After talking about Topic Overview, Reasons to implement and Value Drivers, it is time for „Challenges” in Business Intelligence. As it appears from Gleanster`s report, departments and divisions need to be convinced of the benefits of sharing information for analysis, rather than hoarding it for other advantage. „Data quality problems must be overcome before managers will have confidence in the analysis produced. And to produce better business decisions, the analysis must be focused on core business problems”, it is shown in the report. And, according to „BI Best Practices Brenchmark Report”, none of this is easy.
This is the Top 9 of challenging aspects:
1. Breaking down data / departmental silos.
- this was the top challenge cited by all companies surveyed.
- business units that have been accustomed to managing their own data may be reluctant to share it, even when that sharing is essential at the corporate level. Consolidating data from many databases can be a challenge, but it’s also one of the things BI tools are built for.
2. Integrating with CRM and other systems.
- much of the data that’ll be consumed by the BI system is likely to be found in packaged enterprise applications (ERP, CRM).
- an overarching BI strategy needs to address these integrated reporting tools and how they can be coordinated with the broader platform.
3. Achieving acceptable data quality.
- data quality needs to be high enough that all the most important broad measures of business performance are reflected in the BI system.
- once users of the system learn to doubt the accuracy of the information contained within it, winning back their trust will be extremely challenging.
4. Generating actionable insights.
- the data needs to be meaningful and suggest an appropriate course of action.
- this means working with different constituencies to understand what data they find most useful and the ways that they commonly act upon it.
5. Tracking and measuring success.
- measuring the success of a BI initiative requires imagination, but it’s essential to the program’s success.
- track progress in every area from data quality to user satisfaction with the answers they are getting.
6. Getting managers to use data over “gut instinct” decisions.
- managers who have been successful in the absence of good data may have trouble adjusting to its easy availability.
- unless data-driven decision making is integral to the corporate culture, many managers will continue to go with their gut instincts, even when the data says those instincts are wrong.
7. Securing the right organizational resources.
- to provide proper training or build a BI-competency center may mean hiring employees or consultants.
8. Deploying the right enabling technologies.
- besides securing the budget for purchases, determine which technologies will be most effective.
- balance risks versus rewards.
9. Making the business case in terms of ROI.
- Business Intelligence by itself does not improve business efficiency or profitability.
- anyway, selling or expanding a BI initiative requires a convincing argument, one that ultimately will translate into financial rewards.