Instrumenting Your Enterprise for Maximum Predictive Power

Business is all about placing bets and knowing if the odds are in your favor. As noted in a recent Forrester report, business success depends on your company being able to visualize likely futures and take appropriate actions as soon as possible.

You must be able to predict future scenarios well enough to prepare plans and deploy resources so that you can seize opportunities, neutralize threats, and mitigate risks. Clearly, predictive analytics can play a pivotal role in the day-to-day operation of your business. It can help you focus strategy and continually tweak plans based on actual performance and likely future scenarios. 

As noted in a recent Forrester blog post, the technology can sit at the core of your service-oriented architecture (SOA) strategy as you embed predictive logic deeply into data warehouses, business process management platforms, complex event processing streams, and operational applications.

The grand promise of predictive analytics-still largely unrealized in most companies-is that it will become ubiquitous, guiding all decisions, transactions, and applications. For the technology to rise to that challenge, organizations must move toward a comprehensive advanced analytics strategy that integrates data mining, content analytics, and in-database analytics.

Already, we've sketched out a vision of "Service-Oriented Analytics," under which you break down silos among data mining and content analytics initiatives and leverage these pooled resources across all business processes.

You may agree that this is the right vision but have doubt about whether there is a practical, incremental roadmap for taking your company in that direction. In fact there is, and it starts with re-assessing the core of most companies' predictive analytics capability: your data mining tools.

As you plan your predictive analytics initiatives, you should avoid the traditional approach of focusing on tactical, bottom-up project-specific requirements. You should also try not to shoehorn your requirements into the limited feature set of whatever modeling tool you currently happen to use.

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