Techaisle Blog

Techaisle’s SMB technology adoption study shows that 72 percent of SMBs find Virtualization to be one of the most relevant technologies for their business, 2^nd only to backup and disaster recovery. The actual adoption gets hindered because 56 percent of SMBs find Virtualization to also be one the most complex technologies to understand and adopt. (See infographic)

SMBs cite several reasons for adopting server virtualization; key among them are reducing operating cost, backup and disaster recovery and reducing cost of IT support. Improving existing server and hardware systems utilization is mentioned by 32 percent of SMBs.

In our survey of SMBs either currently using or planning to use Virtualization technologies we found that SMBs currently using Virtualization tended to have a mixed brand Virtualization environment, not relying on a single vendor for the solution, but mixing and matching as they saw appropriate based on their specific requirements.

For example, the above chart shows that within VMware Server Virtualization environments, 66 percent of SMBs also use VMware client Virtualization technology, with both Microsoft and Citrix making up the difference for the client side. Similarly, 78 percent of SMBs that use Microsoft server Virtualization also use Microsoft client Virtualization. Several other findings become apparent from the above chart:

• VMware and Citrix have the most relatively mixed virtualization environment as compared to Microsoft


• Citrix and Microsoft may have a slightly deeper partnership that enables SMBs using Citrix server Virtualization to be combined with Microsoft client Virtualization more easily and cost effectively

However, we cannot look at the above chart in isolation. SMBs have been using Virtualization technologies as the market developed.

In the words of one VP of IT for a mid-market business, “We use Citrix, VMware, Microsoft Hyper-V, and emulation from Ericom. There are ‘n’ numbers of products that are being used in the whole gamut of things”.

The Venn diagram below not only exposes the vulnerabilities faced by Virtualization vendors but also demonstrates that the market is big enough for solutions from all vendors to work in a heterogeneous IT environment.

For example, the above Venn diagram shows that only 12 percent of SMBs use only VMware Virtualization solution which is twice that of Citrix and almost one-fourth of Microsoft. And 9 percent of SMBs use Virtualization solutions from VMware, Citrix and Microsoft. Once we start to include solutions from Parallels, NComputing, Oracle and others the overlaps become very complicated to map.

Our research found that SMBs usually go through a round of server consolidation before moving to Virtualization.

“The very first step was actually to go for server consolidation. Once the servers were consolidated, then the desktop virtualization was performed. So, typically for VDI architecture or any other technology, the first thing is the server consolidation and after that the procurement of solution and licenses were done from VMware and Citrix for the VDI and after which the user terminals were changed”, this according to one IT Director, Mid-market business.

Not all Virtualization projects finish smoothly. SMBs have also had different experiences with each of the three major brands for server Virtualization projects as shown in the chart below:

The factors affecting each of the projects could be dependent upon:

• SMBs’ readiness


• Channel partners’ capabilities

However, the top 3 most common areas that need addressing are Compatibility Issues, Cost Overruns and Lack of Experience, which are perennial issues as all SMB users adopt new technologies.

“The major challenge was the cost, because the initial hardware investment was huge. Getting rid of the system and moving to the cloud and installing virtual servers required purchasing of physical storage and upgrading the system. Another challenge that we faced was the initial configuration which was addressed timely and efficiently by our partners”, Vice President, IT (500 employee size company).

But SMBs have gained tremendous advantages from using Virtualization. “It certainly has helped us to avail richer network services without increasing our capital investment and has increased our operational efficiency. Moreover computing and networking are much simplified now”.

For additional information on this and other topics from the blog, please feel free to contact us for a discussion and gratis consultation.

To purchase Techaisle’s SMB Virtualization Trends and Adoption study or engage Techaisle in a deep-dive custom research please send an email to This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Dusting off my notebooks (the notepad variety) I came upon some carefully documented notes of my conversations with Dell’s Channel team, in particular with Greg Davis, Vice President and General Manager of Global Commercial Channels.  Just reviewing the notes of the previous two years it hit me squarely in my face that Dell’s channels team has been on a restless pursuit of:

• Simplicity,

 

• Training & enablement,

 

• Winning datacenter together with the channel, and

 

• Partner profitability

Fall of 2011

Although Dell’s Partner Direct program was formally launched in 2007 with aggressive channel recruitment and courting happening in 2008, we will pick up on our conversations with Dell’s Greg Davis and Paul Shaffer, Executive Director Global Channel Marketing & Demand Generation from the fall of 2011. Partner enablement, training, certification and integration of acquisitions had percolated to the top of the team's agenda. For an IT company which is notorious in selling direct, drastic measures were needed to become “one” with the channel. Dell delivered 75,000 training modules to its partners, 30 percent of Dell’s commercial business had started to come from channels and 58,000 registration deals were closed. With the acquisition of Force10 Networks Dell announced enhanced network certification programs and 130 premier partners got their certifications. Emphasizing that the training modules were working, Greg Davis had mentioned that top 10 partners who invested most in training had seen 110 percent growth in revenue. Fall 2011 was also the time when partners started seeing the first glimpse of gentle motivations from Dell to push deeper into healthcare segment and drive revenue from datacenter solutions. Inroads were being made into smaller partners for SMBs as much as national and larger partners.

