One of the best TV comedies in the UK over the last couple of years has been The IT Crowd. It is about a fictional IT department and plays to all the possible IT stereotypes. One of my favorite scenes is from the very first episode in which a ‘user’ is left waiting for their call to be answered for an excruciating amount of time and then another ‘IT professional’ is shown speaking to a ‘user’ in complete technology gobbledygook. Yes, this clip is funny but surely these are all extreme cases and only slim comparisons can be made to Enterprise IT today? 

I have to be honest here and say that during my time as an enterprise management consultant I saw all that happened on this clip, but surely modern day IT organizations don’t suffer from these problems? Well, maybe not to the same extent but how often have you heard, or even whispered, these famous words when working with the IT service desk or help desk:

 “Have-you-logged-a-ticket?”

AMD Quietly Rolls Out hUMA – Potential Game-Changer for Parallel Computing

Background – High Performance Attached Processors, Handicapped by Architecture

The application of high-performance accelerators, notably GPUs, GPGPUs (APUs in AMD terminology) to a variety of computing problems has blossomed over the last decade, resulting in ever more affordable compute power for both horizon and mundane problems, along with growing revenue streams for a growing industry ecosystem, most recently joined by Intel’s Xeon Phi accelerators which have to potential to speed adoption even further due to hoped-for synergies generated by the immense universe of x86 code that could potentially run on the Xeon Phi cores.

However, despite any potential synergies, GPUs (I will use this term generically to refer to all forms of these attached accelerators as they currently exist on the market) suffer from a fundamental architectural problem – they are very distant, in terms of latency, from the main scalar system memory and are not part of the coherent memory domain. This in turn has major impacts on performance cost, , design of the GPUs and the structure of the algorithms:

Performance – The latency for memory accesses generally dictated by PCIe latencies, which while much improved over previous generations, are a factor of 100 or more longer than latency from coherent cache or local scalar CPU memory. While clever design and programming, such as overlapping and buffering multiple transfers can hide the latency in a series of transfers, it is difficult to hide the latency for an initial block of data. Even for AMD’s integrated APUs,, in which the GPU elements are on a common die, they do not share a common memory space, and explicit transfers are made in and out of the APU memory.

At Forrester, we define customer experience as how customers perceive their interactions with your company.

Over the past few years, my colleagues and I have written a lot about the perceptions piece of that definition. Here’s a quick overview: Customers’ perceptions occur on three different levels, which we collectively refer to as the customer experience pyramid.  At the base of the pyramid is “meets needs.” Do customers perceive that you’ve met their basic needs and provided value through the interaction? Then we layer on “easy.” Do customers perceive that you’re easy to do business with, or that they have to jump through a bunch of hoops? At the top of the pyramid is “enjoyable.” Do customers perceive that you’re enjoyable to do business with – that you’re connecting with them on some personal, emotional level?

Now let’s talk about the interactions themselves. Customers interact with your company at all stages of the customer journey: discover, evaluate, buy, access, use, get support, leave, and re-engage. But it’s not enough to know that these interactions exist. If you want to shift your customers’ perceptions, you have to examine those interactions on a deeper level. Specifically, you need to look at the types of interactions customers have and the qualities that those interactions embody. And that’s where your business model and your brand come into play.

CEOs and other senior executives must identify ways to improve their enterprise performance by boosting profitability, raising market share, and leapfrogging competitors. But achieving these objectives is not as simple as just looking at the numbers. What about nonfinancial measures (e.g., customer loyalty and employee satisfaction) that don’t show up in financial accounting? How do you quickly and efficiently get the full 360-degree view of your business?

In order to execute their business strategy, business and IT execs need a business-focused, strategic, and pragmatic way to measure their finances and operations — popularly referred to as business intelligence (BI). Without such measurements — supported by enterprisewide BI deployments — businesses can’t link operational results to strategy. Organizations will also find it difficult to get a coherent view of their internal and external processes, customers, logistics, operations, and finances.

However, most firms struggle with BI strategies and programs because turning data into information is an open-ended concept. They frequently go in the wrong direction because of traditional (and often outdated) views and approaches and a focus on technology instead of business, which results in BI programs that are tactical and only project-based. What these firms need is an approach to BI that, while staying true to the importance of long-term vision and looking across silos, provides the flexibility to accommodate varying levels of resource commitment and the political, historical, and cultural obstacles that BI programs often face. Think of Forrester’s new business intelligence playbook as your BI bible; it should guide your BI decisions every step of the way.

The recent Executive Order Making Open and Machine Readable the New Default for Government Information and the Office of Management and Budget (OMB) Memorandum Open Data Policy – Managing Data as an Asset have brought much attention to efforts to promote the use of data by the US Federal Government.  In fact, highlights of the US Federal Open Data Project are already impressive.  Many agencies already provide their data in machine-readable formats through APIs, or at least downloadable datasets.  However, I personally measure “highlights” in terms of the use of the data (not by the number of datasets accessible).  And, many organizations already put this data to good uses in health, energy, education, safety and finance.  My recent blog, Open Data Isn’t Just For Governments Anymore, highlighted several examples of companies built on open data.  Think Symcat, Healthgrades, oPower, or even Zillow which has been using public data for a while now.  How many of you have “zillowed” your house, your neighbor’s house or even a colleague’s house?  Be honest.  I have.