5. Understand what users need. The three broad classes of business intelligence users are strategic, tactical and operational. Strategic users make few decisions, but each one can have a profound effect - for example, should we close operations in Europe and open them in China. Tactical users make many decisions a week, and use both aggregate and detail-level information, and likely need updated information daily. Operational users are the front-line employees, such as call centre staff. They need data within their own set of applications to execute the enormous numbers of transactions. Understanding who will use BI and for what purposes can show the type of information needed and its frequency, and help guide BI decision making.
6. Decide whether to buy or build the analytical data model. One size does not fit all. In general you may benefit from an out-of-the-box, industry-specific data model if you have a more homogeneous IT environment - such as one ERP, one CRM system. Do watch for extensibility and hierarchy flexibility. More complex enterprises may benefit from customization, although you may still want to consider beginning with an industry-standard model as a template or a set of guides (such as typical facts, dimensions and so on).
7. Consider all business intelligence components. Components that affect the success of business intelligence implementations include: metadata, data integration, data quality, data modelling, analytics, centralized metrics management, presentations (reports and dashboards), portals, collaboration, knowledge management and master data management. Be sure to define the architecture for all layers of the business intelligence stack; even though they may not be part of the BI strategy itself, they will effect the success of implementation.
8. Choose a systems integrator. Business intelligence implementations require guidance from a partner who has deep experience. Evelson says be prepared to spend $5 to $7 on services for every $1 on software, and cautions: Do not outsource the fine-tuning of business intelligence. The process requires a high degree of collaboration among end users, analysts and developers.
9. Think "actionable" and "baby steps". Choose an end user, business analyst and developer to create a first proof of concept within a few days. Choose a few key performance indicators and build a few reports, then add new releases every few weeks.
10. Choose low-hanging fruit to start. Evelson recommends choosing high-value, simple components to begin. For example, a sales analytics data mart may present high-value targets that also have plenty of existing models and best practices.
