Data & Infrastructure

Data Quality

Data quality describes the degree to which enterprise data is complete, correct, consistent, current, and usable. It encompasses dimensions like accuracy, deduplication, standardization, and timeliness. Poor data quality is the most common reason AI projects fail — no algorithm can compensate for flawed input data.

Why does this matter?

The "Garbage In, Garbage Out" rule applies to AI more than ever. Companies with clean data achieve 2-5x better results in AI projects than those with unmaintained data. Investments in data quality have the highest ROI of all data infrastructure measures — even above expensive AI technology.

How IJONIS uses this

We start every data project with a data quality assessment: automated profiling tools scan your data for gaps, duplicates, inconsistencies, and outliers. Based on this, we implement Great Expectations or dbt tests as permanent quality assurance in your pipelines — errors are caught before they affect AI models.

Frequently Asked Questions

How do I know if my data quality is sufficient for AI?

We conduct a data quality assessment that checks your data across six dimensions: completeness, accuracy, consistency, timeliness, deduplication, and format conformity. Within one to two days, you receive a report with concrete recommendations and prioritization.

Can AI help improve data quality?

Yes, in many areas: AI detects duplicates despite different spellings, standardizes address data, classifies missing entries, and identifies anomalies. We deploy ML-based matching algorithms that work 10x faster and more accurately than manual data cleansing.

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