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Best Practices Around Cloud Data Integration

A couple of weeks ago, I witnessed a very interesting webinar panel discussion around people’s experience around data integration in the cloud. The panel was moderated by Jeff Kaplan of THINKstrategies, an analyst firm and the panel members included Andrew Bartels of PSA Insurance & Financial Services, Tom Carlock of Dun & Bradstreet, Aaron Mieswinkel of Astadia, and Darren Cunningham of Informatica.
I found this collection of panelists especially interesting because they represented constituents from several ends of the data integration spectrum – PSA was a consumer of cloud data integration services, Dun & Bradstreet was a provider of data quality services, Astadia was a System Integrator with deep experience in cloud integration, and Informatica was an ISV that specialized in out of the box data integration and application integration software.
Amongst the other interesting predictions I learned about on the webinar:
  • IDC predicts that 80% of new software offerings will be available as cloud  services and that by 2014, over one-third of software purchases will be via the cloud
  • More than 85% of Fortune 500 organizations will fail to effectively exploit big data for competitive advantage through 2015
  • 33% executives in a survey conducted by Saugatuck Technology identified integration as their top concern regarding SaaS deployment and use
  • On the SaaS vendor side, a THINKstrategies survey found that 88% of SaaS companies identify integration as important in winning new customers and a common sales hurdle.
These predictions seem quite telling about the main obstacle towards moving to the cloud – data integration. Many CIOs are still hesitant towards moving to the cloud because they believe that doing so would result in a massive strain on IT resources when it came to data integration, that the cost savings for moving to the cloud itself would be shot.
In the first wave of cloud computing adoption, businesses merely used companies such as Amazon Web Services for testing and development of their applications. CRM, and email then followed in the second and most recent wave of cloud adoption. However, when it comes to the third wave of cloud adoption, of moving mission critical systems such as ERP or proprietary financial applications to the cloud, there is still resistance to the idea because of the complex nature of these deployments.
This is where cloud data integration plays a huge part in taking away the complexity of integrating these various on-premise and cloud applications. When you have your systems on separate islands, it is extremely hard to benefit from the real-time BI that cloud computing can offer. Cloud data integration that can be done without the aid of IT personnel, while still following the procedures and rules around IT governance is the solution towards achieving real-time insight into your company’s operations. 
Cloud data integration also serves as a first step towards eventually achieving MDM nirvana – that state where your data workflows, procedures, and processes move seamlessly from system to system, with all personnel having insights into data that’s relevant to them. Cloud data integration can also serve as a catalyst
for cloud analytics, data-as-a-service (a.k.a. DaaS), and PaaS. 
Accelerating PaaS adoption is an area I’m especially interested in, given some work I’ve done in this space with past companies. A lot of application development in the cloud still takes place on two ends of the spectrum – on one end, there are developers who use a cloud application (such as and need certain customizations built into it – they then use to perform those customizations. In other instances, you have indie developers who use Windows Azure, or VMware’s Cloud Foundry to develop “nice to have” applications from scratch. But by and large, mission critical application development still occurs on-premise. 
I strongly believe that cloud data integration that can be performed by non-technical business users, will leave more time for IT personnel to develop mission critical applications in the cloud. With the PaaS space gaining more importance, and consolidating further, the time is ripe to take PaaS development to the next level. This in-turn will lead to cloud data integration morphing into “iPaaS” (or Integration Platform-as-a-Service). This interdependence between iPaas and PaaS will eventually lead to true SOA where we have open systems working hand-in-hand with each other, all in the cloud.
You can view the entire webinar below, and also check out Hollis Tibbetts blog on cloud data integration

LinkedIn – the defacto SaaS recruitment software?

LinkedIn has grown tremendously since its humble beginnings in 2003, when it started with about 4,500 members in its network. It now numbers over 80 million members and has launched a variety of premium services to monetize its technology, in the area of Advertising and Recruiting.

Of these, its Recruiting solutions (known as LinkedIn Talent Advantage) offers an interesting portfolio of capabilities that rival a multitude of job boards (such as Monster and Dice), as well as SaaS vendors such as Taleo. An insightful post by a seasoned recruiter shows that 1 in 20 (or 5% of) LinkedIn profiles are those of recruiters in the U.S. Assuming similar trends hold worldwide, LinkedIn has potentially 4 million recruiters worldwide to market its premium services to.

LinkedIn Talent Advantage currently consists of 5 modules:

1) LinkedIn Recruiter: Recruiters can search for talent by drilling down on location, industry, and keywords, and contact those that they’re interested in through mass InMails. Teams of recruiters from the same company can also manage the entire hiring workflow process and utilize collaboration tools to keep everyone on the same page.

2) Jobs Network: After listing the job posting on LinkedIn, recruiters can get a match of 50 strong candidates for the position. LinkedIn members can also see how they’re connected to the job poster as well as the number of people they’re connected to at the company.

3) Talent Direct: A slight variation of LinkedIn Recruiter, Talent Direct is different in that it allows a customized InMail to be sent to a highly qualified passive job seeker and appear higher and more prominent in the candidate’s Inbox.

