Possibilities (and Possible Pitfalls) of Cloud-Based BI

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Just last week, I wrote about Shaklee's positive experiences with cloud-based business intelligence, shared by CIO Ken Harris during a recent TDWI webcast. Harris' key indicator of success: The company now has multiple users in multiple functional areas using multiple applications -- without being forced to do so. "That's how I know we've got a winner," said Harris. That might not sound like such a big deal, but it surely is given BI's poor track record.


Consider Forrester Research analyst Boris Evelson's discovery, after speaking to a few dozen business professionals, that not one of them relied on IT for their day-to-day information needs.That lack of interest in working with IT to solve their information issues -- which Evelson attributes to the high cost and inflexibility of standard BI solutions, slow deployment times and overall poor relationships with IT, among other factors -- has led business users to rely heavily on Excel spreadsheets. Though many users like Excel, Evelson says they want pre-built BI solutions that can better handle larger amounts of data and allow them to collaborate with co-workers.


For some companies, like Shaklee, that's cloud-based BI. In a TDWI interview, Datasource Consulting President Steve Dine recaps some of the benefits, several of which were also mentioned by Harris during the webcast: ability to easily scale computing resources, faster implementation times, lower upfront costs, ability to leverage the cloud for proofs of concept and upgrades, and ability to scale geographically.


Like Harris, Dine says cloud security can exceed security found on a company's premises. Harris made an interesting point that, because concerns are more top-of-mind when trusting data to an "outsider," CIOs are less likely to sweep security issues under the rug with cloud providers and assume "everything is OK" as is sometimes the case with internal security.


Dine says data transfer speeds can be an issue. While technologies such as Hadoop and MapReduce offer the ability to scale, they don't work well for all types of data. Other methods for transferring large data volumes can create additional overhead to daily processes -- one of the annoyances that cloud-based BI promises to eliminate. And more specifically:

When you're dynamically bringing up instances of virtualized servers, you don't know where those are located within a data center. You can put them within the same zone, but you can't necessarily co-locate those instances on the same box or rack. Therefore, you don't know what the network throughput is between your different cloud-located servers. Also, in most cases you can't really control the architecture of your data storage layer. You will likely be limited to software RAID and won't be able to choose the type of communication backbone between your CPU and storage. You're essentially locked into how the cloud vendor's storage is architected.

Harris downplayed data transfer speeds, calling it a "non-issue" for Shaklee, which uses a solution from PivotLink. Shaklee loads all of its sales, customer, marketing and cost information into its data warehouse. It's "a lot" of data, said Harris, loaded at least daily, in various formats. Commenting on my post, Ajay Dawar said some of PivotLink's customers have "billions of rows of data, thousands of internal and external users and multiple data sources and some have all three of the above, combined."


Dine encourages companies to carefully review cloud pricing structures, as they vary from provider to provider. A possible "gotcha" is that it can vary by month or quarter, making it a square peg trying to fit into the round hole of traditional corporate budgets.


Beyond specific pricing plans, Forrester's Evelson offers a comprehensive list of what to look for in a vendor offering cloud-based BI applications. Among his items:

  • Ability to modify, expose and reuse product functionality in other applications via APIs or Web services.
  • Enough flexibility so the software doesn't depend on a single data source.
  • Favorable recommendations from Salesforce.com or other vendors upon whose platform, data source or analytics the BI product is based.
  • Functionality to support various styles of BI including reporting, querying, OLAP and dashboards.
  • References from customers in production (last two words are key).


Evelson also suggests four way to mitigate risks of cloud-based BI:

  • Back up your own data.
  • Line up a cloud-based Plan B that can be used if your vendor fails (remember LucidEra).
  • Get a commitment from Salesforce.com or similar vendor to support your SaaS analytics if your vendor fails.
  • Create a contingency plan for internal IT to take over and migrate cloud data into the company's enterprise BI solution, and periodically test it/


To end on a positive, rather than a scary, note: Datasource Consulting's Dine thinks cloud computing may finally lead to the long-promised "pervasive BI." He said:

We're starting to see licensing models change to meet the utilization-based computing model. In the near future, companies may no longer be constrained by large, upfront, user-based licensing fees. Software-as-a-service vendors are also leveraging the cloud, making it easier for small businesses to load and analyze their data with very little upfront cost and administrative overhead.