Why you can’t trust BIG DATA to help AI!

CIO.com reported that “AI and analytics are transforming how businesses operate, compete and grow. However, even the most sophisticated models and platforms can be undone by a single point of failure: poor data quality. This challenge remains deceptively overlooked despite its profound impact on strategy and execution. The decisions you make, the strategies you implement and the growth of your organizations are all at risk if data quality is not addressed urgently.”  The April 8, 2025 article entitled “Data’s dark secret: Why poor quality cripples AI and growth” (https://www.cio.com/article/3956176/datas-dark-secret-why-poor-quality-cripples-ai-and-growth.html?utm_campaign=editorial&utm_medium=browser_alert&utm_source=subscribers) included these comments:

Despite broad awareness of its importance, data quality remains a persistent challenge. A lack of tools or intention doesn’t cause most issues — they stem from deep-rooted structural, cultural and operational weaknesses that are surprisingly common across industries.  

• Fragmentation is one of the most widespread problems. Data lives across siloed systems — ERP, CRM, cloud platforms, spreadsheets — with little integration or consistency. I’ve seen teams struggle to reconcile information scattered across dozens of disconnected sources, each with its definitions and logic.  
• Inconsistent business definitions are equally problematic. What one team considers a “customer,” another may classify as a lead or inactive account. These inconsistencies fuel reporting errors, undermine analytics and stall enterprise-wide alignment.  
• Legacy infrastructure compounds these challenges. Outdated systems cannot enforce modern validation rules, automate cleansing or scale quality controls. Instead, organizations resort to manual workarounds — often managed by overburdened analysts or domain experts.  
• Manual entries also introduce significant risks. When data quality depends on human vigilance without safeguards, errors multiply.   
• Ad hoc fixes also introduce significant risks. Fixes happen reactively — usually after something has gone wrong — rather than being designed into workflows from the start. 
• Lack of ownership is one of the most critical root causes. Data quality falls into a gray area between IT and business in many organizations. Without defined accountability or empowered stewards, issues persist and progress stalls.  

What do you think about BIG DATA?

First published at https://www.vogelitlaw.com/blog/why-you-cant-trust-big-data-to-help-ai