It’s a tried-and-true method, but as a “one-size-fits-all” approach to integration and data management, it’s just not nimble enough for the business.  Establishing the infrastructure, information governance and data models is costly, time consuming, and will likely mean many business opportunities will be missed. Further, once built, business users do not have direct access to the data and the request process to receive a report can lag –which can often result in missed opportunities or supply disruptions like stock outs. Moreover, with the advent of ‘big data’, the approach of starting with data consolidation has become even more expensive and time consuming.

The supply chain management function has specific data and analysis needs, and most companies want strategic insights delivered in real-time, as well as segmented by various dimensions like business group, geography, product category, etc.  These market demands have been met with new delivery methods coupled with a class of visualization and analytics tools that deliver rapid, and agile, data analysis capabilities.  Analytics and visualization platforms empower the business user with a self-service, data discovery tool that provides direct access to data analysis. 

We are seeing a new wave of agile development methods and approaches like design thinking, which model how specific business users, called personas, go about their daily tasks to make business decisions through journey maps.  Combining journey maps with the power of visualization and analytics tools helps transform the traditional data service model and delivers meaningful insights much more rapidly with data that is based on business behavior, not on data formats and types.  It allows global fortune 500 businesses that have grown through acquisition to bring data together from a variety of internal ERPs, manufacturing and supply chain systems to better manage forecasts, inventory, and lead times, and improve manufacturing planning.  These rapid prototypes demonstrate to be immediately valuable to the business, and serve as a model for IT, in building the longer term infrastructure to rollout solutions across business units.

An important consideration in taking this approach is the necessary shift in the business culture to support an analytics-driven organization.  There are two key transformations here; first, the IT team should be willing to collaborate with the business users to ensure that analytical projects reach their potential and deliver high value to the business, as well as to suppliers and customers.  Second, decision-makers should be willing to accept analytics as metrics for which the enterprise will use to help drive the business.