As hundreds of AI initiatives and programs are underway across the Department of Defense, many are facing new and diverse challenges when it comes to operationalization. Selecting a solution and putting it into practice are certainly not the same task, creating challenges that span both organizational and data facades. For example, the rise of hybrid and multi-cloud architectures present data integration, access and management challenges for many as agencies seek to leverage data assets that span across both on-premise and hosted solutions. This may be why the GSA Data Center and Cloud Optimization Initiative Program Management Office recently released a Multi-Cloud and Hybrid Cloud Guide to help agencies make better decisions about cloud architecture. Further complicating matters is that the DoD is facing a groundbreaking December award of the Joint Warfighting Cloud Capability procurement. Pentagon Chief Information Officer John Sherman describes the JWCC as a "multi-cloud effort that will provide enterprise cloud capabilities for the Defense Department at all three security classifications: unclassified, secret, and top secret all the way from the continental United States out to the tactical edge."

AI adoption is critical for business success in the changing, competitive market. A HIMMS and IBM study found that 64% of respondents said their organizations placed a “critical” or “high” strategic priority on AI. However, many organizations are still reluctant to fully adopt AI into their processes and decision-making for fear of the unknown. “To many who aren’t data scientists, AI is still a black box and that scares us,” says Kelly Combs, Director of Emerging Technologies Risk Services at KPMG. So how do organizations go about establishing trust in their AI? The answer lies in leadership buy-in, automated checks and balances, and access to clean and complete data. Here are three ways organizations can work to establish trust in their AI: 1. Unite your people and processes around strategic AI through education and information architecture Culture and strategy often trickle from the top of an organization down. This is why it is important to rally leaders and…

Some thought leaders, such as Elon Musk and the late Stephen Hawking, have repeatedly warned about the potential danger of artificial intelligence and expressed fear that AI may annihilate humans someday. Such fear has not been shared by the vast majority of computer scientists and data scientists, who consider the hyped drama of "man vs. machine" a distraction that is grounded in an intriguing but misguided fiction. Meanwhile, a true AI crisis is upon us now, and is having a huge impact on the business world. As much as enterprises are eager to embrace AI to innovate products, transform business, reduce costs, and improve competitive advantages, they find it very difficult to productionize AI and realize its full benefits, due to the time, budget, and skills required. As a result, the rate of AI adoption has significantly lagged the level of interest, particularly for small- and medium-sized enterprises, which are more resource-constrained.

The AI challenges organizations are wrestling with span everything from the integrity of the data being employed to drive AI models to a lack of skills. IBM this week confronted all the hurdles inhibiting adopting of artificial intelligence at a Data and AI Forum event. Industry analysts such as Gartner estimate that despite all the promise of AI, only 14% of organizations have deployed AI in a production environment. The AI challenges organizations are wrestling with span everything from the integrity of the data being employed to drive AI models to a lack of skills. Rob Thomas, general manager for data and AI at IBM, told conference attendees data, skills and lack of trust in AI models are the three biggest inhibitors of AI adoption today.

Landslides have caused more than 11,500 fatalities in 70 countries between 2007-2010. Over 1000 people were victims of a landslide that hit Sierra Leone in August 2017. The situation is getting worse as the volume and intensity of rainfall in West Africa is increasing. In April, Colombia's landslide left at least 254 dead and hundreds missing. Landslides are challenging across various levels, for example: social, economic, infrastructural, and environmental.

Gartner predicts that 60% of organizations will deploy data virtualization software as part of their data integration tool set by 2020. That's a big jump from the adoption rate of about 35% the consulting and market research company cited in a November 2018 report on the data virtualization market. But the technology "is rapidly gaining momentum," a group of four Gartner analysts wrote in the report. The analysts said data virtualization use cases are on the rise partly because IT teams are struggling to physically integrate a growing number of data silos, as relational database management system (DBMS) environments are augmented by big data systems and other new data sources. They also pointed to increased technology maturity that has removed deployment barriers for data virtualization users.

Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.

There was no shortage of AI in the agenda at Strata. Beyond the headlines, there was growing evidence that the big data community is starting to get serious about real time processing.

Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.