The Top 10 Critical Challenges for Business Intelligence Success
More than half of all BI projects fail – make sure yours isn’t one of them.
Let’s start with the bad news: More than half of all Business Intelligence projects are either never completed or fail to deliver the features and benefits that are optimistically agreed on at their outset. While there are many reasons for this high failure rate, the biggest is that companies treat BI projects as just another IT project. Face it: Business Intelligence, or BI, is neither a product nor a system. It is, rather, a constantly evolving strategy, vision and architecture that continuously seeks to align an organization’s operations and direction with its strategic business goals.
With BI, business success is realized through rapid, easy access to actionable information. This access, in turn, is best achieved through timely and accurate insight into business conditions and customers, finances and markets. Complex stuff, but worthwhile. Successful BI brings greater profitability, the true indicator of business success. And success is never an accident; companies achieve it when they do the following: • Make better decisions with greater speed and confidence • Streamline operations • Shorten their product development cycles • Maximize value from existing product lines and anticipate new opportunities. • Create better, more focused marketing as well as improved relationships with customers and suppliers alike.
Organizations must understand and address these 10 critical challenges for BI success. BI projects fail because of:
1. Failure to recognize BI projects as cross-organizational business initiatives, and to understand that as such they differ from typical stand-alone solutions. 2. Unengaged business sponsors (or sponsors who enjoy little or no authority in the enterprise) 3. Unavailable or unwilling business representatives. 4. Lack of skilled and available staff, or sub-optimal staff utilization. 5. No software release concept (no iterative development method) 6. No work breakdown structure (no methodology) 7. No business analysis or standardization activities. 8. No appreciation of the impact of dirty data on business profitability. 9. No understanding of the necessity for and the use of meta-data. 10. Too much reliance on disparate methods and tools (the dreaded silver bullet syndrome).