Within IBM Netezza's product marketing team, Mike Kearney communicates product vision and strategy to a wide community including our customers and prospective customers, our business partners, industry analyst, and media. With more than 25 years experience in IT, Mike has worked in telecommunications, financial services, pharmaceutical, energy and manufacturing industries and for vendors of database, system management and web content management products.
Sr. Product Marketing Director
May 9, 2012
Further to news of SUNY’s exploration of big data to understand possible causes of multiple sclerosis, I spoke with David Smith, VP of Marketing at Revolution Analytics, for a briefing on some advantages of R for analysis of large data sets.
April 26, 2012
Monty Python’s Mr Gumby was anatomically on the money when he exclaimed “my brain hurts”. Our brains and bodies communicate via long axons that run from gray matter through our spinal cords. Neural communication is two way – when we cut a finger or knock a shin messages charge across a network of nerves to reach our brains, and it’s from here that feelings of physical hurt emanate. Communicating bodily damage is just one type of bi-directional traffic flowing between our brains and spinal cords; this network’s vital importance becomes devastating obvious when it fails.
April 3, 2012
When I hear the term inventory management industries that first spring to mind are manufacturing and retail, so I listened closely when I heard a specialist from our financial services team using the term. Banks and other finance companies must exercise tight control of their computing assets to comply with industry regulations. Given the distributed and networked configurations of modern computer systems, meeting regulatory demands can be a challenge and understanding data residing on each machine complicates this already difficult task.
December 12, 2011
Cutting costs and improving process efficiencies are common paths businesses take to boosting their bottom line. The difficultly of driving top line revenues makes this a road less traveled. IBM Netezza customers are showing the way - use advanced analytics on big data to grow top line revenues. This approach unifies sometimes disparate parts of organizations - IT, sales, marketing, operations, and finance - to net out significant opportunities and revenue lift for their business.
November 4, 2011
A slew of announcements made during last week’s IOD conference assert IBM’s R&D machine is running in top gear. Here’s my take on progress integrating Netezza with IBM’s broad product portfolio and how this engineering further simplifies our customers’ warehousing and analytics projects.
October 24, 2011
Smart Consolidation recommends running workloads on the platform best suited to the purpose, and for many of the world’s largest organizations IBM’s System z – the mainframe – is the system powering their transactional heartbeats. The newly-announced IBM DB2 Analytics Accelerator (IDAA) for z/OS brings the analytics performance of IBM Netezza into System z. To discover more, I spoke with two of my colleagues at IBM: Gary Crupi, Executive IT Specialist and Ola Mayer, Product Management Director.
August 24, 2011
While first generation data warehouses managed data, the modern data warehouse provides an information service – its massively parallel processing architecture has the compute muscle to run computationally demanding analytic applications. These bring us into a mathematical world of ones and zeros, algebra and algorithms.
July 20, 2011
Andy Dornan’s recent interview with Tim Shetler in Information Week caught my eye. At IBM Netezza we have long argued that Exadata is not an appliance – see our short video on operational simplicity – and a vice president of product management at Oracle agrees: "We don't call it an appliance because that suggests something that's relatively small or that requires very little administrative support," said Shetler.
May 2, 2011
Thanks to all who responded to last week’s poll when we asked about your organization's approach to enterprise data warehousing. Twenty percent of you currently manage all enterprise data in a centralized EDW. Congratulations to the technical teams involved - this is an impressive achievement, and the data integration challenges formidable. I’d be interested to learn more. Is your warehouse populated predominantly by data from transactional systems? What role does semi-structured or unstructured data play in your enterprise’s decision making?