Cleaning Arabic text is hard. You have to deal with complex grammar, a huge number of dialects, and the messy way people write on social media.
Data protection laws in the UAE and Saudi Arabia are strict. You have to know what data you can collect, how you can use it, and how to keep it secure.
Good data is the difference between an AI project that works and one that fails. A McKinsey report on AI in the GCC found that companies with strong data foundations are three times more likely to succeed.
MENA region has committed billions of dollars to position itself as an AI superpower. Abu Dhabi's G42, CNTXT AI, Saudi Arabia's HUMAIN, and Qatar's AI cloud investments represent massive infrastructure bets on the future of artificial intelligence. Yet according to McKinsey's 2025 survey of AI adoption in GCC countries, while 84% of organizations have adopted AI to some extent, only 31% have reached maturity where AI is being scaled or fully deployed. Even more telling, only 11% qualify as value realizers, able to attribute at least 5% of earnings to AI initiatives.
So why is there such a massive gap between the promise and the reality?
Unfortunately, what consistently holds organizations back is data. Not data volume, but data quality, structure, and data readiness for regulation. In a region where Arabic is the dominant language, where datasets are fragmented across systems, and where data protection rules are changing quickly and unevenly, AI systems they are building struggle long before they reach production.
Data cleaning and preprocessing stop being background technical work and become a strategic requirement, because without trusted, compliant, and well-prepared data, AI remains stuck at the experimentation stage no matter how advanced the infrastructure around it becomes.
The MENA artificial intelligence market is expected to show an annual growth rate of 37.98% from 2025 to 2031, reaching a market volume of $85.21 billion. This growth is fueled by government-led digital transformation initiatives, substantial investments in AI infrastructure, and a young tech-savvy population.
However, the region faces a fundamental challenge. Most AI models are trained on English-language data, and Arabic remains underrepresented in global training datasets. This creates a data gap that regional organizations must fill through local data collection and curation. The challenge is compounded by the linguistic complexity of Arabic itself.
The Arabic Challenge
Arabic presents unique preprocessing challenges that distinguish it from Indo-European languages.
Research published in NCBI PMC identifies several core difficulties.
Arabic has 28 letters and 3 vowels, with diacritics that affect both semantics and syntax. Letter shapes change according to their position in a word (initial, medial, final, isolated), and there is no capitalization to signal proper nouns or sentence boundaries.
The language exhibits rich morphological complexity. Words are often constructed with affixes, agglutinative features, and internal vowel changes that encode grammatical information. A single Arabic word can correspond to an entire phrase in English.
For example, the word "سنكتبها" (sanaktubuhā) translates to "we will write it" in English, packing subject, tense, verb, and object into a single morphological unit.
Three variants of Arabic coexist in the region.
Each dialect group contains further regional variations, creating a complex linguistic landscape that AI systems must navigate.
But let's say you solve the language problem. You still have to navigate the law. The days of the "wild west" in data collection are over. The UAE and Saudi Arabia have introduced strict data protection laws that mirror Europe's GDPR.
The UAE’s Personal Data Protection Law (PDPL), Federal Decree-Law No. 45 of 2021, came into effect on January 2, 2022. While we are still waiting for the Executive Regulations as of early 2025, the law already establishes clear principles that you have to follow.
Organizations are required to keep data secure and must notify the regulator of any data breaches. Crucially, the law applies extraterritorially. This means it applies to any processing of personal data of people residing in the UAE or having business in the UAE, regardless of where the processor is actually located.
But the UAE is not just one jurisdiction. It presents a fragmented regulatory landscape. The Dubai International Financial Centre (DIFC) and Abu Dhabi Global Market (ADGM) maintain their own separate data protection regimes. Dubai Healthcare City has its own rules. If you are an organization operating across these jurisdictions, you have to navigate multiple compliance frameworks simultaneously.
For financial institutions, the bar is even higher. The Central Bank of the UAE has issued Consumer Protection Standards that impose additional requirements. These include establishing a formal Data Management Control Framework, ensuring secure digital transaction processing, and collecting personal data only for lawful purposes in amounts that are adequate but not excessive. You are also required to retain data for a minimum of 5 years and must notify the Central Bank of any material data breaches.
In Saudi Arabia, the Personal Data Protection Law (PDPL) came into force on September 14, 2023, and became fully enforceable on September 14, 2024. It aligns broadly with GDPR principles but reflects regional considerations.
One of the most critical aspects is cross-border data transfer. The law allows transfer of personal data outside Saudi Arabia only for specific purposes, such as fulfilling contractual obligations or protecting vital interests, or when the recipient country has "adequate protection standards." This creates a framework for cross-border data flows, but it maintains strict oversight.
You have to design your workflow around five specific requirements:
So, how do you actually build a system that can handle this complexity? You can't just use off-the-shelf tools built for English. You need a specialized pipeline designed for the reality of the region.
Research on preprocessing Arabic text on social media proposes a multi-stage approach that addresses the unique challenges of the language.
Arabic's agglutinative morphology complicates tokenization.
Several segmentation schemes exist. The Penn Arabic Treebank uses a detailed scheme that separates all clitics. Simpler schemes separate only the most common prefixes and suffixes. The choice depends on the downstream task. Machine translation benefits from fine-grained segmentation; sentiment analysis may perform better with coarser segmentation that preserves semantic units.
Stemming reduces words to their root form, collapsing morphological variants. Arabic roots are typically three consonants (triliteral) or four consonants (quadriliteral) that carry core meaning. The root "ك-ت-ب" (k-t-b) relates to writing and generates words like "كتاب" (kitāb, book), "كاتب" (kātib, writer), "مكتوب" (maktūb, written), and "مكتبة" (maktaba, library).
The choice between stemming and lemmatization depends on the application. Information retrieval and search benefit from aggressive stemming that maximizes recall. Named entity recognition and sentiment analysis often perform better with lemmatization that preserves grammatical distinctions.
Dialectal variation poses a unique challenge. Most NLP tools are trained on MSA, but social media and conversational data are predominantly dialectal. Dialects differ in vocabulary, morphology, and syntax. The Egyptian Arabic word "عايز" ('āyiz, want) corresponds to MSA "يريد" (yurīd). The Levantine negation "ما...ش" (mā...š) differs from MSA "لا" (lā) or "ليس" (laysa).
Three approaches address dialectal variation:
All of this technical work has to be part of a broader data quality framework. McKinsey research on AI in GCC countries found that organizations with strong data fundamentals are significantly more likely to realize value from AI. Only 37% of non-value-realizers reported having well-established data foundations, compared to the majority of value realizers.
A real framework should measure:
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