In the year ahead, data managers and their enterprises will be at a crossroads. Organizations will be leaning more heavily than ever on data—and the teams that manage it—for success with their customers, markets, and operations. The world of data is changing rapidly, and with it, its role in the business, from sitting on databases in the back end to competitive differentiation. Data is driving everything, everywhere.
“We have entered a new era of strategic competition,” said John DeSimone, president of cybersecurity, intelligence, and services at Raytheon, a business of RTX. “The name of the game is no longer about the volume of data. The competition is all about who can collect, access, exploit, and gain intel the fastest, ultimately giving them the advantages they are looking for.”
The following are key trends shaping the data landscape of 2024 as identified by industry leaders:
Artificial intelligence. Front and center to any discussion, of course, is the rapidly increasing use of AI. Expect to see more businesses seeking to “achieve competitive differentiation through AI and generative AI, powered by their data,” said Venkat Gupta, associate VP and portfolio leader for data estate modernization at Capgemini Sogeti. In turn, AI is poised to “empower the business to leverage computers for what they excel at—processing, analyzing, and curating data—but doing so in a manner that is explainable, ethical, and efficient,” said Bill Waid, chief product and technology officer for FICO.
At its best, AI may help “put IT efficiency into overdrive,” predicted Prasad Ramakrishnan, CIO of Freshworks. “People are beginning to better understand what AI looks like, and we’ll move beyond the hype to more valid use cases. As we see IT pros embrace AI to automate workflows and boost efficiency, CIOs need to focus on arming their teams with AI tools.”
Of course, AI won’t develop in a vacuum: It needs to smoothly integrate with existing systems and processes. “More use cases for AI in the data space will emerge, and existing data solutions will integrate AI into their platforms and offerings,” according to Sharad Varshney, CEO of OvalEdge. Expect to see more AI in “technologies supporting data discovery, contextualization, and predictive analysis.”
Open ecosystems. Increasingly supporting the drive to data-driven innovation and decision making will be open ecosystems that reach well beyond traditional enterprise walls. Data will be the foundation of these networks, said Waid. “For example, a reliable digital view of a customer must be constantly evolving based on data stimulus from the customer. The daily activities from interactions with apps, devices, transactions, and behaviors all intersect to form the backbone of personalization.”
Composable APIs and microservices. “Composability” will be another watchword for the reshaping of enterprise data systems in the year ahead, Waid continued. Business composability will be built upon “a kinetic data layer made available through API-first, microservices architecture,” he said.
Rapid product innovation will increasingly be through “ready-to-use modular components or building blocks that can be discovered, shared, combined, and recombined.”
This composable infrastructure will deliver greater “hyper-personalization at scale,” Waid predicted. “This requires a foundational layer of data kinetics, which drives AI and machine learning insights. The most forward-looking B2C companies are mastering this degree of hyper-personalization using platforms that connect legacy systems with dynamic data views and then transform the unified information into useful, actionable, and measurable strategies.”
Data responsibility. With the great analytic power that comes with AI and other capabilities we’ll see in the year ahead comes great responsibility—data responsibility, that is. “Data responsibility has been and will continue to be top of mind for leaders across industries,” said Andrew Reiskind, chief data officer at MasterCard. “As we see and implement emerging technologies like artificial intelligence, it is our responsibility to ensure that we embed privacy and security in our products. We are doing our due diligence to ensure that responsible AI and AI become synonymous.”
FinOps. There will be greater attention to the data that comes with metering cloud usage, or what is known as FinOps. “FinOps is similar to turning on the lights in your house—no need to keep them on all day,” said Jay Upchurch, EVP and CIO for SAS.“But variable usage isn’t how most IT organizations run. In a corporate data center, technologies, services, and infrastructure are always on. IT organizations are used to this model and struggle to take advantage of the cloud’s variable usage.”
Data democracy. As new tools, technologies, and platforms roll out, expect to see a greater push toward data democratization. “As data management silos and bottlenecks come crashing down, organizations will better understand the vital need for its teams and individuals to use data,” said Mathias Golombek, CTO at Exasol. “Additionally, this shift will be a catalyst for change as companies seek to train their workforces on ways to effectively use data and insights to make informed business decisions.”
While centralized data management systems are still seen as the dominant model, “the reality is that departments still own their data and have domain expertise,” said Jay Allardyce, GM of data and analytics at Insight Software. “How organizations can adopt a democratized and open fabric but employ the right data governance strategies to support faster innovation and adoption will be crucial. Doing so will only further support the adoption of AI, which requires strong domain knowledge for value to be truly extracted.”
With this responsibility will be increasing efforts for end users to have more control over their data. “Recording affirmative consent, scopes of usage, and the expiration of consent will dominate the industry and allow customers to take complete control of their data,”predicted Scott Zoldi, chief analytics officer at FICO. “For some, the work may involve identifying data no longer to use, re-examining usage of data brokers, or developing new consent tracking. Responsible AI usage of the data will need to remediate customer-reported data mistakes and knowing the data’s provenance.”
Workforce empowerment. Of course, effective data management isn’t just about technology, or even data alone. Over the coming year, “being strategic about your data management workforce will become a key differentiator for enterprises,” according to Barry Shurkey, CIO at NTT DATA Services. Enterprises will increasingly look to achieve “a well-balanced labor pyramid by offering career growth opportunities and cross-functional rotations inside and outside of their business units. Empowering employees with upskilling and reskilling opportunities will be instrumental, not only in attracting and retaining employees, but in creating intentional connections between the skills and competencies needed in the various roles within the organization.”