2009 Jan 01
Does On-Demand Business Intelligence Make Sense?
David M. Raab
DM Review
January 2009

Whether you call it hosted, cialis on-demand, viagra software-as-a-service or cloud computing, online the idea of using shared systems managed by someone else is being tried for every conceivable technology function, including business intelligence.    But technologists are famous for pushing good ideas beyond their natural limits.  So it’s worth asking whether on-demand business intelligence truly makes sense.

In some ways, the fit is quite good.  Business intelligence systems are primarily analytical, not operational in the sense of order processing or billing.  This means a business intelligence system need not be tightly integrated with the other enterprise processes and can endure unexpected downtime without catastrophic results.  Integration and availability are both significant issues for on-demand products.  There is also a long tradition, at least among marketers, of hiring outside service bureaus to build and manage their analytical databases.  This is primarily because internal IT departments have had other priorities and lacked the specialized skills needed for such systems.  This history of relying on outsiders makes it easier to consider using another external service.

But on-demand business intelligence faces hurdles as well.  The most daunting is sheer diversity. Popular on-demand applications, such as collaboration, content management and sales automation, are largely the same from company to company.  Not so with business intelligence, where most implementations integrate a unique set of data sources into a custom-tailored structure.  This makes standardization difficult and requires personal attention from technical experts.  Yet savings from standardization and automation are precisely how on-demand systems make their money.  Without them, on-demand has no particular advantage over conventional approaches.

On-demand business intelligence vendors including Autometrics, Birst, BlinkLogic, GoodData, LucidEra, oco, OnDemandIQ, and PivotLink have addressed these issues in different ways.  Strategies include:

– predefined sources.  Vendors can focus on specific applications, such as analyzing data from Salesforce.com.  This lets them prebuild connectors to the source data, map their contents into a standard database design, and create standard reports.  Technically this is a simple approach.  In fact, it is no different from the vertical application packages commonly built for conventional business intelligence systems.  Nevertheless, it lets vendors provide substantial benefits to their clients.

– tools to let users do the work for themselves.  Common examples are wizard-driven interfaces to set up data integration rules and define reporting requirements.  This is a relatively easy solution from the vendor perspective, since it generally boils down to putting a new interface on an existing tool.  However, it places a substantial burden on clients who may lack the time or skill to do the work.  Vendors who take this approach must supplement it with staff experts who help clients when needed.  Although relying on staff experts begins to look uncomfortably similar to a conventional service bureau, the vendors would argue that their tools allow both clients and staff to be significantly more productive than traditional alternatives.

– automated tools for design and integration.  Several vendors have built systems that automatically import client data, analyze it, and create appropriate data models and integration processes.  Although business and technical experts should review the results before implementation, the automated tools do have the potential to provide a reasonable first pass at the system.  This gives the experts something to review and the end users something they can work with right away.  Automated design is the most technically demanding approach to on-demand business intelligence, but it also tackles the twin issues of variety and labor most directly.

– specialized analytical databases.  Columnar and in-memory database engines are more efficient at analytical processing than conventional products like Oracle, DB2, and SQL Server.  This lets business intelligence vendors build successful systems without fine tuning each implementation, and lets the system accommodate new data elements and queries without major redesign.  Specialized databases nicely complement automated design tools by allowing simpler database designs, which are easier to automate; providing adequate performance without optimization; and allowing easier adjustments to the automated system’s recommendations. The analytical databases also run on less expensive hardware than conventional database technology, supporting the fundamental on-demand goal of lower costs.

– automated opportunity discovery.  This addresses the ultimate roadblock to business intelligence adoption: users don’t always know what to do with the results.  Automated data evaluation tools be adapted to mine for significant business information and present the findings to users.   Although conventional software and staff experts can do something similar, automated discovery is particularly value for on-demand systems because it proves the system’s value almost immediately.  In fact, if the vendor has successfully eliminated labor from the rest of the implementation process, it may be able to make automated analytics part of the sales cycle, reducing the buyer’s risk to almost nothing.

Can any of these strategies simplify the delivery of business intelligence systems enough to make them a viable on-demand business?  Bear in mind that the vendors are competing not only with each other, but against the on-premise and on-demand offerings of conventional business intelligence providers; analytical offerings integrated with Customer Relationship Management and Enterprise Resource Management suites; and solution mash-ups that combine on-demand services such as data integration, data storage, and processing power.  All these vendors have the same underlying technologies available.

Although the jury is still out, I believe the answer is yes.  Right now, offerings tied to specific data sources and applications are most likely to succeed because they combine inherent labor savings with the efficiencies of on-demand infrastructure.  Longer term, the greatest potential lies with automated tools and analytical databases.  These should let developers create an integrated process that requires little effort from the vendor or its clients, and is substantially more flexible and cheaper than conventional alternatives.

As with any technology, on-demand business intelligence will be limited at first.  For now, it’s worth testing the automated systems to understand what they really can do.  Even if the current reality doesn’t quite live up to the promises, bear in mind that the systems will grow over time.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics.  He can be reached at draab@raabassociates.com.