Data management agility has become a mission-critical priority for organizations in an increasingly diverse, distributed, and complex environment. To reduce human errors and overall costs, data and analytics (D&A) leaders need to look beyond traditional data management practices and shift toward modern solutions like AI-enabled data integration
Industrial companies realize that digitization is more than a buzzword and know that e-commerce, data analytics , and other solutions will transform every aspect of their businesses. Some have even set ambitious goals to reduce costs, increase revenues, or improve cash management through digital solutions, with many focusing on the digitization of sales and marketing as more end customers migrate online.
When organizations bring machine learning efforts to the cloud, the benefits can include speed, productivity, and technology. The key is to start with a business case, assess data and model requirements, invest in the right talent, and adopt a ‘fail fast’ mindset. Machine learning (ML) is transforming nearly every industry, helping companies to gain efficiencies, innovate rapidly, and meet customer needs at an unprecedented pace and scale.
Ever since the onset of COVID-19, technology has become even more essential in responding to uncertainty and creating a more adaptive, agile and composable organization.
In fact, Gartner research reveals that even amid the current cost constrained environment, where one-third of HR leaders plan for budget cuts this year, 90% still plan to either maintain or increase their investment in technology. Gartner has identified five trends that HR leaders must evaluate and embrace to advance their multiyear strategic technology planning process:
Healthcare has often been the victim of hyped-up technology, so it’s no surprise that CIOs are wary of new trends. Welcome to the buzz surrounding low-code software. Low code offers the potential to facilitate the development of solutions by using a graphical user interface instead of the traditional approach of relying on hand-coded programming, thus requiring little to no coding to build applications and processes.
As much as any big brand, Domino’s Pizza leans heavily on data to improve customer service. But the data science team at the world’s No. 1 pizza maker employs a secret ingredient to augment its decision-making: a machine learning (ML) hosting platform that helps it deliver models to production more quickly.
Many organizations are generating significant value with artificial intelligence (AI) and recognize that this technology will shape the future. At the same time, organizations are discovering that AI could expose them to a fast-changing universe of risks and ethical pitfalls that regulators signal they’ll be watching for—and potentially penalizing.
Veteran IT leader and early AI adopter Jim Swanson explains how he’s integrated AI and data capabilities with the business to create exponential change. In Jim Swanson’s personal life, AI is omnipresent.
Offering a compelling omnichannel experience used to be the bleeding edge of retail. Now it’s a requirement for survival. More than one-third of Americans have made omnichannel features such as buying online for in-store pickup part of their regular shopping routine since the pandemic, and nearly two-thirds of those individuals plan to continue.
How many employees have ever asked IT to collect more analytics? The answer, as you might have guessed, is somewhere around zero. Yet, IT is constantly collecting data with evermore zeal, believing it is the best way to measure their value and offset threats to budgets.
Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever
Eleven years ago, an Office of Management and Budget memo pushed the concepts of Agile and DevOps into the mainstream government consciousness by stating that “the Federal Government has not taken full advantage of this transformation due to poor management of its technology investments.” The promise of Agile DevOps was to get value from digital solutions faster, enable flexibility to support changing priorities, and adapt to future business needs.
Business leaders can better assess and strengthen analytical frameworks using seven evaluation criteria.
Before the COVID-19 pandemic, flexible work was a benefit that a lucky few employees, or especially innovative employers, had the luxury of experiencing. Yet within a year, lockdowns leaving little choice but to work from home has changed the way we think.
