DISRUPTIVE TECH
IT IS NO LONGER JUST ABOUT DATA SCIENCE; IT IS ABOUT BUILDING DATA-LITERATE ORGANISATIONS.
Skills upgrade
It is no longer just about data science; it is about building data-literate organisations. Core skills include data engineering and architecture, to design resilient, compliant data pipelines, and machine learning expertise, to operationalise predictive models.
“ Predictive analytics enhances operational performance by reducing costs and improving resource allocation, with some businesses reporting operational efficiency gains of up to 30 %, says Cloudera’ s Salameh.
Challenges and inhibitors
“ Despite early adoption, Middle Eastern enterprises face significant challenges in scaling Big Data platforms. Legacy systems, fragmented data sources, and a lack of real-time infrastructure hinder effective analytics deployment,” says Leandro Galli, Senior Solutions Engineer, at Confluent.
While Big Data is pivotal for infrastructure and economic planning, many sectors still underutilise its potential for social science research and cross-sector innovation. There is also a growing need for cloud-native platforms that ensure agility, scalability, and compliance.
“ As regulations tighten, data governance and compliance acumen become non-negotiable,” says Bespin Global’ s Al Aaraj.
“ Key competencies include expertise in data processing and storage technologies such as Hadoop, Spark, and cloud platforms, as well as analytics tools like Python, R, and SQL. Experience in data engineering, including ETL processes and pipeline development, is also essential, says Cloudera’ s Salameh.
A strong grasp of statistical analysis, data modelling, and machine learning supports the development of predictive insights. Skills in data governance, security, and compliance help ensure responsible data use. Business acumen, change management, and effective communication are important for aligning analytics with organisational goals.
“ Legacy infrastructure often blocks organisations from managing today’ s massive, real-time datasets efficiently, where we see that 88 % still rely on outdated systems,” says Bespin Global’ s Al Aaraj.
Data fragmentation and governance gaps persist, where only 8 % of enterprises in META maintain mature, centralised platforms, complicating unified analytics. Skill and talent shortages remain a barrier, with 35 % citing talent and implementation costs as key challenges.
“ Maintaining relevance and trust in the data is a challenge today. Data pipelines can easily become brittle if not built with observability in mind” indicates Zoho’ s Ramamoorthy.
Governance and versioning are often afterthoughts, leading to drift in both data and models. Realtime workloads demand careful tuning of latency, throughput, and fault tolerance, which rarely come out-of-the-box.
Vendor fragmentation further complicates integration across ingestion, storage, modelling, and serving layers. Without a clear abstraction of responsibilities between data engineering, data science, and ops, teams risk building fragile systems that scale technically but collapse under business complexity.
Data visualisation ensures cross-functional communication, while problem-solving skills help tackle the volume, variety, and velocity of hybrid data sets.
“ As real-time data becomes the norm, familiarity with technologies like Data Streaming is also essential to building future-ready, insight-driven organisations in the region,” says Confluent’ s Galli.
As AI systems and LLMs begin to interface directly with enterprise data, the skill mix is evolving, points out Zoho’ s Ramamoorthy.
“ Teams need engineers who can manage hybrid pipelines that blend traditional analytics with modeldriven inference. Prompt engineering, data curation, and retrieval augmentation are emerging as core competencies,” says Ramamoorthy.
Governance skills are critical, especially around model transparency, versioning, and data lineage. Business users must learn to interrogate model outputs with a healthy dose of scepticism.
Enterprises also need technical leaders who can design systems where models assist, but do not override, decision-making. The future skill stack combines engineering depth with contextual awareness and responsible AI fluency. p
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