Cloud Channel

During the same time period while Dell was building out its confidence and trust with the channels, enterprises and SMBs were moving to cloud, thus dis-intermediating the channel. Especially the VAR channels (which typically form the largest percent of channel partners of an IT Vendor) had been finding their traditional business models threatened by products and services that could be sold direct by a vendor over the Internet. To continue to adapt to the changing times and never taking its eye off the channel partners’ livelihood Dell launched cloud channel programs in the spring of 2012:

• Cloud Builder,

 

• Cloud Provider, &

 

• Cloud Service Enabler

A technical services team was also put into place to help partners sell data center solutions namely, server and storage. Dell now had roughly 250 premium partners and had delivered 135,000 training modules in the year.

Work was far from complete. More acquisitions were taking place; these acquisitions had to be integrated and above all emerging market countries had to be targeted. Both Greg Davis and Amit Midha, President, Asia Pacific and Japan, Chairman, Global Emerging Markets underscored the fact that they were working to ensure a consistent channel engagement across every market covering:

• Deal registration

 

• Compensation neutrality

 

• Conflict escalation process, and

 

• Executive priority

Asia/Pacific

The channel commitment work in Asia/Pacific countries in our opinion is far from complete. There are still some major strides to be made, specifically in the Asia/Pacific region. By its own acknowledgement, Asia/Pacific is the fastest growing regions for Dell which requires a constant confidence and trust building process with the channels. In many of Techaisle’s analyst interactions with channel partners in 2012 in Asia/Pacific, it was found that channels had warmed up to Dell but some questioned Dell’s sincerity whenever bigger contracts were involved.

In both summer and fall of 2012 we asked Greg Davis and Amit Midha where they thought they were with consistency and confidence. Not only were they bullish but also recognized that they have some hills to climb. They were also candid that services remain a big component of any channel’s revenue mix and while typical services such as warranty, break-fix, and insurance were straightforward re-sale of Dell Services, partnering in consulting was a bit more challenging.

Summer 2012

By the summer of 2012, efforts were paying off, 62,000 deal registrations per quarter were coming through partners with 72 percent approval rate, 35,000 training modules were being delivered per quarter, the number of premier and preferred partners had jumped to 2500, Asia/Pacific channel programs were being strengthened, SonicWALL was integrated and specific courses were introduced on how to talk to a CIO, value of integrated datacenter. Above all social media training programs were launched for the benefit of the channels.

In late summer, in a conversation with Greg Davis and Bob Skelley, Executive Director, Global Certified Partner Program & Channel, they reiterated their commitment to make Dell “easy to work with” and restated their deep & maniacal focus on training and competencies. This focus resulted in 34 percent of global commercial business funneling through Dell channels, up from 30 percent in the fall of 2011. Number of deal registrations had jumped to 71,000 and an enhanced deal registration tool on mobile platforms was rolled-out. 47,000 training courses had been delivered in the quarter and Dell now had 113,000 channel partners. Initial focus on healthcare segment had resulted in a surge in end-user customers. A 40 percent growth in certifications was also achieved when compared with previous quarter. With the integration of Wyse, a desktop virtualization certification program was introduced. Dell channels had truly arrived and there was never a question of ever turning back.

One year later, Fall 2012

One year later, by fall of 2012, Dell had 130,000 channel partners, 35 percent of commercial business revenue was funneling through channels, 142,000 training courses had been delivered in the year, number of deal registrations had shot up to 65,000 and there were now 3600 preferred and premier channel partners. In the words of Greg Davis, “Dell has the most confident and competent channel partners in the world”. One year later, I saw an urgency to deliver with a profound focus on datacenters, systems management and cloud services. Virtualization was also beginning to take center stage. Kathy Schneider, Executive Director, Global Channel Marketing & Programs, drove home the point that she and her team were focused on driving best practices across four strategic pillars:

1. Easy to do business with One Price and Sales Tools

 

1. Win in the Enterprise using a comprehensive sales tool aptly named as Enterprise Master

 

1. Training & enablement through expansion of training beyond Dell’s standard solutions to include social media

 

1. Partner profitability through a simple, effective and rewarding incentives program

It has been a long way from direct PC selling to indirect solution selling. Real progress has been made. Dell’s channel executives are an end-to-end solutions empowering team for the channels. Not all channels will thrive but those that are equally committed to learn, adapt and practice will certainly succeed.

