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.
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).
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."
Picture a room of information technology professionals where they are asked a simple question: How many have been working on a data science model that still has not gone into production after nine months? Then imagine that more than 90 percent raised their hands. That is what's known as a real business opportunity. "It all boils down to one thing: Companies need to use the data that they've been storing for years," said Ian Swanson (pictured), founder and chief executive officer of Datascience Inc., who posed the above question during a recent conference presentation. "We give the tools to data scientists to get that data in action. That's the last mile that we're all working on, and what's exciting is we can make it possible today."
Many see Splunk Inc. as the big big data company. They've got a lot of seven-figure deals filed away with large enterprises that implement their platform for bottom-up projects built from scratch by data scientists. So who would believe Splunk will be the ones to finally package big data into use-case ready products for smaller scale businesses? Access to the computing foundation layer will help Splunk build easy-to-use products for industry verticals, according to Susan St. Ledger (pictured), president of worldwide field operations at Splunk. Her company is tapping ecosystem partners in the open-source community, the likes of Apache Spark and Flink experts, for the parts necessary to knock out specific business use cases.