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New cloud analytics and BI services to use big data

New cloud analytics and business intelligence (BI) services can help businesses better manage big data and cloud applications, according to cloud expert Jeff Kaplan.

Gathering and analyzing business intelligence (BI) has never been easy, but today BI is complicated further by overwhelming data loads and the number of data entry and access points. New cloud analytics advances may offer BI relief and even profit-increasing predictability for enterprises, according to Jeff Kaplan, founder of consulting firm THINKstrategies. 

“These new cloud analytics applications can deliver functional capabilities that can be easily, quickly and economically deployed, actually producing tangible and measurable benefits far more rapidly than in the past,” said Kaplan, who was an executive analyst for IDC, Dataquest/Gartner and META Group before founding THINKstrategies.

In this interview, Kaplan offers advice and information about cloud analytics, SaaS, PaaS and cloud data services for CIOs, CXOs, CTOs, IT and business users.

How do cloud analytics offerings today stack up against the problems businesses need to solve?

Jeff Kaplan: The basic framework of the cloud is made up of three layers: infrastructure, software and platforms.

At the Infrastructure as a Service (IaaS) layer, the advancements of companies like Amazon, Rackspace, Google and others bring us an exponentially more economical data capture and storage capability. That is needed to handle the sheer growth of data that has overwhelmed many organizations, organizations that can't afford the luxury of rolling out huge server farms of their own. Now, [they can] pay-as-they-go for IaaS offerings. That comes into a level of maturity just in time to respond to the data capture and storage issue. 

Three interesting things are happening in the Software as a Service (SaaS) layer.

  • First, every good SaaS app today contains a dashboard that gives the users, especially a manager, some real-time statistics, activity records and even trending data about app utilization. A key step to dealing with “big data” is being sure the app is responding to whatever the functional requirement. That requirement can be customer relationship management (CRM), ERP or whatever.
  • Next comes what's happening behind that dashboard. Every good SaaS cloud vendor is capturing every keystroke to understand better the heuristics of a customer’s behavior. Some people get concerned about the privacy issues associated with that; but most of us also enjoy the benefits, which include continuous enhancements of that application.
  • The third front concerns the multi-tenant architecture of SaaS, where we now have the ability to aggregate data across a user community to produce benchmark statistics. That capability delivers a new level of analytic benefits to the customer. 

These aggregated data sources that the SaaS vendors are capturing can be converted into very meaningful benchmark statistics and KPIs.

Finally, on the Platform as a Service (PaaS) cloud, we now have the ability to plug in business intelligence (BI) or analytic capabilities as we develop new apps. This exciting new opportunity wasn't available before the emergence of the cloud. 

How do the application analytics use cases for SaaS and PaaS cloud services differ?

Kaplan: Most people are first exposed to cloud through a SaaS. SaaS whets their appetite for finally solving not only the big data challenge, but also more importantly, the age-old issue of BI and cloud analytics. First off, they want check out functionality that's embedded within the primary SaaS apps., for instance, has built in quite a bit of analytics; so much so that their dashboard is iconic, and many SaaS vendors have architected and designed similar dashboards. Users get comfortable with that and become, say, power users, who then want to be able to further exploit more of their data. Building new applications with greater analytic capabilities in a PaaS development environment could be one scenario for doing that.

The other scenario is when a vendor takes advantage of a third-party platform. This would be when the app exchange partners with’s, Microsoft Azure or Google AppEngine to build their applications or enhance their applications to have greater analytic functionality to be more competitive in their own SaaS or ISV sector.

Finally, some ISVs are trying to be cloud platform players, such as cloud analytic software vendors who want to position their analytic engines as a PaaS that can be embedded into other third-party apps.         

Where is the cloud data services market today? Microsoft has this marketplace where you can hook up things like weather data over a span of time. Is there an appetite for such cloud data services?

Kaplan: Data as a service is becoming a key driver of cloud, but some people view it with ambivalence. They wonder: Is it part of the problem of big data? Is it part of the solution of the cloud? In fact, it represents a little bit of both.  Fortunately, it’s weighted more on the solution side


We’re seeing more and more organizations that recognize that effectively analyzing their business needs and providing the data they require to make the right business decisions depends on a combination of internally generated data and externally available data. That's where those kinds of data sources you were just referring to come in. So there are companies addressing [either or both] sides; structured data from services with formal data server solutions, and/or unstructured data coming in from, say, Twitter and Facebook.

Alteryx, Salesforce, Jigsaw, Microsoft Marketplace, Dun & Bradstreet, are in that space. Reuters is doing financial stuff. There’s a company in our backyard [the Boston area] called Buildium, that’s got an interesting SaaS app that helps property managers manage their facilities. 

Have you seen organizations building their own data warehouses in the cloud?

Kaplan: The pattern matches what they tend to be doing on the business IaaS side. There are three clear alternatives: 

  • Hiding behind the four walls of your firewall and building out your own internal private cloud to emulate the best practices and characteristics of a public cloud environment.
  • Realizing you can do it yourself so far, so you acquire a private cloud service from a third-party service provider like Amazon or Rackspace.
  • Use a public cloud service. Look these maybe shared resources but know how to partition them. SaaS or other cloud providers will house that warehouse and provide additional tools you can plug and play. 

That's the kind of continuum that we're seeing.

Extra reporting by Barney Beal, news director.

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