One of the developing trends in computing, relevant to both enterprise and service providers alike, is the notion of workload-specific or application-centric computing architectures. These architectures, optimized for specific workloads, promise improved efficiencies for running their targeted workloads, and by extension the services that they support. Earlier this year we covered the basics of this concept in “Optimize Scalable Workload-Specific Infrastructure for Customer Experiences”, and this week HP has announced a pair of server cartridges for their Moonshot system that exemplify this concept, as well as being representative of the next wave of ARM products that will emerge during the remainder of 2014 and into 2015 to tilt once more at the x86 windmill that currently dominates the computing landscape.
Specifically, HP has announced the ProLiant m400 Server Cartridge (m400) and the ProLiant m800 Server Cartridge (m800), both ARM-based servers packaged as cartridges for the HP Moonshot system, which can hold up to 45 of these cartridges in its approximately 4U enclosure. These servers are interesting from two perspectives – that they are both ARM-based products, one being the first tier-1 vendor offering of a 64-bit ARM CPU and that they are both being introduced with a specific workload target in mind for which they have been specifically optimized.
I’ve recently been thinking a lot about application-specific workloads and architectures (Optimize Scalalable Workload-Specific Infrastructure for Customer Experiences), and it got me to thinking about the extremes of the server spectrum – the very small and the very large as they apply to x86 servers. The range, and the variation in intended workloads is pretty spectacular as we diverge from the mean, which for the enterprise means a 2-socket Xeon server, usually in 1U or 2U form factors.
At the bottom, we find really tiny embedded servers, some with very non-traditional packaging. My favorite is probably the technology from Arnouse digital technology, a small boutique that produces computers primarily for military and industrial ruggedized environments.
Slightly bigger than a credit card, their BioDigital server is a rugged embedded server with up to 8 GB of RAM and 128 GB SSD and a very low power footprint. Based on an Atom-class CPU, thus is clearly not the choice for most workloads, but it is an exemplar of what happens when the workload is in a hostile environment and the computer maybe needs to be part of a man-carried or vehicle-mounted portable tactical or field system. While its creators are testing the waters for acceptance as a compute cluster with up to 4000 of them mounted in a standard rack, it’s likely that these will remain a niche product for applications requiring the intersection of small size, extreme ruggedness and complete x86 compatibility, which includes a wide range of applications from military to portable desktop modules.
Language is evolving; the written word is giving way to visual vocabulary.
Interpersonal communications are shifting from being text-based to image-based, and you don't have to look far for the evidence: We spell using the Emoji alphabet; we comment with photographs; we engage through pictures.
Therefore, it’s no surprise that consumer adoption of visual social networks is growing and that social chatter is becoming increasingly pictorial. Forrester's Consumer Technographics® data shows that US online consumers across generations are interacting with content on Instagram and Pinterest more than before:
As consumers become increasingly versed in the language of visual content, curated images become a powerful means of expressing opinions, conveying emotion, and recounting experiences. As a result, pure text analytics no longer suffice to interpret social chatter; instead, insights professionals have an opportunity to mine the wealth of media-rich data that increasingly pervades social networking sites.
Unified information architecture, data governance, and standard enterprise BI platforms are all but a journey via a long and winding road. Even if one deploys the "latest and greatest" BI tools and best practices, the organization may not be getting any closer to the light at the end of the tunnel because:
Technology-driven enterprise BI is scalable but not agile. For the last decade, top down data governance, centralization of BI support on standardized infrastructure, scalability, robustness, support for mission critical applications, minimizing operational risk, and drive toward absolute single version of the truth — the good of enterprise BI — were the strategies that allowed organizations to reap multiple business benefits. However, today's business outlook is much different and one cannot pretend to put new wine into old wine skins. If these were the only best practices, why is it that Forrester research constantly finds that homegrown or shadow BI applications by far outstrip applications created on enterprise BI platforms? Our research often uncovers that — here's where the bad part comes in — enterprise BI environments are complex, inflexible, and slow to react and, therefore, are largely ineffective in the age of the customer. More specifically, our clients cite that the their enterprise BI applications do not have all of the data they need, do not have the right data models to support all of the latest use cases, take too long, and are too complex to use. These are just some of the reasons Forrester's latest survey indicated that approximately 63% of business decision-makers are using an equal amount or more of homegrown versus enterprise BI applications. And an astonishingly miniscule 2% of business decision-makers reported using solely enterprise BI applications.