4) Recruitment Advertising: Targeted banner-style ads on the main landing page to improve perception of employment brand.

5) Career Pages: A variation of the Recruitment Advertising module. Allows users to create a “Careers” tab to their main Company profile page and customize it to achieve their employment branding objectives.

LinkedIn has the potential to broaden its scope of recruitment offerings by offering the following enhancements to its current portfolio. A lot of these require LinkedIn to embed advanced analytics into its algorithms with dashboarding abilities.

Connections Added: Passive job seekers are generally looking to make a change when they’re starting to network with various professionals in their industry and add connections. A connection spike in the ‘Friend’, ‘Other’, or ‘Classmate’ categories vs. the ‘Colleague’ category would most likely point to a passive job seeker in the initial stages of his job search.

Groups Joined: Joining groups within one’s industry, starting group discussions, and answering questions is another way to network and make oneself known within the community.

Company & Department rating feature: Glassdoor already offers employees the ability to rate companies in terms of salary, their interview experiences, and their working experience there. Such a rating feature on LinkedIn (with a high level of data aggregation and anonymity in order to prevent employees from being singled out and being retaliated against) could help recruiters (as well as the company’s own HR department) gauge the willingness of a set of candidates to new opportunities. A ‘Department’ feature could allow employees to add themselves to a particular department within their company. The name of the department could be created virally, with subsequent profiles adding themselves to a department name that most closely matches what they type in when creating or updating their profile.

Company Departures: The ‘Company’ feature on LinkedIn lists departures whenever someone changes their job title and company. Active job seekers could be given the ability to tag themselves as a potential candidate for that vacancy, with the tag only viewable to recruiters of the departing company who have purchased a Talent Advantage subscription. This would give such recruiters an active pipeline of leads to follow up on before even creating the job description for the newly open requisition.

Following Companies: LinkedIn Users who follow companies generally do so because they’re interested in finding potential opportunities there. An “engagement meter” which records the number of blog entries, tweets, or company news that followers click on is yet another level of analysis that shows how interested a certain user is in the company.

Online Presence: High quality candidates are bombarded with InMails all the time. A presence feature that shows whether a certain user is currently online, or predicts based on usage patterns when this user would be online again can enable recruiters to send their InMail either instantly (if the user is online) or at a specified time (based on when the next predicted online availability is).

In summary, LinkedIn already has a great suite of products for recruiters to source quality candidates. By increasing the level of engagement with passive candidates, and mining some more nuggets of information about their LinkedIn habits (coupled with adequate privacy controls of course), LinkedIn definitely has the wherewithal to turn into the number one SaaS-style recruitment engine on the web today.

The ROI of the Cloud

With many pilot cloud projects gathering steam, organizations are evaluating transitioning their IT systems to a cloud-based architecture. However, such a full-scale move must take into account security risks, lock-in risks, and cost-benefit analysis over the lifetime of the investment. An InformationWeek Analytics report outlined a comprehensive survey of 393 individuals within various companies, 28% of whom had more than 10,000 employees. Amongst the many findings within the report, the most interesting ones were:

  • 34% of respondents involved in the cloud used it for SaaS (applications delivered via the cloud), 21% for IaaS (storage or virtual servers delivered via the cloud), 16% for PaaS (web platform delivered via the cloud), and 16% for DaaS (Data-as-a-Service for BI and other data lookup services delivered via the cloud)
  • 29% were not using the cloud at all
  • More than one-third claimed to build in 31% or more excess server and storage capacity for non-cloud computing systems
  • 73% cited “Integration with Enterprise applications” and 69% cited “Cost of Hardware and Software” as factors when choosing a business technology
  • Almost 92% exhibited some sort of likelihood to comprehensively carry out an extensive ROI analysis of the expected lifespan of a cloud computing project
  • 46% said that their ROI calculation would span 3-5 years
  • 45% stated that “elasticity” is frequently or often required

There was also a feeling amongst respondents that cloud computing works for commodity applications but that complex integration requirements make costs skyrocket. The major sources of cost savings touted by cloud proponents involved three areas: efficiencies as a result of economies of scale, use of commodity gear and elasticity.

This last area of elasticity is worth exploring further, especially in light of the number of respondents claiming to require excess capacity for their non-cloud computing applications, and the assertion that complex integration requirements are increasing the costs in the cloud. Elasticity refers to the ability to scale up or scale down on storage resources on the fly. But the reality of elasticity “on-demand” is that most major software vendors don’t provide the ability to add CPUs without additional costs, and coding applications that appropriately scale up are difficult. Thus, given the above data about the necessity of elasticity and the large percentage of companies that conduct detailed cloud ROI analysis, it is evident that these two factors are correlated.