Anurag Agrawal
With contribution from Gitika Bajaj in Asia/Pacific

 

Many organizations are starting to think about “analytics-as-a-service” (no acronym allowed) as they struggle to cope with the problem of analyzing massive amounts of data to find patterns, extract signals from background noise and make predictions. In our discussions with CIOs and others, we are increasingly talking about leveraging the private or public cloud computing to build an analytics-as-a-service model.

The strategic goal is to harness data to drive insights and better decisions faster than competition as a core competency.  Executing this goal requires developing state-of-the-art capabilities around three facets:  algorithms, platform building blocks, and infrastructure.

Analytics is moving out of the IT function and into business — marketing, research and development, into strategy.  As a result of this shift, the focus is greater on speed-to-insight than on common or low-cost platforms.   In most IT organizations it takes anywhere from 6 weeks to 6 months to procure and configure servers.  Then another several months to load configure and test software. Not very fast for a business user who needs to churn data and test hypothesis. Hence cloud-as-a-analytics alternative is gaining traction with business users.

The “analytics-as-a-service” operating model that businesses are thinking about is already being facilitated by Amazon, Opera Solutions, eBay and others like LiquidHub.  They are anticipating the value migrating from traditional outmoded BI to an Analytics-as-a-service model.  We believe that Amazon’s analytics-as-a-service model provides a directional and aspirational target for IT organizations who want to build an on-premise equivalent.

 

Situation/Problem Summary: The Challenges of Departmental or Functional Analytics

The dominant design of analytics today is static or dependent on specific questions or dimensions. With the need for predictive analytics-driven business insights growing at ever increasing speeds, it’s clear that current departmental stove-pipe implementations are unable to meet the demands of increasingly complex KPIs, metrics and dashboards that will define the coming generation of Enterprise Performance Management. The fact that this capability will also be available to SMBs follows the trend of embedded BI and dashboards that is already sweeping the market as an integral part of SaaS applications. As we have written in the past, the move to true mobile BI can be provided as an application "bolt-ons" that work in conjunction with an existing Enterprise Applications or as pure play developed from scratch BI applications that take advantage of new technologies like HTML5. Generally, the large companies do the former through acquisition with existing technology and integration and with start-ups for the latter. Whether at the Departmental or Enterprise level, the requirements to hold down costs, minimize complexity and increase access and usability are pretty much universal, especially for SMBs, who are quickly moving away from on-premise equipment, software and services.

After years of cost cutting, organizations are looking for top-line growth again and finding that with the proliferation of front-end analytics tools and back-end BI tools, platforms and data marts, the burden/overhead of managing, maintaining and developing the “raw data to insights” value chain is growing in cost and complexity - a balance that brings SaaS and on-premise benefits together is needed.

The perennial challenge of a good BI deployment remains: it is becoming increasingly necessary to bring the disparate platforms/tools/information into a more centralized but flexible analytical architecture. Add to this the growth in volume of Big Data across all company types and the challenges accelerate.

Centralization of analytics infrastructure conflicts with the business requirement of time-to-impact, high quality and rate of user adoption - time can be more important than money if the application is strategic.  Line of Business teams need usable, adaptable, and flexible and constantly changing insights to keep up with customers.  The front-line teams care about revenue, alignment with customers and sales opportunities. So how do you bridge the two worlds and deliver the ultimate flexibility with the lowest possible cost of ownership?

The solution is Analytics-as-a-Service.

 

Emerging Operating Model:  Analytics-as-a-Service

It’s clear that sophisticated firms are moving along a trajectory of consolidating their departmental platforms into general purpose analytical platforms (either inside or outside the firewall) and then packaging them into a shared services utility.

This model is about providing a cloud computing model for analytics to anyone within or even outside an organization.  Fundamental building blocks (or enablers) like – Information Security, Data Integrity, Data and Storage Management, iPad and Mobile capabilities and other aspects – which are critical, don’t have to be designed, developed, tested again and again. More complex enablers like Operations Research, Data Mining, Machine Learning, Statistical models are also thought of as services.

