In the world of constantly growing data masses the efficient extraction, saving and accessing that data for business intelligence and analytics has become increasingly important to businesses. Analytics and business intelligence software is offered by many providers in the market for all sizes of organizations and there are multiple ways to build an analytics system, or pipeline from scratch or integrated with tools available on the market.
In this case study we explore and re-design the analytics pipeline solution of a medium sized software product company by utilizing the design science research methodology. We discuss the current technologies and tools on the market for business intelligence and analytics and consider how they fit into our case study context.
As design science suggests, we design, implement and evaluate two prototypes of an analyt- ics pipeline with an Extract, Transform and Load (ETL) solution and data warehouse. The prototypes represent two different approaches to building an analytics pipeline - an in-house approach, and a partially outsourced approach.
Our study brings out typical challenges similar businesses may face when designing and building their own business intelligence and analytics software. In our case we lean towards an analytics pipeline with an outsourced ETL process to be able to pass various different types of event data with a consistent data schema into our data warehouse with minimal maintenance work. However, we also show the value of near real time analytics with an in-house solution, and offer some ideas on how such a pipeline may be built.
