Huawei’s New Switch Looks Promising, and its Storyline Needs Reinforcing

Clement Teo

by Clement Teo, Bryan Wang, Katyayan Gupta

We recently met with Huawei executives during the launch of its latest product in China, the S12700 switch.  The product, which ships in limited quantity in Q1 2014 is designed for managing campus networks, and acts as a core and aggregation switch in the heart of campus networks. While wired/wireless convergence, policy control and management come as standard features, the draw is the Ethernet Network Processor (ENP). The ENP competes against merchant silicon in competitive switch products, and Huawei claims to be able to deliver new programmable services in six months, compared to one to three years for competitive application-specific integrated circuit (ASIC) chips. This helps IT managers respond quicker to the needs of campus network users, especially in the age of BYOD, Big Data, and cloud computing.

While it is a commendable product in its own right, Huawei will need to position its value more strategically against IT managers that have technology inertia, especially in ‘Cisco-heavy’ networks:

  • Tying the value of the switch to existing and future enterprise campus needs. In the age of cloud computing, big data, mobility, and social networking, IT managers need to solve network challenges like insufficient service processing capability and slow service responses. Huawei says the new switch is able to provide agile services and respond flexibly to changes in service requirements, on demand. For example, the switch has access control built in for wired/wireless access management. This is a good start. Enterprises will need to understand how the switch plays a central role in a campus network, and Huawei should continue to reinforce its agile network architecture’s storyline.
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Will Privacy Concerns Stop Or Stunt The Power Of Predictive Analytics

Mike Gualtieri

The power of predictive analytics in the age of Big Data is super-cool, but will privacy concerns stop or stunt it's adoption? Watch this episode of Forrester TechnoPolitics with Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Lie, Buy, or Die to find out. 

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Moneyball, Big Data, And The Data Scientist

Mike Gualtieri

Ari Kaplan is a real moneyball guy. As President of Ariball, he has worked with more than half of all the MLB organizations to evaluate players for maximum return on the baseball club's investment. But, Ari is much more than just a moneyball guy, he is also a computer scientist, a data scientist, and has the business acumen to produce dramatic results for the teams he works with. He is the real deal. Forrester TechnoPolitics caught up with Ari at Predictive Analytics World in Chicago to ask him how Big Data and the role of the data scientist will advance the science of moneyball. 

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Maximize Your Chances Of Business Intelligence Success

Martha Bennett

Too little data, too much data, inaccessible data, reports and dashboard that take too long to produce and often aren’t fit for purpose, analytics tools that can only be used by a handful of trained specialists – the list of complaints about business intelligence (BI) delivery is long, and IT is often seen as part of the problem. At the same time, BI has been a top implementation priority for organizations for a number of years now, as firms clearly recognize the value of data and analytics when it comes to improving decisions and outcomes.

So what can you do to make sure that your BI initiative doesn't end up on the scrap heap of failed projects? Seeking answers to this question isn't unique to BI projects — but there is an added sense of urgency in the BI context, given that BI-related endeavors are typically difficult to get off the ground, and there are horror stories aplenty of big-ticket BI investments that haven’t yielded the desired benefit.

In a recent research project, we set out to discover what sets apart successful BI projects from those that struggle. The best practices we identified may seem obvious, but they are what differentiates those whose BI projects fail to meet business needs (or fail altogether) from those whose projects are successful. Overall, it’s about finding the right balance between business and IT when it comes to responsibilities and tasks – neither party can go it alone. The six key best practices are:

·         Put the business into business intelligence.

·         Be agile, and aim to deliver self-service.

·         Establish a solid foundation for your data as well your BI initiative.

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Enable business strategy through technology innovation

Charlie Dai

How is it possible for a local company to defeat global giants like Pepsi, Coca-Cola, and Watsons in your market segment and establish market leadership for more than a decade? The answer is given by Nongfu Spring, a Chinese company in manufacturing and retail industries. In my recent report “Case Study: Technology Innovation Enables Nongfu Spring To Strengthen Market Leadership”, I analyzed the key factors behind their success, and provide related best practice from enterprise architecture perspective. These factors include

  • Business strategy is enterprise architecture's top priority.  EA pros often need to be involved in project-level IT activities to resolve issues and help IT teams put out fires. But it's much more important that architects have a vision, clearly understand the business strategy, and thoroughly consider the appropriate road map that will support it in order to be able to address the root causes of challenges.
  • Agile infrastructure sets up the foundation for scalable business growth. Infrastructure scalability is the basis of business scalability. Infrastructure experts should consider not only the agility that virtualization and IaaS solutions will provide next-generation infrastructure, but also network-level load balancing among multiple telecom carriers. They should also refine the network topology for enterprise security.
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Intel Lays Out Future Data Center Strategy - Serious Focus on Emerging Opportunities

Richard Fichera

Yesterday Intel had a major press and analyst event in San Francisco to talk about their vision for the future of the data center, anchored on what has become in many eyes the virtuous cycle of future infrastructure demand – mobile devices and “the Internet of things” driving cloud resource consumption, which in turn spews out big data which spawns storage and the requirement for yet more computing to analyze it. As usual with these kinds of events from Intel, it was long on serious vision, and strong on strategic positioning but a bit parsimonious on actual future product information with a couple of interesting exceptions.

