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Showing posts from 2016

That's too Expensive, the Pricing Battle

Pricing is a tricky beast. I've seen a lot of models out there, and each has pros and cons. Firstly it depends on who are your target customers. My experience is in Enterprise software, which typically has a larger transaction price and volume, yet the number of deals is smaller. When focusing on SMBs as a target, the models change along with the selling motion. For good reasons most software companies want to have both models, but I haven't seen many companies able to execute this strategy. You end up with vendors adopting one model and causing fractures in the way tooling is licensed, many times the economics don't equate to good business decisions for the end user or the vendor. Here are the various licensing models I've seen in the IT Operations Management space: Application footprint or infrastructure footprint based pricing Per node, per CPU, per application server, per JVM, per CLR, per runtime User-based pricing Per concurrent user, per named

Industry Insights: The Cycle of Innovation: The Rise and Fall of HP Software

I find the cycles in technology fascinating, and it's an unfolding lesson of historical cycles. Although we believe our industry moves at a rapid pace there are many macro cycles which occur over decades, the patterns do not change much. The first and current example is HP Software (with more focus on IT Operations). Let's rewind to the foundational pieces of HP Software, which came from the acquisition of Mercury Interactive in 2006. HP spent $4.5b to purchase Mercury and built a large well-established business off the platform in both Quality Assurance (QA) and IT Operations. Over time HP failed to invest, in what at one point was the market dominance of QA and a substantial footprint in ITOps, these once large market shares eroded as technologies commoditized and the buying shifted to best-of-breed. HPs solution set became difficult to implement (even for HP engineers), and ongoing management is hard requiring consulting and many resources. Having managed this portfolio at

Breaking Down Engineering Investment at Innovative Companies

I’m always looking for good ways to understand how companies invest money internally. When I was a Gartner analyst, I would keep an eye on data from Glassdoor and LinkedIn regularly to try to gauge the trends. I would use them in my understanding of companies versus what I was being told by the companies and end-users themselves. I always did this by checking content on the sites regularly and recording it into build patterns and trends. Thankfully, LinkedIn has come to the rescue by releasing the Premium Insights feature on company pages . I’ve been looking at this data, and I’m finding some interesting trends regarding what it uncovers. I’m also going to compare the LinkedIn data with the Gartner data which shows market share and revenue.   I’m going to have a look at the larger companies in ITOM to compare those which are investing for the future of their customers and those who seem to prioritize other functions. I’m going to look at Support and Engineering percentages and r

Industry Insights: The Challenges Behind New User Interfaces

The future of computing will be bifurcated. On one hand, there will be entirely new models for computing such as voice, autonomous agents, and bots, with no traditional user interfaces. On the extreme opposite hand, there will be new user interfaces augmented with our ‘real’ worlds, such as the innovation done by Microsoft holographic computing technologies, along with virtual reality platforms coming to market from Google and Facebook. Bringing these trends to fruition, though, will require some key enabling technological limitations to be overcome. Voice It’s been slowly happening for a while now: Voice recognition will change one of the key interfaces with today’s computing and applications. Apple’s Siri, Google Now, Microsoft Cortana, and the super-hot Amazon Echo, along with their smart agents, are the practical embodiments of a growing trend toward the application of machine learning to voice and data. Andrew Ng, chief scientists at Baidu, says that 99 percent accuracy is th