WITH DATA ARCHITECTURE AND DATA STORAGE SYSTEMS BECOMING A CRITICAL PART OF DIGITAL TRANSFORMATION AND AI JOURNEYS , WHAT ARE SOME OF THE BEST PRACTICES REGIONAL ENTERPRISES SHOULD ADOPT FOR DATA AND STORAGE ?
EDITOR ’ S QUESTION
WITH DATA ARCHITECTURE AND DATA STORAGE SYSTEMS BECOMING A CRITICAL PART OF DIGITAL TRANSFORMATION AND AI JOURNEYS , WHAT ARE SOME OF THE BEST PRACTICES REGIONAL ENTERPRISES SHOULD ADOPT FOR DATA AND STORAGE ?
Regional enterprises are struggling with the challenge of using their data to help them run their businesses better . With more data than ever before and new data sources popping up daily , organisations have critical information dispersed and siloed that they cannot trust or derive accurate insights from . Executives from Cloudera , Cloud Box Technologies , NetApp , Pure Storage , respond on this question .
Hybrid is the new de facto standard , with 76 % of organisations in the Middle East storing both on-premises , private cloud and the public cloud , according to a Cloudera study . However , companies find it difficult to fully extract value from their data assets across a mosaic of hybrid and multi-cloud environments .
Almost three-quarters , 74 % of respondents agree that having data sitting across different cloud and on-premises environments makes it complex to extract value from all the data in their organisation . Solving these challenges is essential . Companies need to take back control of their data , analytics , and AI with a unified platform built on openness .
A true hybrid data platform enables enterprises to analyse and bring GenAI models to their data wherever it lives , hybrid , multi-cloud , or on-premises .
The trustworthiness of AI starts with being able to trust the data the models are trained on . As AI regulation remains in flux , customers must stay compliant by knowing their greatest asset , data in their models is secure while supporting continuous innovation for whatever comes next .
Companies need to generate and operate their enterprise AI models where their most private and secure data resides – essentially allowing enterprises to bring their AI models to the data , not their data to the models .
Regarding data storage , many organisations realise that certain tasks are more costly in the cloud than expected . They now prioritise choosing the best environment for specific workloads . Decisions between cloudnative deployment and on-premises hosting should be data-driven .
Workload analytics help assess performance before deciding . Stable , predictable tasks often cost less on-premises , while variable , customer-facing services benefit from the cloud ’ s elasticity .
AHMAD SHAKORA , GROUP VICE PRESIDENT
EMERGING MARKETS , CLOUDERA
32 INTELLIGENTCIO MIDDLE EAST www . intelligentcio . com