When it comes to data technology, are you lost in translation? What's the difference between data federation, virtualization, and data or information-as-a-service? Are columnar databases also relational? Does one use the same or different tools for BAM (Business Activity Monitoring) and for CEP (Complex Event Processing)? These questions are just the tip of the iceberg of a plethora of terms and definitions in the rich and complex world of enterprise data and information. Enterprise application developers, data, and information architects manage multiple challenges on a daily basis already, and the last thing they need to deal with are misunderstandings of the various data technology component definitions.
How does the CI pro responsible for marketing technology buying make an informed decision when faced with so many options? Well, to quote Ron Davies (feel free to summon the voices of Three Dog Night, David Bowie or Shelby Lynne, if you prefer), “It Ain’t Easy!” To help CI pros with their decision-making, my latest brief The Marketing Technology Buyer’s Dilemma provides advice on how to maintain customer focus while navigating market changes.
The battle over customer versus internal business processes requirements and priorities has been fought — and the internal processes lost. Game over. Customers are now empowered with mobile devices and ubiquitous cloud-based all-but-unlimited access to information about products, services, and prices. Customer stickiness is extremely difficult to achieve as customers demand instant gratification of their ever changing needs, tastes, and requirements, while switching vendors is just a matter of clicking a few keys on a mobile phone. Forrester calls this phenomenon the age of the customer. The age of the customer elevates business and technology priorities to achieve:
Business agility. Forrester consistently finds one common thread running through the profile of successful organizations — the ability to manage change. In the age of the customer, business agility often equals the ability to adopt, react, and succeed in the midst of an unending fountain of customer driven requirements. Forrester sees agile organizations making decisions differently by embracing a new, more grass-roots-based management approach. Employees down in the trenches, in individual business units, are the ones who are in close touch with customer problems, market shifts, and process inefficiencies. These workers are often in the best position to understand challenges and opportunities and to make decisions to improve the business. It is only when responses to change come from within, from these highly aware and empowered employees, that enterprises become agile, competitive, and successful.
This past Friday I had one of the most enjoyable meetings of my professional life. I had initially been worried about this particular meeting. After spending 3 nights in Switzerland, I travelled back to the UK, spent 2.5 hours at Heathrow and then caught a flight to Finland, arriving well after midnight. Knowing that I would only have a few hours’ sleep in Helsinki before heading 100 km north to Lahti for the meeting, I was concerned that travel and tiredness might take their toll.
I needn’t have worried. Several participants had enjoyed a late night at Lahti’s famous summer retreat, and they were pleased I had made the extra effort to join them. As we drove up to the log cabin in the woods, I was reminded of my 4-H camping days back in West Virginia. Though I had spent childhood summers barefoot, I was surprised when asked to remove my shoes for a business meeting. But, when in Finland… So we added our shoes to the 9 or 10 pairs already by the front door and joined the others in a family-style sitting room.
BI is no longer a nice-to-have back-office application that counts widgets — it is now used as a key competitive differentiator by all leading organizations. For decades, most of the BI business cases were based on intangible benefits, but these days are over — today 41% of professionals, with knowledge of their firm's business case, base their business case on tangible benefits, like an increased margin or profitability. As a result, BI is front and center of most enterprise agendas, with North American data and analytics technology decision-makers who know their firm's technology budget telling Forrester in 2014 that 15% of their technology management budget will go toward BI-related purchases, initiatives, and projects.
But taking advantage of this trend by deploying a single centralized BI platform is easier said than done at most organizations. Legacy platforms, mergers and acquisitions (M&A), BI embedded into enterprise resource planning (ERP) applications, and organizational silos are just a few reasons why no large organization out there has a single enterprise BI platform. Anecdotal evidence shows that most enterprises have three or more enterprise BI platforms and many more shadow IT BI platforms.
“Business Intelligence in the cloud? You’ve got to be joking!” That’s the response I got when I recently asked a client whether they’d considered availing themselves of a software-as-a-service (SaaS) solution to meet a particular BI need. Well, I wasn’t joking. There are many scenarios when it makes sense to turn to the cloud for a BI solution, and increasing numbers of organizations are indeed doing so. Indications are also that companies are taking a pragmatic approach to cloud BI, headlines to the contrary notwithstanding. Forrester has found that:
· Less than one third of organizations have no plans for cloud BI. When we asked respondents in our Forrsights Software Survey Q4 2013 whether they were using SaaS BI in the cloud, or were intending to do so, not even one third declared that they had no plans. Of the rest, 34% were already using cloud BI, and 31% had cloud in their BI plans for the next two years. But it’s not a case of either/or: the majority of those who’ve either already adopted cloud BI or are intending to do so are using the SaaS system to complement their existing BI and analytics capabilities. Still, it’s worth noting that 12% of survey respondents had already replaced most or all or their existing BI systems with SaaS, and a further 16% were intending to do so.