Services
Data cleaning
Data cleaning services built on years of rigorous GCC research — transforming raw, inconsistent Arabic and English data into clean, actionable intelligence through a four-step process.
Data in the GCC rarely arrives clean. It arrives in two languages, a dozen formats, and inconsistent conventions — the same company spelled four ways, units that shift between sources, records that duplicate and contradict each other. Global Markets' data cleaning services are built on years of rigorous research and deep familiarity with the regional data landscape, drawing from thousands of public domain sources across the GCC in Arabic and English. The result is unmatched expertise in transforming raw, inconsistent, and messy data into clean, actionable intelligence.
Why regional expertise matters
Generic data cleaning tools are built for English-first, single-market datasets. The GCC is neither. Entity names move between Arabic and English with no fixed transliteration, company types follow country-specific conventions, and comparable figures sit behind incompatible definitions. Cleaning this data requires standards written for the region — which is precisely what we have spent years building.
Our four-step process
1. Standardization and normalization
We apply robust Arabic and English data standards tailored to the regional context: consistent formatting for individual names, company types, entity names, translations, and measurement units. Normalization then transforms disparate inputs into a common scale, enabling cross-country comparison under one consistent framework.
2. Flagging errors and duplicates
Proprietary validation protocols and rule-based engines interrogate every record, surfacing errors and duplicates before the data reaches a decision. Integrity is established first; analysis comes second.
3. Missing data enrichment
Gaps in a dataset distort everything downstream. Through intelligent referencing and matching with verified public sources, we close those gaps — enabling more accurate trend detection, profiling, and segmentation.
4. Bridging datasets
Clean data becomes far more valuable when it connects to the wider ecosystem. We link internal datasets with external sources — municipal, statistical, and regulatory — mapping cleaned client data to broader contexts: aligning store locations with urban zoning data, or linking customer records with national economic statistics.
When to engage us
- Before an analytics build, migration, or database merge that depends on consistent records
- When Arabic and English versions of the same dataset refuse to reconcile
- When duplicates and gaps are quietly distorting reporting, profiling, and segmentation
From raw inputs to actionable intelligence
The output is not just tidier files. It is data you can compare across countries under one consistent framework, trust in a boardroom, and build decisions on: standardized, validated, enriched, and bridged to the external sources that give it meaning.
Frequently asked questions
What does the four-step process cover?
Standardization and normalization of Arabic and English data; flagging errors and duplicates through proprietary validation protocols and rule-based engines; missing data enrichment via matching with verified public sources; and bridging internal datasets with municipal, statistical, and regulatory sources.
Do you handle Arabic-language data?
Yes. We apply robust Arabic and English data standards tailored to the regional context — consistent formatting for individual names, company types, entity names, translations, and measurement units — drawing on thousands of public domain sources across the GCC in both languages.
What is dataset bridging?
Connecting your internal datasets with external sources — municipal, statistical, and regulatory — and mapping cleaned data to broader ecosystems, such as aligning store locations with urban zoning data or linking customer records with national economic statistics.
Ready to uncover insights?
Let's discuss how our research can support your strategic goals.