Every AI project begins as a data project, but it's a long, winding road

Research based on insights from more than 10,000 analytics, IT, and business leaders reveals the need for a strong data foundation in order to fuel AI adoption and benefits.
Written by Vala Afshar, Contributing Writer
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Every AI project should begin as a data project. 

The first important step is to connect, organize, and harmonize your company data so you can understand and meet the needs of your customers with AI-powered solutions. Nearly all analytics and IT decision makers surveyed (92%) say trustworthy data is needed more than ever before, according to Salesforce's "State of Data and Analytics" report. Salesforce surveyed 5,540 analytics and IT decision-makers and 5,540 line-of-business leaders worldwide.

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Here is the executive summary of that report: 

  • A strong data foundation fuels AI:  Advances in AI are fast-moving, putting pressure on data management teams to supply algorithms with high-quality data. Eighty-seven percent of analytics and IT leaders say advances in AI make data management a high priority.
  • Data's full potential remains elusive: Analytics, IT, and business leaders all cite security threats as the top barrier to successful data management. However, misalignment between data strategy and business goals complicates efforts. Meanwhile, the amount of data that companies generate is expected to increase 22% on average over the next 12 months.
  • The road to data and AI success is winding:  To secure and scale data and analytics capabilities, analytics and IT leaders use a combination of strategies, like reimagining data governance, strengthening internal data culture, and deploying cloud technologies. Simplifying IT management is the biggest driver for moving apps and analytics to the cloud.

The demand for trusted data is higher than ever. Eighty-six percent of analytics and IT leaders agree that AI's outputs are only as good as its data inputs. Generative AI is intensifying these demands, and analytics and IT leaders are racing to fortify their data foundations. The report found that 92% of analytics and IT leaders agree the need for trustworthy data is higher than ever. However, only 6% of these leaders describe their data maturity as below industry standard or nonexistent, representing -- at best -- the difficulty of benchmarking maturity against peers, or -- at worst -- overconfidence in data strategy and capabilities.

The report also found that business leaders are not satisfied with the value they currently derive from their data. The report noted that 94% of business leaders feel their organization should be getting more value out of its data. 

The top priorities for analytics and IT leaders are: 

  1. Improve data quality.
  2. Strengthen security and compliance.
  3. Build AI capabilities.
  4. Improve company-wide data literacy.
  5. Modernize tools and technologies. 

A strong data foundation fuels AI

Generative AI is a significant leap beyond more established iterations of related technologies like predictive AI, and business leaders are embracing its promise. More than nine in 10 (91%) see generative AI as providing a major advantage given appealing use cases ranging from content creation to software development. Marketing leaders are especially nervous that they aren't fully harnessing generative AI in workflows, with 88% concerned their companies are falling behind.

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Generative AI spurs data ethics and equity concerns. The report noted that 83% of IT leaders think companies must work together to ensure generative AI is used ethically. 

Analytics and IT leader's top realized benefits of data management are: 

  1. Faster business decision-making
  2. Operational efficiency
  3. Freed up time for valuable work
  4. Automated workflows
  5. Improved customer satisfaction

Given the dependence of AI's outputs on the quality of underlying data, it's no surprise that nearly nine in 10 analytics and IT leaders say new developments in AI make data management a high priority.

Data maturity is a sign of AI preparedness. Data maturity is a building block of successful AI adoption. High-maturity respondents are 2x more likely than low-maturity respondents to have the high-quality data needed to use AI effectively.

Data's full potential remains elusive

Forty-one percent of line-of-business leaders say their data strategy has only partial or no alignment with business objectives. Similarly, 37% of analytics and IT leaders see room for improvement. Over six in 10 analytics and IT leaders are in the dark about line-of-business teams' data utilization or speed to insight. Furthermore, fewer than one-third of analytics and IT leaders track the value of data monetization.

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Security is the top roadblock to achieving data goals. Security threats are the primary data challenge for business, analytics, and IT leaders. With 94% of business leaders believing they should get more value from their data, what's stopping them? The report found that 78% of analytics and IT leaders say their organizations struggle to drive business priorities with data. Nearly half of analytics and IT leaders say they have either a partial view or no view into how data is used within their companies.

Data accuracy -- and confidence in data accuracy -- is a key component of trusted data. Departments closest to the data, like data and analytics teams, have the highest confidence in their data accuracy. Confidence among line-of-business leaders is lower, revealing an opportunity to instill data confidence across marketing, sales, and service teams -- only 57% of data and analytics leaders have complete confidence in their data's accuracy. 

Surging data overwhelms users -- but it poses an opportunity. Over two-thirds of analytics and IT leaders expect data volumes to increase 22% on average over the next year. They expect similar growth rates across a variety of sources including third-party data and device data. Almost two-thirds (65%) of customers say they expect companies to adapt experiences to match their changing needs, yet 80% of business leaders say personalization is difficult to scale.

The road to data and AI success is winding

Improving trust in data is more than a technical fix; culture is critical to driving confidence and adoption. Data culture is the collective behaviors and beliefs of people who value, practice, and encourage data usage to improve decision-making. It equips everyone in an organization with insights for tackling complex business challenges. More than seven in 10 are increasing budgets for data analysis tools and training. 

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Data governance is more than a list of rules and restrictions. Used strategically, it can help bolster data trustworthiness. In fact, 85% of analytics and IT leaders use data governance to ensure and certify baseline data quality. Data governance is the set of rules or policies by which information is collected, managed, stored, measured, and communicated. It establishes parameters for data access, accuracy, privacy, security, and retention. The report found that 86% of high-maturity organizations use governance to democratize data access, compared to 70% of low-maturity organizations.

Improving data quality is the number one priority for analytics and IT leaders. IT leaders must find ways to defy data gravity. Data gravity refers to the idea that as large amounts of data amass in a location or system, they attract additional applications and services, making data relocation more difficult and more expensive. The key message here is that technical leaders must aim to simplify IT management. 

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The overwhelming majority of analytics and IT leaders are moving their applications to the cloud. Nearly three-quarters of analytics and IT organizations have already started their cloud migrations, or have always been in the cloud, and an additional 17% plan to make the move. 

The top priorities for IT leaders are:

  1. Simplify IT management
  2. Enhance security
  3. Increase flexibility
  4. Improve scalability
  5. Increase capability for innovation

The report concludes that unlocking the value of data is no small feat. Fortunately, analytics and IT leaders can lean on data and analytics platforms for help. In addition, technical leaders want solutions that pave the way for growing AI capabilities. Finally, technical leaders have their work cut out for them, but the benefits of maximizing their data's value are well worth the effort.

To learn more about the State of Data and Analytics report, you can visit here

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