The lack of complete, updated and quality data is one of the biggest obstacles for companies to take advantage of new technological tools, the new study concluded “State of Data and Analytics”released by Salesforce this week. Among the business leaders surveyed, 76% said they are under increasing pressure to generate value through data, but the approach needs to change. The vast majority of data and analytics leaders (84%) reported the need to completely revamp strategies to successfully execute their AI ambition.
“The lessons we learned from the first waves of AI adoption provide a roadmap for companies to become true agent organizations, where people and intelligent agents work together,” commented Michael Andrew, Chief Data Officer at Salesforce, on the study’s findings. “Reliable, unified and contextual data is the key to unlocking the full potential of AI,” he continued, stressing that “this is the time” to strengthen the data foundations, in order to be able to implement AI at scale and “generate real value.”
One of the problems is that data infrastructures are not keeping up with business ambitions, which is why 63% of data leaders admit that their companies have difficulty transforming data into business value. This reveals, the study points out, a clear discrepancy between what is perception and what is reality.
At a time when more and more devices are connected and generating huge volumes of data, companies that are unable to use them to improve efficiency and decision-making are at a disadvantage. Less than half of business leaders surveyed say they can generate reliable and timely insights from the data they collect. Even more worrying, about half admit that their companies draw the wrong conclusions because they don’t have the right business context.
With data that is not up to date or of poor quality – what Salesforce calls deficient data – the path to agent AI is more difficult, which is the main trend right now. Two-thirds of data leaders feel pressure to implement AI quickly, but many (42%) don’t fully trust the accuracy or relevance of AI-generated answers.
In other words: on the one hand, managers want more and better insights and increased productivity. On the other hand, technical managers warn of problems and the need for a new approach to data management and analysis.
Salesforce research points to ways to reduce this gap, following the best practices of the most prepared companies. They include ensuring up-to-date and context-rich data, as even quality data does not have the same value if it is isolated, reinforced governance and “zero copy” architectures, which allow access to dispersed data, regardless of where it is located.