Every AI project should start as a data project.
The first important step is Connect, organize and harmonize Your company’s data so you can understand and meet your customers’ needs with AI-powered solutions. According to Salesforce, nearly all analytics and IT decision makers surveyed (92%) said reliable data is needed more than ever.Status of data and analysis” 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 put pressure on data management teams to provide algorithms with fast-paced, high-quality data. 87 percent of analytics and IT leaders say advances in AI make data management a high priority
- The full potential of the data remains elusive: Analytics, IT, and business leaders all cite security threats as the top barrier to successful data management. However, confusion between data strategy and business goals complicates the effort. Meanwhile, the amount of data that companies generate is expected to grow by an average of 22% over the next 12 months.
- Data and AI are driving the road to success: To secure and scale data and analytics capabilities, analytics and IT leaders use a combination of strategies, such as reshaping data governance, strengthening internal data culture, and deploying cloud technologies. Simplifying IT management is one of the biggest drivers for moving apps and analytics to the cloud.
The demand for reliable data is greater than ever. 86 percent of analytics and IT leaders agree that AI’s outputs are only as good as its data inputs. Generative AI is making these demands even more intense, and analytics and IT leaders are racing to strengthen their data foundations. The report found that 92% of analytics and IT leaders agree that the need for trusted data is greater than ever. However, only 6% of these leaders describe their data maturity as below industry standards or nonexistent, representing — at best — maturity benchmark disadvantage against peers or — at worst — overconfidence in data strategies 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 think their organization should get more value from data.
The top priorities for analytics and IT leaders are:
- Improve data quality.
- Strengthen security and compliance.
- Build AI capabilities.
- Improve data literacy across the company.
- Modernize equipment and technology.
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%) give generative AI a big advantage in interesting use cases, from content creation to software development. Marketing leaders are especially nervous that they aren’t fully utilizing generative AI in workflows, with 88% worried their companies are falling behind.
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Generative AI raises data ethics and equity concerns. The report notes that 83% of IT leaders think companies need to work together to ensure that generative AI is used ethically.
The top perceived benefits of data management analytics and IT leaders are:
- Make quick business decisions
- Functional skills
- Free up time for valuable work
- Automated workflow
- Improved customer satisfaction
Given the dependence of AI’s output on the quality of the 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 readiness. Data maturity is a building block of successful AI adoption. High-maturity respondents are 2 times more likely than low-maturity respondents to have the high-quality data needed to use AI effectively.
The full potential of the data remains elusive
Forty-one percent of line-of-business leaders say their data strategy has partial or no alignment with business objectives. Similarly, 37% of analytics and IT leaders see room for improvement. More than six in 10 analytics and IT leaders are in the dark about line-of-business teams’ data usage or speed of insight. Furthermore, less than one-third of analytics and IT leaders track the value of data monetization.
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Security is the main barrier to achieving data goals. Security threats are primary data challenges for business, analytics, and IT leaders. 94% of business leaders believe they should get more value from their data, what’s holding them back? 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 of how data is used within their company.
Data accuracy — and confidence in data accuracy — is a key component of trusted data. The departments closest to the data, such as the data and analytics teams, have the highest confidence in their data accuracy Trust is low among line-of-business leaders, revealing an opportunity to build data trust across marketing, sales and service teams — only 57% of data and analytics leaders have full confidence in data accuracy.
Increased data on users – but that creates an opportunity. More than two-thirds of analytics and IT leaders expect data volumes to grow by an average of 22% over the next year. They expect similar growth rates across different sources, including third-party data and device data Nearly two-thirds (65%) of consumers say they expect companies to adapt experiences to match their changing needs, yet 80% of business leaders say personalization is difficult to scale.
Data and AI are driving the road to success
Improving trust in information is more than a technical solution; Culture is important in driving confidence and adoption. Data culture is the collective behavior and beliefs of people who value, practice and encourage the use of data to improve decision making. It equips everyone in an organization with insights to tackle complex business challenges. More than seven in 10 are increasing budgets for data analytics tools and training.
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Data governance is more than a list of rules and restrictions. Used strategically, it can help increase data credibility. 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 principles by which information is collected, managed, stored, measured and communicated. It establishes parameters for data access, accuracy, confidentiality, security and retention. The report found that 86% of high-maturity organizations use governance to democratize data access, compared to 70% of less-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 when large amounts of data accumulate in a location or system, they attract additional applications and services, making data migration more difficult and more expensive. The key message here is that technology leaders must aim to simplify IT management.
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The 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 migration, or are always in the cloud, and an additional 17% plan to take action.
Top priorities for IT leaders are:
- Simplify IT management
- Increase security
- Increase flexibility
- Improve scalability
- Increase capacity for innovation
The report concludes that unlocking the value of data is no small feat. Fortunately, analytics and IT leaders can turn to data and analytics platforms for help. Additionally, technology leaders want solutions that pave the way for growing AI capabilities. Ultimately, technology leaders have their work cut out for them, but the benefits of maximizing the value of their data are worth the effort.
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