Enterprise architects are migrating to “analytics-as-a-service” because they want to address three core challenges – size, speed, type – in every organization:

• The vast amount of data that needs to be processed to produce accurate and actionable results

 

• The speed at which one needs to analyze data to produce results

 

• The type of data that one analyzes - structured versus unstructured

The real value of this service bureau model lies in achieving the economies of scale and scope…the more virtual analytical apps one deploys, the better the overall scalability and higher the cost savings. With growing data volumes and dozens of virtual analytical apps, chances are that more and more of them leverage processing at different times, usage patterns and frequencies, one of the main selling points of service pooling in the first place.

 

Amazon Analytics-as-a-Service in the Cloud

Amazon.com is becoming a market leader in supporting the analytics-as-a-service concept. They are attacking this as a cloud-enabled business model innovation opportunity than an incremental BI extension.  This is a great example of value migration from outmoded methods to new architectural patterns that are better able to satisfy business’ priorities.

Amazon is aiming at firms that deal with lots and lots of data and need elastic/flexible infrastructure.  This can be domain areas like Gene Sequencing, Clickstream analysis, Sensors, Instrumentation, Logs, Cyber-Security, Fraud, Geolocation, Oil Exploration modeling, HR/workforce analytics and others. The challenge is to harness data and derive insights without spending years building complex infrastructure.

Amazon is betting that traditional enterprise “hard-coded” BI infrastructure will be unable to handle the data volume growth, data structure flexibility and data dimensionality issues.  Also even if the IT organization wants to evolve from the status quo they are hamstrung with resource constraints, talent shortage and tight budgets. Predicting infrastructure needs for emerging (and yet-to-be-defined) analytics scenarios is not trivial.

Analytics-as-a-service that supports dynamic requirements requires some serious heavy lifting and complex infrastructure. Enter the AWS cloud.  The cloud offers some interesting value 1) on demand; 2) pay-as-you-go; 3) elastic; 4) programmable; 5) abstraction; and in many cases 6) better security.

The core differentiator for Amazon is parallel efficiency - the effectiveness of distributing large amounts of workload over pools and grids of servers coupled with techniques like MapReduce and Hadoop.

Amazon has analyzed the core requirements for general analytics-as-a-service infrastructure and is providing core building blocks that include 1) scalable persistent storage like Amazon Elastic Block Store; 2) scalable storage like Amazon S3; 3) elastic on-demand resources like Amazon Elastic Compute Cloud (Amazon EC2); and 4) tools like Amazon Elastic MapReduce.  It offers choice in the database images (Amazon RDS, Oracle, MySQL, etc.)

 

How does Amazon Analytics-in-the-Cloud work?

BestBuy had a clickstream analysis problem — 3.5 billion records, 71 million unique cookies, 1.7 million targeted ads required per day. How to make sense of this data? They used a partner to implement an analytic solution on Amazon Web Services and Elastic MapReduce. Solution was a 100 node cluster on demand; processing time was reduced from 2+ days to 8 hours.

Predictive exploration of data, separating “signals from noise” is the base use case. This manifests in different problem spaces like targeted advertising / clickstream analysis; data warehousing applications; bioinformatics; financial modeling; file processing; web indexing; data mining and BI.  Amazon analytics-as-a-service is perfect for compute intensive scenarios in financial services like Credit Ratings, Fraud Models, Portfolio analysis, and VaR calculations.

The ultimate goal for Amazon in Analytics-as-a-Service is to provide unconstrained tools for unconstrained growth. What is interesting is that an architecture of mixing commercial off-the-shelf packages with core Amazon services is also possible.

 

The Power of Amazon’s Analytics-as-a-Service

So what does the future hold?  The market in predictive analytics is shifting.  It is moving from “Data-at-Rest” to “Data-in-motion” Analytics.

The service infrastructure to do “data-in-motion” analytics is pretty complicated to setup and execute.  The complexity ranges from the core (e.g., analytics and query optimization), to the practical (e.g., horizontal scaling), to the mundane (e.g., backup and recovery).  Doing all these well while insulating the end-user is where Amazon.com will be most dominant.

 

Data in motion analytics

Data “in motion” analytics is the analysis of data before it has come to rest on a hard drive or other storage medium. Due to the vast amount of data being collected today, it is often not feasible to store the data first before analyzing it. In addition, even if you have the space to store the data first, additional time is required to store and then analyze. This time delay is often not acceptable in some use cases.

 

Data at rest analytics

Due to the vast amounts of data stored, technology is needed to sift through it, make sense of it, and draw conclusions from it. Much data is stored in relational or OLAP stores. But, more data today is not stored in a structured manner. With the explosive growth of unstructured data, technology is required to provide analytics on relational, non-relational, structured, and unstructured data sources.

