Business success depends on having the right information at the right time. The stakes are even higher with artificial intelligence spurring an unprecedented need for businesses to have access to quality, trusted information. In fact, 92% of analytics and IT decision-makers say trustworthy data is needed more than ever. To meet this demand, leaders need to empower everyone to lead with data maturity and build a trusted data foundation across their organizations.
To discover how leaders are fortifying that foundation and blazing new trails for an AI-driven future, Salesforce reached out to over 10,000 global analytics, IT, and business leaders and summarized the findings into a new State of Data and Analytics Report.
Let’s dig into a few key takeaways and find out how you can apply these insights to make the most of your data and seize the AI opportunity.
Takeaway #1: Without trusted data, you can’t build credible AI
Every AI use case — from predictive analytics to IT management — is powered by data. But your AI solutions won’t produce the results you’re looking for if you aren’t working with quality, trusted data. It’s a simple calculus: Flawed inputs equal flawed outputs.
So what, specifically, makes data trustworthy? With ever-increasing volumes of data, what best practices should be followed to ensure that all data meets this standard? Here are just a few of the things you should ask to determine whether your data can be trusted.
Takeaway #2: Getting more value from your data is easier when your data and business strategies are aligned
The amount of data generated worldwide is on the grow, and is expected to increase 22% on average over the next 12 months.
Getting value from this avalanche of data is top-of-mind with business leaders — 94% agree they should be getting even more value from their data. But, according to our survey, two significant obstacles are blocking the way: security considerations and managing the sheer volume of data.
These two issues go hand-in-hand. As data volumes increase, security policies need to keep pace. But six in 10 analytics and IT leaders say they are in the dark about how line-of-business teams are using data. This lack of visibility between parts of the business leaves the organization not only more vulnerable to security threats but also makes it difficult for the business as a whole to get value from all of its data assets.
To get more value from data, business and data and analytics leaders need to address the disconnect. The solution begins with a deep understanding of business goals, and what data is required to support them. Once leaders are aligned on business strategy, they can build a data strategy and a technology infrastructure that supports business priorities. This unified approach helps to drive much-needed insights across the organization and reduce data silos that impede collaboration and decision-making.
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The end result is improved financial performance, increased productivity, and long-term competitive advantage. According to McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers, and 19 times as likely to be profitable.
Takeaway #3: Higher data maturity helps reap more AI rewards
“Data mature” is another way of saying that an organization is well into its data transformation, using data to make key day-to-day decisions, drive innovation, and plan for the future. And data maturity has its rewards: McKinsey research shows data-driven companies accomplish goals faster and that their initiatives contribute at least 20% to earnings before income taxes.
A data mature organization also has a well-defined data culture — which equips everyone with the insights they need to be data-driven. In a data culture, data is woven into every aspect of its operations. Organizations with data cultures:
- Align data and analytics to business outcomes
- Prioritize data in decision-making and business processes
- Unite over a shared mission to lead with data
Data culture is a defining feature of a data mature company, and it’s no surprise that data maturity is a key indicator of AI success, too. In fact, respondents in high-maturity organizations are twice as likely to have the high-quality data needed to use AI effectively.
Successful businesses have mastered the art and science of adaptation — and the AI revolution has proven that data transformation is one of the most critical adaptations of all. It’s been said that AI will be as pervasive as the internet, and based on the current hype cycle, that doesn’t seem so far-fetched.
Discover data tactics for the age of AI
See trends from global business, IT, and analytics leaders in our new State of Data and Analytics Report.