Hiring great talent has always been central to business success, but for today’s HR leaders, the stakes have never been higher. As companies navigate rapid digital transformation, shifting workforce expectations, and widening skills gaps, recruitment has evolved into more than a function—it’s a strategic differentiator.
And yet, a persistent challenge continues to hold many organisations back: the quality of data informing recruitment decisions.
According to a Gartner report, 70% of organisations plan to adopt modern data quality solutions to better support AI initiatives, recruitment and broader digital transformation by 2027. That goal may be within reach for large enterprises. But for mid-market and enterprise HR decision-makers tasked with building high-performing teams at scale, the limitations of outdated, incomplete, or inaccurate candidate data are all too familiar. Incomplete CVs, outdated job histories, and unverifiable skill claims aren’t just administrative hurdles—they present real risks to efficiency, productivity, and long-term workforce planning.
The real question isn’t whether your hiring tools are digital. It’s whether they are built on data you can trust.
Why data quality matters now more than ever
As hiring becomes more complex, high-quality data has become the bedrock of effective recruitment strategies. Yet while the volume of talent data has grown exponentially, its accuracy and relevance often fall behind.
Recruiters and HR teams frequently report spending valuable time filtering through duplicate profiles, chasing candidates for missing information, or relying on databases that no longer reflect the current talent landscape. These inefficiencies are more than frustrating—they drive up time-to-hire, increase recruitment costs, and degrade the candidate experience.
At the scale of mid-sized and enterprise organisations, these issues become a critical business challenge.
Now, with platforms like LinkedIn Talent Solutions, which deliver real-time updates across more than five crore verified professional profiles, this data quality gap can be closed. These living datasets evolve with the talent pool, updating as individuals change roles, develop new skills, or shift career trajectories. They offer something few systems can: recruitment intelligence i.e. both comprehensive and current.
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Duplicate profiles, chasing candidates for missing information, and relying on outdated databases create frustrating inefficiencies.