Now Amazon AWS is not the only show in town attempting to provide analytics-as-a-service.  Competitors like Google BigQuery, a managed data analytics service in the cloud is aimed at analyzing big sets of data… one can run query analysis on big data sets — 5 to ten terabytes — and get a response back pretty quickly, in a matter of seconds, ten to twenty seconds. That’s pretty useful when you just want a standardized self-service machine learning service. How is BigQuery used? Claritic has built an application for game developers to gather real-time insights into gaming behavior. Another firm, Crystalloids, built an application to help a resort network “analyze customer reservations, optimize marketing and maximize revenue.” (THINKstrategies’ Cloud Analytics Summit in April, Ju-kay Kwek, product manager for Google’s cloud platform).

 

Bottom-line and Takeaways

Analytics is moving from the domain of departments to the enterprise level.   As the demand for analytics grows rapidly the CIOs and IT organizations are going to be under increasing pressure to deliver.  It will be especially interesting to watch how companies that have outsourced and offshored extensively (50+%) to Infosys, TCS, IBM,  Wipro, Cognizant, Accenture, HP, CapGemini and others will adapt and leverage their partners to deliver analytics innovation.

At the enterprise level a shared utility model is the right operating model.  But given the multiple BI projects already in progress and vendor stacks in place (sunk cost and effort); it is going to be extraordinarily difficult in most large corporations to rip-and-replace.  They will instead take a conservative and incremental integrate-and-enhance-what-we-have approach which will put them at a disadvantage. Users will increasingly complain that IT is not able to deliver what innovators like Amazon Web Services are providing.

Amazon’s analytics-as-a-service platform strategy shows exactly where the enterprise analytics marketplace is moving to or needs to go. But most IT groups are going to struggle to implement this trajectory without some strong leadership support, experimentation and program management. We expect this enterprise analytics transformation trend will take a decade to play out (innovation to maturity cycle).

Shirish Netke

Techaisle’s SMB Cloud Adoption survey shows that SMBs that are using Cloud applications are experiencing tremendous improvement in customer acquisition, retention and work satisfaction. In fact, 1 in 4 SMBs say that customer retention has improved, and nearly 1 in 3 says that customer acquisition has improved.

In general SMBs have experienced improved customer acquisition and retention after using cloud applications, however, SBs (1-99 employees) and MBs (100-999 employees) differ. Typically, SBs are more hard-pressed to acquire customers, a top business issue for them. With the adoption of cloud, 32 percent SBs say that they have seen improvement. MBs on the other hand, have better direct sales force for customer acquisition, but after equipping the sales force and marketing with cloud applications they have seen marked improvement in customer retention. Additionally, an important point to note is that 29 percent of SBs have reported improved group productivity and 34 percent improved employee satisfaction.

The survey also showed that B2C and B2B SMBs have had different experiences in customer acquisition and retention.  Specifically, B2B SMBs have reported nearly twice as high improved experiences as B2C SMBs. Many B2C SMBs are using social media platforms such as Facebook and twitter and marketing automation solutions to build a set of followers to improve their customer retention and acquisition. On the other hand, comparatively higher percentage of B2B SMBs are using LinkedIn, Twitter and specialized platforms such as Chatter, Yammer and GageIn to track news and conversations with their customersaction.

CRM has become the central application and the core around which other features and functionality are deployed as required by an SMB organization, department within an SMB or an individual user within the SMB. CRM is that core cloud business application. After the SMB CRM base has been built (or simultaneously), the order of implementation depends on the SMB’s focus but is likely to be business intelligence, marketing automation, Financials, HR/Payroll, customer service for service companies, ERP, fulfillment (SCM) and industry vertical applications.

There are four key areas of SMB cloud usage and deployment. Each has got many sub-sets of applications. These four areas are:

1. Infrastructure and Platforms (US$13.0 Billion SMB Opportunity by 2016)


2. Communications and Collaboration (US$7.9 Billion SMB Opportunity by 2016)


3. Business productivity & Applications (US$15.5 Billion SMB Opportunity by 2016)


4. Industry specific applications ((US$2.7 Billion SMB Opportunity by 2016)

While there are many niche vendors addressing each niche area, the complexity grows manifold as businesses move from one application to another, from one device to multiple devices. As Cloud computing adoption among SMBs grows, the real issue of data integration continues to come into play and it will become imperative for each of the four areas to communicate with the other. And once that “integration enlightenment” happens SMBs will witness even higher improvements in productivity, satisfaction, acquisition and retention.

Anurag Agrawal
Techaisle