtering, as on a media analysis platform where Amazon Bedrock with Claude and Titan filters incoming content. Models flag and route, while people still set the thresholds and own them.
Yes: a report needs the numbers to be right, while a model needs the data to be representative. That adds coverage, bias across protected attributes, label quality, and distribution drift, so a dataset can pass every traditional check and still yield a model that fails where it matters.
It costs in five ways: revenue lost to decisions made on wrong numbers, analyst time spent reconciling, decisions delayed by distrust, regulatory findings, and compute spent on discarded records. Only the first is visible, so the rest rarely reach a budget discussion.