Content and Core Topics:

No major surprises on the underlying demand-side drivers. The the proliferation of mobile device, the impending Internet of Things and the mountains of big data that they generate will combine to continue to increase demand for cloud-resident infrastructure, particularly servers and storage, both of which present Intel with an opportunity to sell semiconductors. Needless to say, Intel laced their presentations with frequent reminders about who was the king of semiconductor manufacturingJ

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How to estimate cost of BI deployment

Boris Evelson

Initial business intelligence (BI) ployment efforts are often difficult to predict and may dwarf the investment you made in BI platform software. The effort and costs associated with professional services, whether you use internal staff or hire contractors, depend not only on the complexity of business requirements like metrics, measures, reports, dashboards, and alerts, but also on the number of data sources you are integrating, the complexity of your data integration processes, and logical and physical data modeling. At the very least Forrester recommends considering the following components and their complexity to estimate development, system integration and deployment effort:

  • Top down business requirements such number of 
    • Goals and objectives
    • Metrics, Measures
    • Attributes and dimensions
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Amdocs Gains Momentum As A Leading Customer Experience Provider

Dan Bieler

Dan Bieler, Katyayan Gupta, Clement Teo

We recently attended Amdocs' customer event in Singapore. Amdocs is gradually adjusting its strategy to reflect one of the most fundamental changes in the ICT industry today: Increasingly, business line managers, think the marketing or sales officer, are the ones influencing sourcing decisions. Traditional decision-makers, CTOs and CIOs, are no longer the sole ICT decision-makers. Amdocs is addressing this shift by:

  • Strengthening its customer experience portfolio.Successful telcos will try to regain lost relevance through improved customer experience. Marketing, portfolio product development, and sales are therefore growing in importance for telcos. Amdocs’ integrated customer experience offering, CES 9, provides telcos with a multichannel experience; proactive care; and self-service tools.
  • Betting big on big data/analytics.Amdocs is leveraging big data/analytics to provide real-time, predictive, and prescriptive insights to telcos about their customers’ behaviour. Communications-industry-specific converged charging and billing solutions as well as other catalogue solutions give Amdocs the opportunity to provide more value to telcos than some of the other players.
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Google Enterprise Roadshow 2013: Shooting For The Moon

Dan Bieler

I attended Google’s annual atmosphere road show recently, an event aimed at presenting solutions for business customers. The main points I took away were:

  • Google’s “mosaic” approach to portfolio development offers tremendous potential. Google has comprehensive offerings covering communications and collaboration solutions (Gmail, Google Plus), contextualized services (Maps, Compute Engine), application development (App Engine), discovery and archiving (Search, Vault), and access tools to information and entertainment (Nexus range, Chromebook/Chromebox).
  • Google’s approach to innovation sets an industry benchmark. Google is going for 10x innovation, rather than the typical industry approach of pursuing 10% incremental improvements. Compared with its peers, this “moonshot” approach is unorthodox. However, moonshot innovation constitutes a cornerstone of Google’s competitive advantage. It requires Google’s team to think outside established norms. One part of its innovation drive encourages staff to spend 20% of their work time outside their day-to-day tasks. Google is a rare species of company in that it does not see failure if experiments don’t work out. Google cuts the losses, looks at the lessons learned — and employees move on to new projects.
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How The Obama Campaign Used Predictive Analytics To Influence Voters

Mike Gualtieri

The Obama 2012 campaign famously used big data predictive analytics to influence individual voters. They hired more than 50 analytics experts, including data scientists, to predict which voters will be positively persuaded by political campaign contact such as a call, door knock, flyer, or TV ad. Uplift modeling (aka persuasion modeling) is one of the hottest forms of predictive analytics, for obvious reasons — most organizations wish to persuade people to to do something such as buy! In this special episode of Forrester TechnoPolitics, Mike interviews Eric Siegel, Ph.D., author of Predictive Analytics, to find out: 1) What exactly is uplift modeling? and 2) How did the Obama 2012 campaign use it to persuade voters? (< 4 minutes)

 

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