Business Intelligence (BI) is not a new concept to the enterprise world, it has long held a core position in the integration and operation of businesses of reasonable complexity. Advances in BI technology over the past two decades have enabled two significant trends in the enterprise environment: systems integration and information management. Systems integration includes infrastructure communication, tools for application development and social collaboration for business process management. Information management on the other hand is about capturing, organizing and presenting the information.
Industry leaders taking advantage of these capabilities (to increase business flexibility and information transparency) have driven much of the value created by BI systems.
Integrated analysis capabilities is an area within BI platforms that has not developed as quickly. This form of analysis provides the ability to perform processing and analytics within the BI platform to drive modeling, simulation and predictive activities. Combined with previously implemented integration and information technologies, integrated analysis can support real-time, accurate decision making across business areas. Advances in server-side processing, data visualization, and predictive technologies have powered the rise of analytics and the improvements have not gone unnoticed. A Forrester Survey on BI analytics usage indicates that the majority of firms have already implemented or are planning to implement basic capabilities such as reporting, dashboards, performance, web and embedded analytics. The survey also found significant interest in advanced technologies including predictive, location and interactive discovery analytics.
Analysis is the third leg of the next generation BI platform and is integral for business success. As the data flowing within organizations and from external sources continues to grow, analytics will be the only way to gain insight into vital business processes. The importance of time-efficient and even automated analytics then becomes key to bridging the gap between intuitive and fact-based decision making. Big data presents a tremendous opportunity and analytics is the key to unlocking that potential.
To prepare for analytics integration businesses should follow four main steps: identify, plan, train and measure.
Identify
Identify critical business activities that can be better informed through data. This requires collaboration from stakeholders across the company in order to build an understanding of business processes and data availability.
Plan
Planning is the selection and physical integration of the appropriate technologies to address the business opportunity.
Train
Training is arguably the pivotal step in a successful integration, as the value of analytics cannot be realized if workers do not utilize them or know how to interpret the results.
Measure
The measuring step ensures that the impact of any analytics implementation can be measured and captured for future optimization.
