Goto

Collaborating Authors

10 big data blunders businesses should avoid

#artificialintelligence

Big data is a promising investment for firms, but embracing data can also bring confusion and potential minefields -- everything from where companies should be spending money to how they should be staffing their data teams. MIT adjunct professor Michael Stonebraker, a computer scientist, database research pioneer, and Turing award winner, said he sees several things companies should do to build their data enterprises -- and just as importantly, mistakes companies should cease or avoid. In a talk last fall as part of the 2019 MIT Citi Conference, Stonebraker borrowed a page from David Letterman to offer 10 big data blunders he's seen in the last decade or so. His (sometimes opinionated!) advice comes from discussions with tech and data executives during more than decades in the field as well as his work with several data startups. Companies should be moving their data out of the building and into a public cloud, or purchase a private cloud, Stonebraker said.


10 big data blunders businesses should avoid

#artificialintelligence

Big data is a promising investment for firms, but embracing data can also bring confusion and potential minefields -- everything from where companies should be spending money to how they should be staffing their data teams. MIT adjunct professor Michael Stonebraker, a computer scientist, database research pioneer, and Turing award winner, said he sees several things companies should do to build their data enterprises -- and just as importantly, mistakes companies should cease or avoid. In a talk last fall as part of the 2019 MIT Citi Conference, Stonebraker borrowed a page from David Letterman to offer 10 big data blunders he's seen in the last decade or so. His (sometimes opinionated!) advice comes from discussions with tech and data executives during more than decades in the field as well as his work with several data startups. Companies should be moving their data out of the building and into a public cloud, or purchase a private cloud, Stonebraker said.


CDOs step out of comfort zones as data monetization efforts increase - SiliconANGLE

#artificialintelligence

Monetizing data assets is enticing for businesses sitting on lakes of information about consumer likes, dislikes, wants and needs. The spotlight is on the benefits of artificial intelligence and machine learning to parse through it all, but this big data is personal data, and Wild-West attitudes to collection and analysis methods can have serious consequences in the modern business world. "Business leaders don't necessarily know how [AI models] work or what can go wrong with them," said Cortnie Abercrombie (pictured, left), founder and chief executive officer of the non-profit AITruth.org. "Data scientists are just trying to fulfill the challenge at hand, and they get really swept up in it to the point where data is getting bartered back and forth without any real governance or policies in place." So what are companies supposed to do? "What I'm advising executives, the board, and my clients is that we need to step back and think bigger about this, think about it not just as GDPR -- the European scope -- it's global data privacy," said Carl Gerber (pictured, right), managing partner at Global Data Analytics Leaders LLC.


HPE deploys new tool to operationalize AI and machine learning in the enterprise - SiliconANGLE

#artificialintelligence

Less than 10% … that's how many artificial-intelligence test projects are estimated to be deployed into full-scale production in enterprise environments, according to a recent report from the International Institute for Analytics. There are a number of reasons for this surprisingly small amount, including an overwhelming amount of data and the lack of easy-to-use tools to analyze it. It's a problem that calls for operationalizing AI and machine learning, making it accessible and repeatable consistently. "Ultimately, if you want to get business value from those models and all of the hard work that you've done, it has to be injected into the business process," said Anant Chintamaneni (pictured), vice president and general manager of BlueData at Hewlett Packard Enterprise Co. "Operationalization of machine learning is ultimately the key, and that's the progression that enterprises have to make." Burris was joined for a digital community event by co-host Stu Miniman (@stu), and they also interviewed Nanda Vijaydev, distinguished technologist and lead data scientist at HPE; Patrick Osborne, vice president and general manager of big data, analytics, and scale-out data platforms at HPE; and Wikibon analyst James Kobielus (@jameskobielus).


Q&A: Revealing the hidden value in dark data deposits - SiliconANGLE

#artificialintelligence

More data is concealed in company archives than is available on the web. But that data is in silos, secured behind firewalls, unsearchable by web crawlers. Some of it may even be sitting in paper files, stored in a warehouse and forgotten for decades. But as data becomes an ever stronger driving force in the economy, companies are starting to realize the value of these hidden assets. "More and more companies are removing the silos, bringing that dark data out," said Gokula Mishra (pictured), business-driven information technology strategy expert and former senior director of global data analytics and supply chain at McDonald's Corp. "The key to that is companies being able to value their data, and as soon as they're able to value the data, they're able to leverage a lot of the data."