Enterprise-wide deployment of business intelligence (BI) is being replaced by phased deployment in which a new BI strategy is tried and tested in one department or one application within a company. Through such an incremental approach, the BI strategy can be vetted, lessons learned, and its implementation refined, before it is deployed enterprise-wide, thus averting the substantial time and resources wasted backtracking over earlier attempts across an organization. These lessons can then be used to better plan and execute future roll outs, not only of the BI strategy presently considered, but of future ones.
Step One: Basic Reporting
To reiterate, the idea at the start of a phased BI deployment is to start extremely small, basically taking a micro-to-macro approach. With this in mind, test out a new BI strategy on a relatively small business unit or department. If there is one department which is struggling under substantial backlog, even better. Often this will be the department that is taxed with challenging demands for reporting and comparative analysis. In other words, go where the need is greatest…and the most difficult to consistently meet.
Succeeding in such a department will provide the strongest BI strategy ROI, in that it will prove the case in the worst-case scenario at your firm, thus legitimizing the strategy even more robustly, while addressing the most critical resource drain first. As an enterprise commences a progressively expanded roll-out (integrating more information systems), it’s worthwhile to keep in mind that one department’s performance variables - and challenges meeting them – will likely exist within and affect other departments and business units.
To consolidate data dispersion in redundant operational silos, include a corporate data warehouse within the BI deployment and evolution strategy. Of course it goes without saying that the BI strategy will serve an enterprise best when the impact it will have on the entire enterprise is understood from the outset.
Step Two: Performance Measurement
At this point, BI software can be used to clarify targets and proximity to them by offering a range of tools for users, such as scorecards (ideally, high-level information is paired with intuitively accessible source detail), dashboards (highly visual, user-friendly, graphic aids that present relevant performance data clearly and cogently), best-in-class analysis, exclusion reporting, segmentation, and actual versus target analysis.
Step Three: Decision Enablement
This is the stage at which robustly informed decision-making is enabled for business users. This is supported by the planning functionality offered by some BI software, which helps users scope out plans, replenishment, pricing, etc. Monitoring and alerting capabilities, also available through some BI software, can provide alerts to performance anomalies, supporting faster and more effective corrective action (Source: InetSoft's Business Intelligence Software). And lastly, collaboration BI tools allow users to enjoy smoother workflow management, by facilitating shared viewing of performance analytics across the user base.
Step Four: Predictive Analytics
At the forecasting and optimization stage of a BI strategy, the learnings extracted up to this stage, coupled with the deeper analysis that current versus historical analysis offers, can now be used to prognosticate future conditions and events. This level of analysis, near the apex of the BI strategy, is the point at which users can begin to identify new opportunities and capitalize on them more efficaciously. Tools at this level include: statistical forecasts, outlier detection, and inventory planning.
Step Five: Extended Supply Chain
This is the apex, or better yet, the culmination of the all the stages up to this point. Insights can now be shared with external collaborators, e.g., suppliers, contractors, and customers or clients, in the best way to support the interests of each in working together, while advancing the operational targets of the enterprise.