Data security and reliability have always been core pillars of a data program. But rapid advancements in artificial intelligence (AI) have made these components all the more important. Ensuring AI is fuelled by accurate, comprehensive data is key to realising its full potential.
Zuko Mdwaba, Salesforce area vice-president, Africa executive and South Africa country leader
Analytics and IT leaders are nearly unanimous (92%): the need for trusted data is higher than ever. Despite generally positive self-assessments by IT and analytics leaders, however, over nine in 10 business leaders believe they should be getting even more value from their data.
This is according to Salesforce’s State of Data and Analytics report, a survey of over 10,000 analytics, IT and line-of-business leaders worldwide which reveals how, to meet the challenges of data accuracy and security, leaders are leaning on data governance and data culture.
Data maturity is a precursor to effective AI use
From content creation to software development, business leaders are fully embracing the promise of generative AI. Technical leaders report noticeably faster decision-making and operations. Analytics and IT leaders say they have more time to tackle strategic challenges, rather than mundane tasks.
Despite its novelty, generative AI is advancing quickly. 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 makes data management a high priority.
Taking advantage of generative AI requires complete, unified, and accurate data. But roadblocks to achieving this remain, holding IT leaders back from integrating these capabilities into their current tech stack.
IT leaders are also wary of ethical considerations, biassed or inaccurate results. Eighty-three percent of IT leaders think businesses must work together to ensure generative AI is used ethically. Yet fewer than a third consider ethical use guidelines to be critical.
Data maturity is a building block of successful AI adoption. High maturity organisations cite superior infrastructure, strategy, and alignment compared to low data maturity organisations. When it comes to data quality, high-maturity respondents are twice more likely to have the high-quality data needed to integrate AI effectively.
Connected strategies as a brand differentiator
Getting a handle on organisational data may not pay off until business and technical Leaders readily admit they need tighter alignment. 41% of line-of-business leaders say their data strategy has only partial or no alignment with business objectives. Over six in 10 analytics and IT leaders are in the dark about line-of-business teams’ data utilisation or speed to insight.
Sharing key performance indicators (KPIs) and increased tracking of critical metrics could be one giant leap forward. Data quality, data utilisation, data management and costs, data services delivery, and the ROI of data initiatives are all relevant.
Departments closest to the data, like data and analytics teams, have the highest confidence in their data’s accuracy. Yet even these teams have room for improvement.
Data accuracy — and confidence in this accuracy — is a key component of trusted data, yet as opportunities to integrate new data sources and leverage new technologies increase, so do vulnerabilities.
With the volume and complexity of data set to increase substantially, harmonising data to deliver better customer experiences presents an opportunity for competitive advantage.
The popularity of cloud solutions suggests that organisations seek enhanced security as well as the flexibility to adapt and choose where their data resides, rather than being locked into a single environment.
By establishing data governance – setting the rules or policies by which information is collected, managed, stored, measured, and communicated – companies can set the foundations by AI success with parameters for data access, accuracy, privacy, security, and retention.
Culture to maximise data value
Beyond technical fixes, instilling a data culture is critical to driving data confidence and adoption. Equipping everyone in an organisation with insights for tackling complex business challenges has promising payoffs, from greater employee productivity and decision making, to superior customer service.
Leveraging non-technical, user-friendly solutions will also ensure that data-driven decision-making is democratised across the organisation.
Nearly three-quarters of analytics and IT organisations have already started their cloud migrations, or have always been in the cloud, and an additional 17% plan to make the move.
Business leaders are nearly unanimous: data and analytics improves decision-making — provided the data results are accurate and accessible. But unlocking the value of data is no small feat. Technical leaders may have their work cut out for them, but with the support of the wider business the benefits of maximising their data’s value that paves the way for growing AI capabilities is well worth it.