Tags: SAP, Sapphire, Supernova
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R “Ray” Wang’s Constellation group is worth watching anyway. But just now there are a couple of good reasons.
First, if you’re a SAP user, they have coverage of the recent SAPphire conference. Remember that Ray’s primary expertise, from his days at Forrester, is in ERP. Just go to Constellation and search for “Sapphire 2014″ for pre- and post-event analysis. There are of course also replays and other notes on the SAP website, if you want to go back to the originals.
Secondly, they are launching the call for this year’s Supernova innovation awards. Again, worth watching if your focus includes the what, how and who of innovation in business. As I’ve commented before, I’m not clear on the relationship between this Supernova event and the one formerly hosted by Kevin Wehrbach of the Wharton Business School (University of Pennsylvania) but Wehrbach’s Supernova hasn’t happened since 2010 and was described by him in 2012 as “on hold”.
Note, by the way, that their URL has changed from constellationrg.com to just constellationr.com.
• Constellation: search for Sapphire 2014
• Call for Applications: SuperNova Awards for leaders in disruptive technology, Courtney Sato, Constellation, 17 Jun 2014
• SAPPHIRE NOW 2014 (SAP Events)
Tags: Big Data, Frost and Sullivan, Smart
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I’m on a Frost and Sullivan webinar: Growth, Innovation and Leadership (GIL: a major Frost theme). It’s a half-hour panel to discuss successful types of innovation and examples of future innovative technologies with Roberta Gamble, Partner, Energy & Environmental Markets, and Jeff Cotrupe, Director, Stratecast. David Frigstad, Frost’s Chairman, is leading. The event recording will be available in due course.
Frigstad asserts that most industries are undergoing a cycle of disrupt, collapse, transform (or die: Disrupt or Die is an old theme of mine). We start with a concept called the Serendipity Innovation Engine. It’s based on tracking nine technology clusters; major trends; industry sectors; and the “application labs” undertaking development (which includes real labs and also standards bodies and others). And all of this is in the context of seven global challenges: education, security, environment, economic development, healthcare, infrastructure, and human rights.
Handover to Gamble. This is a thread on industry convergence in energy and environment, seen as a single sector. Urbanisation, and the growth of upcoming economies, are major influences here in demand growth.
We do move to an IT element: innovation in smart homes and smart cities, with integration between sensor/actuator technology and social/cloud media: emphasising this, Google has just bought a smart home company (Nest Labs). City CIOs and City Managers are mentioned as key people – a very US-centric view when most urbanisation is not occurring in the developed world … we do return to implications for developing economies, where the message is that foundations for Smart (which includes effective, clean energy use) should be laid now while there is a relatively uncluttered base to start from.
Frigstad poses a question based on the idea that Big Data is one of the most disruptive trends in this market. Gamble suggests that parking is an example. Apps to find a parking spot, based on data from road sensors or connected parking meters, are not though only being piloted in San Francisco. Similar developments in the UK were mentioned at a Corporate IT Forum event I supported earlier this year.
It’s a segue into the next section: an introduction for Cotrupe, whose field is Big Data and Analytics. Examples of disruption around here include the Google car: who would have thought Google would be an automotive manufacturer? Is your competitor someone you wouldn’t expect? An old question, of course. The UK’s canal companies competed with each other and perhaps with the turnpike roads; they mainly didn’t foresee the railways.
Cotrupe’s main question is: What is Big Data really? He posits it as an element of data management, together with Analytics and BI. I’d want to think about that equation; it’s not intuitively the right way round. But high volume, rapidly moving data does have to be managed effectively for its benefit to be realised – delivering the data users need, when they need it, but not in to overwhelm them. And this means near real-time. It’s IT plus Data Science.
Frost suggest they are more conservative than some, because they see growth of the BD market held back by the sheer cost of large scale facilities.
We’re on the promised half hour for the primary conversations, but still going strong, basically talking with Cotrupe about various industry sectors where Big Data has potential: to support, for example, a move from branch based banking to personal service in an online environment. There’s some discussion of Big Data in government: how will this affect the style of government in perhaps the next 20 years? Cotrupe mentions a transformation in the speed of US immigration in recent years, where data is pre-fetched and the process takes minutes instead of hours. He’s advocating opening up, sharing of information: in other industries too, for example not being frozen by HIPAA requirements in (US) healthcare or, perhaps, EU data protection requirements. I have personal experience of obstructive customer service people trying to hide behind those, and in fact parading their lack of actual knowledge.
Cotrupe talks about privacy, not least in the wake of Snowden and what’s been learned about sharing between NSA and the UK agencies. Cotrupe would like to see theis ease of sharing brought to bear in other areas: but asks how we manage privacy here? There are companies which are leading the way in data collection in consumer-sensitive ways, and this needs to become standard practice. In any case, not collecting data you don’t need will reduce your data centre (should that be Data Center?) footprint.
As we come to a close, with a commercial for the September event in Silicon Valley, I have to say I’m not convinced this webinar was wholly coherent.
If you call something a Serendipity Innovation Engine I want to know how it relates to serendipity: that is, the chance identification of novel discoveries.
If you present a layered model, I expect the layers to relate (probably hierarchically) to one another. It would be more valuable to talk about the four elements of this model separately and be clearer about what each represents. For example, “Health and Wellness” occurs as a Technology Cluster (why?). It’s also a Mega Trend in a layer where Social Trends also sits; surely people’s concern about Health and Wellness is a social trend? Each layer seems to mix social, technical and other concerns.
I learned a more useful framework when teaching the OU’s Personal Development course. This really is layered. The two internal layers (this is for personal development) are one’s immediate environment, and other elements of your working organisation. Then Zone 3 (near external) encompasses competitors, customers/clients, suppliers and local influences. Zone 4 (far external) includes national and international influences: social, technological, economic, environmental and political (STEEP). On this framework you can chart all the changes discussed in today’s webinar and, I think, more easily draw conclusions!
• Frost & Sullivan Growth Innovation & Leadership
• Google buys Nest Labs for $3.2bn …, The Guardian, 13 Jan 2014
• STEEP framework: Sheila Tyler, The Manager’s Good Study Guide (third edition, 2007). The Open University. Pages 198-202