Take a forward thinking, while pragmatic approach to Windows migration

Charlie Dai

Many CIOs, technical architects as infrastructure and operations (I&O) professionals in Chinese companies are struggling with the pressures of all kinds of business and IT initiatives as well as daily maintenance of system applications. At the same time they are trying to figure out what should be right approach for the company to adapt technology waves like cloud, enterprise mobility, etc., to survive in highly competitive market landscape.   Among all the puzzles for the solution of strategic growth, Operating System (OS) migration might seem to have the lowest priority:  business application enhancements deliver explicit business value, but it’s hard to justify changing operating systems when they work today. OS is the most fundamental infrastructure software that all other systems depend on, so the complexity and uncertainty of migrations is daunting. As a result, IT organizations in China usually tend to live with the existing OS as much as possible.

Take Microsoft Windows for example. Windows XP and Windows Server 2003 have been widely used on client side and server side.  Very few companies have put Windows migration on its IT evolution roadmap. However, I believe the time is now for IT professionals in Chinese companies to seriously consider putting Windows upgrade into IT road map for the next 6 months for a couple of key reasons. 

Windows XP and pirated OS won’t be viable much longer to support your business.

  • Ending support. Extended support, which includes security patches, ends April 8, 2014. Beyond that point, we could expect that more malwares or security attacks toward Windows XP would occur.
Read more

Don't Have A Big Data Strategy Yet? Good.

Brian  Hopkins

Big data noise has reached the point where most are reaching for the ear plugs. And with any good hype bubble, the naysayers are now grabbing attention with contrarian positions. For example, The New York Times expressed doubt about the economic viability of big data in "Is Big Data an Economic Big Dud?" This post grabbed a lot of attention, but, like many others I read, it fundamentally misses the point of what big data is all about and why it's important. The article compares the productivity boom associated with the first wave of the Internet to the lack of growth experienced since the inception of "big data"; it implies that big data’s expected economic impact may not happen. Furthermore, the article implies that big data is something that firms will do or implement. Thinking about big data this way or differentiating between data sets as big, medium, or small is dangerous. It leads to chasing rabbits down holes.

Read more

Big Data Governance Protect And Serve Are Equals

Michele Goetz

I had the opportunity to speak and participate in a panel on data governance as it pertained to big data. My presentation was based on recently completed research sponsored by IBM to understand, what does data governance look like by firms embarking/executing on big data? The overarching theme was that data governance is about protect and serve. Manage security and privacy while delivering trusted data.

Yet, when you look at data governance and what it means to the data practice, not the technology, protect and serve is also a credo. In business terms it represents:

  • Protect the reputation and mitigate risk associated with inappropriate use or dirty data.
  • Serve information needs of the business to have information fast and stay agile to market conditions.
Read more

Make Business Agility A Key Corporate Attribute – It Could Be What Saves You

Craig Le Clair

There was a time when economies of scale swamped all other corporate attributes – and a time of stable competitive advantage – where sticking to a single core competency was sufficient. Big companies dominated. Sure, they were slow to react to market change, but they had huge cost advantages and could lock down distribution channels, suppliers, and other sources of strength.

But that is last decade’s thinking. Seventy percent of the companies that were on the Fortune 1000 list a mere 10 years ago have now vanished – unable to adapt to change. In those 10 years we’ve seen digital disruption change the business landscape. We’ve watched the Internet become pervasive, embraced cloud-based applications that update multiple times a year, acquired mobile devices that connect everywhere in the neighborhood and around the globe, and embraced information workers who use their own tools to do corporate work on their own time.

Today, companies must break away from the assumption of sustainable competitive advantage and embrace adaptable differentiation, i.e., develop an agility advantage. But what does this mean? Forrester defines business agility as the quality that allows an enterprise to embrace market and operational changes as a matter of routine.

Read more

Introducing A New Peer Network For EA Executives

Sharyn Leaver

We’ve been talking to many of you in the last year about improving our Forrester Leadership Boards for Enterprise Architecture Professionals -- our peer collaboration program for senior executives.

In our research, we found there was a clear distinction between the executive audience: the enterprise architects and the leaders of strategy, planning and innovation for their IT organizations.

As such, in addition to our existing Enterprise Architecture Council, we have just launched our Business Technology Strategy Council to better serve our executives in this role! In order to distinguish between these groups, below are some examples of some of the member challenges you’ll find in each of these groups.

Business Technology Strategy Council:

  • Establish strategies with quantifiable business impact.

  • Drive innovation and embracing emerging technologies.

  • Mobilize executives, peers, and customers around your BT strategy.

  • Optimize the IT portfolio for top performance.

Read more

Data Quality And Data Science Are Not Polar Opposites

Michele Goetz

Big data gurus have said that data quality isn’t important for big data. Good enough is good enough. However, business stakeholders still complain about poor data quality. In fact, when Forrester surveyed customer intelligence professionals, the ability to integrate data and manage data quality are the top two factors holding customer intelligence back.

So, do big data gurus have it wrong? Sort of . . .

