This is the second half of a series on on-demand business intelligence tools
- Can on-demand business intelligence break down pervasive BI barriers?
- How to overcome the downsides of on-demand and SaaS BI software
As we discussed in the first part of this article, on-demand business intelligence (BI) software can be less expensive, faster to deploy and easier to use than traditional on-premise BI tools are. That is the optimist’s view of on-demand BI; but what about the glass-is-half-empty side of things?
The fact is that the typical enterprise needs to overcome several roadblocks before leveraging on-demand and Software as a Service (SaaS) BI technology. For starters, companies often are looking to reduce the number of BI tools they’re using, not add more.
After the initial BI rush in the 1990s, many organizations found that they had various groups using different tools -- more than a half-dozen separate products, in some cases. A lot of those companies have worked hard to consolidate BI systems in order to cut costs, conserve scarce resources and make it easier for business users to get trained on BI applications. That makes introducing yet another BI tool a tough sell -- no matter how compelling the technology might be.
There are other issues to contend with as well:
Who’s selling? Most of the vendors selling on-demand and SaaS BI software are small and relatively obscure compared with the high-tech titans that sell the mainstay on-premise BI products. In addition, the fact that the BI mega-vendors engage in so many mergers and acquisitions doesn’t inspire much confidence about which of the on-demand BI vendors will still be around tomorrow.
If it sounds too good to be true... On-demand BI vendors often make outrageous claims about how little time it takes to set up BI systems with their technologies and how business users will never need IT’s help again. People in both IT and the business who have been there and done that know it’s easy to build reports, no matter what data you’re accessing. What takes time is creating consistent, comprehensive and up-to-date information for users to access. That’s not a function of the BI tool but of data integration, data quality and data governance programs. Vendors claiming to be able to deploy full BI systems in minutes just sound naive.
Maybe they mean “too simple” rather than “easy.” SaaS BI software is often easier to use specifically because (as mentioned in Part 1 of this article) vendors haven’t packed everything they could think of into their tools. More is sometimes less in the eyes of business users. But in a functional comparison (the bedrock of software evaluations), on-premise products generally check off many more features than on-demand BI offerings do, and thus they rack up much better scores.
So, there are pluses and minuses to using on-demand BI technologies. The key, then, for an organization that’s interested in adopting SaaS BI tools is to position them properly within its overall BI architecture. Here are some ways to do that:
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Find the sweet spot. Traditionally, on-premise BI has targeted business power users -- i.e., those with the greatest need for analytic functionality. These are the folks who in the past built the Excel spreadmarts or “data shadow systems” that BI applications were meant to replace. The power users enjoy working with technology and spend a lot of time using it. The rest of the business community within an organization just wants BI tools to make their jobs easier; they don’t want using the tools to become their jobs. On-demand BI tools that are easy to learn and easy to use will appeal to those users.
Stretch your BI budget. Rolling out an on-premise BI implementation enterprise-wide can be too costly for many organizations because of software licensing costs and the amount of IT resources that are necessary. Most traditional BI vendors have software pricing models that work against pervasive BI deployments. Because of that, BI licenses are often “rationed” to power users. In contrast, SaaS BI costs typically involve a monthly subscription fee that is predictable from a budgeting standpoint. In addition, on-demand technologies usually don’t require increased IT staffing or infusions of money for IT infrastructure investments. That can make it easier to scale installations toward a pervasive BI goal.
Knock Excel off its throne. Excel remains the true king of BI applications, and trying to stamp out Excel BI use may be a futile endeavor for IT pros. But many business users have done as much as they can with Excel and are ready for more. Deployments of on-demand BI tools can be oriented toward Excel users who want to increase their reporting and analytical capabilities.
The bottom line is that many enterprises have invested heavily in data integration, data warehousing and BI, yet they still have only limited numbers of business users getting value out of those investments. Real return on investment (ROI) comes from BI becoming more pervasive throughout organizations. Instead of a rigid either/or scenario, using on-demand BI tools to complement on-premise products can enable companies to extend reporting and analytics to more employees and thereby achieve a healthier value proposition for their BI investments.
Some might suggest that an enterprise should just try to expand the use of existing on-premise BI tools. But the reality is that if such an expansion were going to succeed, it probably would have been done already. And in many cases, it isn’t really a question of on-premise vs. on-demand BI technologies but rather one of spreadsheets vs. SaaS BI tools. That’s a choice that should be easy to make.
About the author: Rick Sherman is the founder of Athena IT Solutions, a Stow, Mass.-based firm that provides data warehouse and business intelligence consulting, training and vendor services. In addition to having more than 20 years of experience in the IT business, Sherman is a published author of more than 50 articles, a frequent industry speaker, an Information Management Innovative Solution Awards judge and an expert contributor to both SearchBusinessAnalytics.com and SearchDataManagement.com. He can be found blogging at The Data Doghouse and can be reached at email@example.com.