Increasing the ROI of a Cloud Deployment

In order for CIOs to see more of an ROI from deploying applications in the cloud, several things must happen:

  1. Data center automation software must reach a level of sophistication where they are able to automatically coordinate tasks between on-premise and cloud applications to optimize elasticity
  2. Software vendors must allow for special pricing for cloud providers so that these savings can be passed onto customers – a way to allow this is to ensure a multi-tenant architecture consisting of customers that use the same on-premise software as is used in the cloud-based edition by the respective provider
  3. Ensure that the web platform used for PaaS purposes by the customer is compatible with the SaaS applications that they subscribe to, in order to enable any custom widgets that may need to be written
  4. Massive improvements in the “converged fabric” architecture that brings together servers, storage, and networking, so that pools of additional capacity are easily available where elasticity is needed.

Factors Slowing Cloud Computing Adoption

During the recent recession, Cloud Computing was touted as a new model for IT to adopt, in order to cut operational costs and extract maximum efficiencies out of their software. 2010 was supposed to be the ‘Year of Cloud Computing’ yet adoption still remains slow.

A recent InformationWeek article referenced a study conducted by Avanade which showed that 91% of U.S. respondents understood the term Cloud Computing while only 61% of respondents from the rest of the world understood it. Even more surprising was the fact that over half of U.S. respondents claimed to be using a combination of internal IT systems and cloud services (in other words “hybrid clouds”), while those who didn’t adopt any form of cloud computing cited security and control as their primary reasons for not doing so.

The unusually large number of ‘cloud computing adopters’ leads one to believe that the respondents considered web-hosting,, and other SaaS-type offerings to be cloud computing as opposed to pure-play cloud providers such as Amazon EC2, Heroku, and Google AppEngine. This leads us to the first reason for slow cloud computing adoption:

Misunderstanding Cloud Computing

The definition of Cloud Computing has converged on three distinct layers, each of them mapped appropriately to the ‘old’ traditional datacenter model of hardware, OS, and application:

Infrastructure-as-a-Service (IaaS): This includes servers, storage, and networking hardware stored remotely and delivered on an as-needed basis in the form of CPU cycles or data. Amazon EC2 and GoGrid are prime examples of IaaS providers.

Platform-as-a-Service (PaaS): This consists of a complete platform upon which to build your custom applications. APIs, database development, storage, and testing are provided as well. Microsoft’s Azure and’s platforms are examples of early PaaS providers.

Software-as-a-Service (SaaS): This consists of applications delivered over the web and accessed through an internet browser.’s CRM modules, Gmail, and Workday are all examples of SaaS providers. However, as you’ll see below, there is a fine difference between a SaaS solution, and a SaaS cloud-computing solution.

While the above definitions provide a basic foundation for understanding what cloud computing is, they still do not enable decision makers to understand the myriad of complexities involved with pushing the ‘go’ button when it comes to migration, and deployment. I found this useful in-depth InfoWorld Cloud Computing Deep Dive report, which addresses all the ‘middleware’ components needed for a successful cloud computing migration, amongst other issues. One of InfoWorld’s main cloud computing bloggers, David Linthicum, wrote a book called Cloud Computing and SOA Convergence in Your Enterprise: A Step-by-Step Guide, which outlines the 11 categories of Cloud Computing. I’ve reproduced the image from the InfoWorld Report below:
Although the above topology adds more granularity to the various components of cloud computing, it is sometimes too all-encompassing. For instance, the Application-as-a-Service segment (a.k.a. SaaS) consists of any software delivered over the web. But to be a true cloud-computing solution, I believe that such SaaS solutions must be able to not only integrate well with on-premise software but also with other SaaS solutions that exist on some other platform.

Apart from understanding what cloud computing really means, the next biggest impediment towards adopting it is:

Security in the Cloud

The vast majority of enterprises who have taken to the cloud have done so in the area of non-critical business applications. However, to truly realize the full benefits of cloud computing, enterprises must be able to consume their mission-critical business applications in the cloud, and be able to transition seamlessly between their on-premise applications and the cloud. An old Gartner report almost two years ago, summarizes seven main security risks of cloud computing. The seven risks outlined were:
1) Privileged user access (what controls are in place over the administrators at the service provider who have access to your critical data)
2) Regulatory compliance (what kind of external audits and security certifications has the provider gone through)
3) Data location (what country is the data stored at and will privacy of customers’ data be guaranteed at this location)
4) Data segregation (data in a cloud datacenter is typically in a shared environment. What encryption schemes are there to ensure that private data is not delivered to another customer by mistake)
5) Recovery (what disaster recovery mechanisms are there for backup of data)
6) Long-term viability (what exit or continuation strategies are available in case of acquisition or bankruptcy of the provider)

The above list though, is not comprehensive. Moreover, current security solutions in the cloud are merely limited to security vendors that have SaaS extensions to their existing software. Security issues around protecting the platform in the cloud have not been addressed yet. A nightmarish security scenario would involve a hacker exploiting vulnerabilities in the or Azure platform, and the virus quickly spreading to any applications that are run off it. Such a virus could then quickly proliferate its way to all customers using these applications. If you thought any of the MyDoom viruses of 2004 caused havoc, a virus of this scale through a cloud computing platform would bring significant business disruption. PaaS vendors such as Microsoft and need to assure customers of in-built anti-virus mechanisms to protect applications that run on their platforms.

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