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Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management can overcome those challenges. Achieving ROI from AI requires both high-performance data management technology and a focused business strategy.
First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients).
“Business leaders get scared and say, ‘Tell me the plan so I can sleep at night,’” said Ronica Roth, co-founder and principal of The Welcome Elephant. They are afraid of failure and the uncertainty of knowledge work, and so that’s stressful. You can always add more, but you can never get back the wasted time.
These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As Finally, our goal is to diminish consumer risk evaluation periods by 80% without compromising the safety of our products.”
A businessobjective to “arrive” more patients per hour or the CEO’s desire to leverage historical data to predict future patient volume and revenue doesn’t start with a technology discussion or spoon-feed IT a particular business strategy to execute. Deepa Soni, CIO, The Hartford Insurance Co. The Hartford Insurance Co.
While the past few years have left us with a business landscape scarred by the impact of economic and geopolitical uncertainties, the current AI movement has become a rocket ship for significant transformative changes set to accelerate new opportunities.
With so much economic uncertainty, coupled with the unrelenting advance of “Industry 4.0” But without the right data practices in place you run the risk of misusing data and missing opportunities. Manage and mitigate risk. Data defense minimizes risk while data offense ensures data is used to support businessobjectives.
Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
That, in turn, helps leaders to plan effectively for a range of circumstances, allowing for greater flexibility to accommodate uncertainty. Download Now: Select Your Closest Time Zone -- Select One -- Business Email *. In many cases, it is used to evaluate best case, worst case, and likely estimates.
As AI technologies evolve, organizations can utilize frameworks to measure short-term ROI from AI initiatives against key performance indicators (KPIs) linked to businessobjectives, says Soumendra Mohanty, chief strategy officer at data science and AI solutions provider Tredence.
Weigh your virtualization options VMwares shift in licensing strategy has left many organizations in a state of uncertainty, and potentially locked into multi-year terms. Buying time can help enable you to better mitigate and minimize risks while planning for long-term success.
: Trusted advisor: While enterprise architects can often be seen as the catalysts for technology they must provide credible guidance to business leadership, offering insights into technology trends, risks and opportunities and avoid repeating mistakes of the past. They must ensure any gaps are identified and addressed accordingly.
Industries use established frameworks, like ISO 55000, to align asset management with organisational goals supporting risk-based decisions, continuous improvement, and value realisation from assets. Missing context, ambiguity in business requirements, and a lack of accessibility makes tackling data issues complex.
While EA leaders have long been positioned as key enablers of digital transformation, the rapidly shifting business landscape of 2025 presents new pressures. Economic uncertainty, geopolitical instability, and the explosion of AI-driven initiatives mean that enterprise architects must redefine their roles to remain relevant and valuable.
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