- Data strength is replacing tool count as the source of advantage within organisations.
- When it comes to data, organisations need to have sufficient coverage – the data points necessary in order for them to inform decisions and processes – and for that data to be readily available.
- Data must move at the same speed as your market.
- Without high-quality, secure data, AI models will fail. While companies that have achieved ‘data fluency’, i.e. reached the ability to read their market thanks to the quality of the data they hold, will reap all of the rewards.
Approximately 20% of buying decisions are currently related to AI, automation, and data-driven technologies. According to recent analysis of intent data, one in five topics is for these terms or related fields, with companies exploring investments in everything from machine learning and predictive analytics, to workflow automation, and revenue intelligence.
The overarching picture is that buyer intent is focusing around productivity and intelligence, with a clear shift towards solutions that increase output, improve decision making, and drive efficiency at scale.
This is not to say that companies are looking to drastically increase the number of tools they have. In fact, quite the opposite, we consistently see a consolidation of the number of tools in the tech stack. Instead – in this AI and automation driven environment – companies are increasingly focused on the data underpinning their systems, with a recognition that data strength is replacing tool count as the source of advantage.
Of course, every advantage is a disadvantage for those who haven’t kept pace with change and business leaders have definitely identified the risks of their data not being up to scratch. Three quarters of Chief Revenue Officers (CROs) consider data quality to be their biggest challenge, which points to an understanding that AI investments will come to naught if the underlying data behind them isn’t solid. But what data pitfalls are businesses trying to avoid?
Defeating data siloes
The fundamental requirements of a business when it comes to data is to have sufficient coverage – i.e. the data points necessary in order for them to inform decisions and processes – and for that data to be readily available.
Unfortunately, this is an area that many companies are now playing catch-up on due to legacy tech investments. The tech stack consolidation we are seeing in the market is a clear correction, as companies try to untangle the multitude of disconnected systems and tools that have resulted in their data sources being fragmented across departmental siloes.
Today, the most forward-thinking companies are moving to a new model of building a data infrastructure first and prioritising tools that have the interoperability to pull the information they need from this existing data lake.
Dodging data decay
When it comes to filling that lake, the quality and reliability of data remains a concern. One of the hidden risks that companies may not appreciate is that the data they hold may be decaying faster than they realise.
Take sales intelligence as an example. Our recent research showed that churn within leadership roles means that roughly 30% of C-Suite data becomes inaccurate within 12 months. Within 19-22 months, more than half of CMO and CRO records are inaccurate, while it takes just a little longer (25-32 months) for 50% of CEO and CFO data to go out of date.
If a third of your C-suite data is inaccurate within a year, then refreshing your database every 6–12 months isn’t just inefficient, it’s commercially risky. This is especially true as companies increasingly look to go-to-market AI tools to reach out to these decision makers. If the underlying data is outdated, it completely undermines their ability to win business and grow revenue.
This is redefining what “good” data looks like. In this environment, advantage doesn’t come from having more data. It comes from having data that moves at the same speed as your market.
Demonstrating data governance
It should go without saying (although it hasn’t always) that another aspect of “good” data is compliance. Once again looking to address sins of the past, evidence shows that companies are also tightening how they control and govern data.
Organisations are dedicating a workforce equivalent to 11% of their legal function specifically to data governance, privacy, and compliance, reflecting a structural change in how businesses manage risk.
This shift is being driven by an increasingly complex regulatory environment. From GDPR and the EU Data Act to the EU AI Act and NIS2, organisations are facing expanding requirements that extend beyond legal interpretation into operational execution. Once again, it is forcing them to prioritise the acuity of the data their systems are running on.
Aspiring for data fluency
The market shift we are seeing from multiple isolated solutions, to AI models overlaid across available data sources, is requiring companies to take a closer look at their underlying data infrastructure to make sure they can meet their priorities: efficiency, intelligence, and impact.
As AI-powered tools become embedded into business workflows, the real differentiator won’t be the models themselves, but the standard of the data beneath them. Without high-quality, secure data, AI models will fail. While companies that have achieved “data fluency”, i.e. reached the ability to read their market thanks to the quality of the data they hold, will reap all of the rewards.
Dominic Allon is CEO of Cognism.
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