Business intelligence professionals face a challenge inherent in the field. On one hand, data analysis and use without expertise can result in disaster. Bad data quality, misunderstanding of data definitions, the mistaking of correlation with causality, and a focus on unimportant data relationships are just a few of the ways that lay people can trip and, with them, the business.
On the other hand, self-service is necessary. A new study from Forbes Insights says that successful BI programs “are significantly more likely to place analysis and decision-making solutions in the hands of business users” than to hold those functions for central BI staff.
The global study, which surveyed more than 400 senior IT and business professionals, confirms that self-service data environments deliver stronger returns—with 64 percent of respondents agreeing that “self-service data analysis models create significant competitive advantage.” At the same time, IT departments and business unit heads alike are concerned about data governance issues impacting everything from data security to its completeness and veracity.
As the study suggests, decentralized use of BI tools makes sense because it is the business users that know the most about what they need. Beyond the contextual knowledge that lets them know what to look for, business users who have access to self-serve technology can make use of it when they need to. Relying on a central BI bureau introduces delay, sometimes significant, to the process. The pressure for self-service often comes from a combination of senior management and bottom-up initiatives.
Self-service BI is here to stay, and that’s good. Without it, chances are that efforts might not deliver the value the business expects. But BI experts have to consider and plan for a number of issues, including the following:
- Self-serve doesn’t mean hands-off for BI experts. Someone has to create, configure, and manage the systems. Factor in the costs when discussing new initiatives.
- Any self-service system will require users to be cognizant about the requirements and limitations of data analysis. You’ll need to consider education, initial and continuing, to get users up to speed and keep them there as well as automation technology to help encourage people to do what they should and avoid what they shouldn’t.
- New systems mean setting realistic expectations. If people expect much more than software and data can give, they’ll never be satisfied and the project will not deliver perceptions of value. If expectations are in line with what is possible, users may be far happier.
- Focus on the business needs. No tool will deliver if it becomes more important than the job to be done.
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