I had the chance to attend and present at a marketing event put on by MITX last week in Boston that focused on data science for marketing and customer experience. I recommend all data and big data professionals do this. Here is why. How marketers and agencies talk about big data and data science is different than how IT talks about it. This isn’t just a language barrier, it’s a philosophy barrier. Let’s look at this closer:

  • Data is totals. When IT talks about data, it’s talking of the physical elements stored in systems. When marketing talks about data, it’s referring to the totals and calculation outputs from analysis.
  • Quality is completeness. At the MITX event, Panera Bread was asked, how do they understand customers that pay cash? This lack of data didn’t hinder analysis. Panera looked at customers in their loyalty program and promotions that paid cash to make assumptions about this segment and their behavior. Analytics was the data quality tool that completed the customer picture.
  • Data rules are algorithms. When rules are applied to data, these are more aligned to segmentation and status that would be input into personalized customer interaction. Data rules are not about transformation to marketers.
Read more

Kofax Acquires Kapow — Targets Content Transformation Market

Craig Le Clair

Kofax continues its acquisition rampage with a cash purchase of Kapow. I came across Kofax a few years ago while doing the research for "Take A Process View Of Content Integration." Apparently Kofax has taken the "process view." The idea behind that piece was that enterprises had so many diverse content stores that they needed to view conversion and migration of unstructured content as an internal competency.

But while content integration can reduce infrastructure costs and license fees, the real value is from improving business processes by linking content to business process management (BPM) and dynamic case management systems to reduce cycle time and improve compliance, customer support, and decision-making. These projects can be complex, difficult, and challenging, but Kofax correctly sees this as a large opportunity. I do as well.

Another Kapow capability is to scrape websites and create consolidated views. For example, customer service reps often switch between apps in a clumsy and inefficient manner while the customer is on hold. In some cases, ECI software should grab the needed content behind the scenes and present it in a unified way. Kapow Technologies' content integration solution works like a robot to extract, transform, and load content from Web-based apps to consolidated views. I interviewed one large telecommunications company that used Kapow's robot for customer service business processes to eliminate task switching and repetitive tasks. According to the company:

Read more

Data Quality Blooms With Crowdflower

Michele Goetz

Sometimes getting the data quality right is just hard, if not impossible. Even after implementing data quality tools, acquiring third-party data feeds, and implementing data steward remediation processes, often the business is still not satisfied with the quality of the data. Data is still missing and considered old or irrelevant. For example: Insurance companies want access to construction data to improve catastrophe modeling. Food chains need to incorporate drop-off bays and instructions for outlets in shopping malls and plazas to get food supplies to the prep tables. Global companies need to validate address information in developing countries that have incomplete or fast-changing postal directories for logistics. What it takes to complete the data and improve it has now entered the realm of hands-on processes.

Crowdflower says they have the answer to the data challenges listed above. It has a model of combining a crowdsourcing model and data stewardship platform to manage the last mile in data quality. The crowd is a vast network of people around the globe that are notified of data quality tasks through a data stewardship platform. If they can help with the data quality need within the time period requester, the contributor accepts the task and get to work. The crowd can use all resources and channels available to them to complete tasks such as web searches, visits, and phone inquiries. Quality control is performed to validate crowdsourced data and improvements. If an organization has more data quality tasks, machine learning is applied to analyze and optimize crowd sourcing based on the scores and results of contributors.

Read more

Business Design Patterns: Myth Or Reality?

Gordon Barnett

The other day, I had one of those eureka-like moments. As I lay in the bath, my thoughts shifted back and forth between the past and the present, recognizing how advances (or the lack of advances) in technology have affected our lives. When thinking about the past, I remember the days of my communication engineering apprenticeship; this was in the days of electro-mechanical exchanges. Some of you may remember or may have seen, in an old film, a telephone operator connecting two phone lines by placing a connecting cord between two phone line jacks. This was the world of telecommunication exchanges in the 1970s — no fancy computing technology existed in telecommunications at the time. In was certainly not a trivial exercise in upgrading capacity, maintaining the exchange, or connecting to another exchange. When thinking about the present, I marvel at the continuing improvements in plug-and-play hardware and software technology. As an example, I buy a new camera and, hey presto! I now have the ability to edit and post pictures on forums or cloud applications, to send them by email, or to store them on third-party storage from my camera.

So back to my eureka-like moment. I’m thinking that, surely, all these present-day technology advances have been enabled because of standards, design patterns, and common interfaces. My mind keeps focusing on design patterns, and the question arises: "Is there such a thing as business design patterns?" I have done some initial research, and I am yet to find evidence of the term or concept of business design patterns. However, I do have my suspicions they exist because:

Read more

Data Science And "Closed-Loop" Analytics Changes Master Data Strategy

Michele Goetz
I had a conversation recently with Brian Lent, founder, chairman, and CTO of Medio. If you don’t know Brian, he has worked with companies such as Google and Amazon to build and hone their algorithms and is currently taking predictive analytics to mobile engagement. The perspective he brings as a data scientist not only has ramifications for big data analytics, but drastically shifts the paradigm for how we architect our master data and ensure quality.
 
We discussed big data analytics in the context of behavior and engagement. Think shopping carts and search. At the core, analytics is about the “closed loop.” It is, as Brian says, a rinse and repeat cycle. You gain insight for relevant engagement with a customer, you engage, then you take the results of that engagement and put them back into the analysis.
 
Sounds simple, but think about what that means for data management. Brian provided two principles:
  • Context is more important than source.
  • You need to know the customer.
Read more