DBTA E-EDITION
May 2026
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Trends and Applications
As organizations embrace AI technologies, they face growing challenges related to fairness, privacy, and unpredictable system behavior. At Data Summit 2026, Nicole Janeway Bills, CEO and founder, Data Strategy Professionals, led the session, "AI Risks & Risk Mitigation Strategies," using real-world examples to help attendees recognize 10 critical AI risks, detect emerging issues early, and implement practical measures to mitigate them while maximizing business value.
AI adoption is accelerating, but results are falling short, according to new DBTA research that will be released soon. Fewer than half of AI initiatives succeed, and most pilots never reach production. What's more, AI failures are expensive, with 36% costing more than $500K and 16% exceeding $1M. The root cause is clear: data and the gaps in quality, access, trust, and lineage that continue to limit outcomes and prevent AI from scaling. This is not a new problem; it is an unsolved one.
Self-service analytics promises faster answers, yet many organizations end up with conflicting dashboards, duplicated or leaky data, and renewed reliance on IT. The issue is not control versus flexibility, its delivering governed, analytics-ready data that supports secure independent exploration and consistent reporting.
For more than a decade, enterprises have been locked in a cycle of building increasingly complex data stacks to keep pace with the demands of modern analytics. Warehouses, OLAP engines, and streaming systems have all played their part. However, as data volumes have increased, customer-facing use cases have proliferated, and costs have risen, the cracks in legacy approaches are widening.
Imagine you're on LinkedIn using your work laptop when a co-worker messages you a document for review. You click the attachment—which looks like a standard PDF link—without a second thought. In one click, you've handed a hacker the keys to not only your data, but your entire company's network.
How do you separate the signal from the noise and explore what's truly working in AI implementations across industries, and where companies are falling short? From the common pitfalls that derail well-intentioned efforts to the design principles behind AI initiatives that create measurable value, Ryan Frederick, principal, Transform Labs, shared the lessons that every leader needs to understand to make AI a competitive advantage rather than a costly experiment during his session, "Learnings From the Front Lines of AI," at Data Summit 2026 in the new Data + AI Leadership Forum.
Columns - DBA Corner
Most organizations spend a great deal of time thinking about database performance. DBAs tune SQL, design indexes, and monitor workloads to ensure that applications run quickly and efficiently. Performance is important, of course, but there is another attribute that can be even more critical: resilience.
MV Community
In a recent blog post, Mike Wright, CTO at Zumasys noted that the rise of AI has changed PICK MultiValue Modernization ultimately for the better. AI assisted development has changed the economics of modernization. It does not remove the need for strong leadership, experienced teams, sound architecture, or careful planning. It does not make legacy modernization easy. But it does reduce the cost and effort of some of the hardest parts of the work, he writes.
Following the MultiValue World Conference in April, Jay LaBonte, founder and chairman, MultiValue World Foundation, and president, Paradigm Systems, said the future is bright for the MultiValue space.