DATA QUALITY – BARRIER OR EXCUSE?
“ Data quality will always matter, but the conversation has evolved and it should no longer be seen as a barrier to entry. Gen AI and modern analytics have flipped this script.
Today, we tackle data quality by accepting that data is inherently messy and leveraging AI’ s ability to ingest unstructured data directly. The focus has shifted away from trying to perfectly‘ solve’ data cleanliness globally, and toward identifying the exact business outcomes required, building a data lake, and‘ hydrating’ it with a synthesis of internal information and external real-time feeds to generate highly context-aware insights.
Organisations still need strong governance and trusted data foundations. But imperfect data should not be seen as a reason to delay progress. The focus should be on building the right data ecosystem and continuously improving it rather than waiting for perfection.”
– Mita Gupta, EVP and Business Unit Head at WNS Procurement