Urika-GX: A Game Changer for Big Data Analytics

There’s a lot of hype around big data in healthcare and the life sciences. But big data is here to stay. Information is what drives the entire industry. When I worked in big pharma, I learned that the product of a pharmaceutical company is not a pill, it's a label. And a label is just a specific assemblage of information, carefully distilled from terabytes of information collected and analyzed over the course of many years by many intelligent people. To compete, companies have to be very good at turning data into information, and information into knowledge. The stakes couldn't be higher, because every day millions of patients rely on the quality of this data and the strength of the analyses done by researchers. Analyzing big data is ... [ Read More ]

Graph Databases: Key Thoughts from Online Chat

It’s pretty interesting to see graph analytics gain traction in the work of big data. We’ve been focusing on graph databases to round out Hadoop® and Spark™ ecosystems and allow for more advanced analytics — and enable people to uncover never-before-seen patterns. (Tell me that’s not cool!) From solving real-world problems such as detecting cyberattacks and creating value from IoT sensor data to precisely identifying drug interactions faster than ever before, graph has become a powerhouse in looking at complex, irregular and very large datasets to identify patterns in near real-time. On March 16, we hosted an online chat titled “Graph: The Missing Link in Big Data Analytics” with industry experts from Deloitte and Mphasis. Sixty-one ... [ Read More ]

Graph: The Missing Link in Big Data Analytics

Graph analytics is gaining traction in the world of big data and IoT. From solving real-world problems such as detecting cyberattacks and creating value from IoT sensor data to precisely identifying drug interactions faster than ever before, graph has become a powerhouse in detecting never-before-seen connections and emergent patterns. It’s critical to understand how graph can be added to traditional Hadoop® and Spark™ workflows for successful results. Join us Wednesday, March 16, for a live online chat, “Graph: The Missing Link in Big Data Analytics,” to learn and discuss all things graph analytics. You can easily participate using a Twitter, LinkedIn or Facebook account. Hear from industry experts from Deloitte, Mphasis and Cray who ... [ Read More ]

The Best Practices are No Longer the “Best” Practices – Part II

Earlier this week, I shared my views about best practices and cybersecurity. Now I want to move beyond best practices as your sole defense. The traditional cybersecurity mindset is one of prevention, believing threats cannot penetrate — and this is why security plans fail. It’s easy to assume defenses are successful against an insidious threat. Metrics will show an effective compliance program, intrusion detection and access denial. Yet to take for granted that the threat is gone, rather than having simply moved to another path within your network, is foolhardy. Assuming there are numerous threats to your security measures that are coming in a dynamic and continuous fashion may seem paranoid, but just because you’re paranoid doesn’t mean ... [ Read More ]

The Best Practices are No Longer the “Best” Practices – Part I

There was a time when information systems security was simpler. It focused on the appropriate time frame to take a sample of a one-dimensional system log that tracked events like blocked traffic, virus detection and machines taken off-line. Reporting on routine operations is a best practice, and following best practices is always… well… a good practice. Include some metrics for the percentage of your personnel trained in cyber risk avoidance and endpoint compliance and you have a nice corporate report. But, does it really mean anything? Clearly, the continuous barrage of media reports on breaches, data theft and cybercrime are not abating. In today’s Cray blog post, I would like to take you through a few cybersecurity “best practices” ... [ Read More ]