The phenomenal growth of global data volume in recent years originates from many sources, including automated business processes, web applications, and user-created content, as well as digital data that is generated through the instrumentation of the physical infrastructure. Taking advantage of this big data presents new opportunities for today’s organizations to accomplish tasks more intelligently and productively. The problem is that the necessary information is rarely available to the organization precisely when and where it is needed because the data is growing faster than IT processes can extract and process it. Over time, subsets of the data have ended up in siloed warehouses and applications, where it is then replicated and duplicated into multiple forms throughout the organization.
This decentralized data infrastructure poses critical challenges to those who provide and consume business analytics information. Implementing business intelligence and analytics solutions within siloed organizational business units has accelerated the sprawl of data marts, data warehouses, and even operational data stores. With the decentralization of the data also comes an explosion of processes to take care of extracting, transforming, moving, and reloading data. In most cases, these processes are coordinated by a strategic architectural design and therefore result in a fragmented and siloed approach to business intelligence.
This IBM® Redbooks® Point-of-View publication describes a cost-effective solution to these challenges by using a centralized hub that provides the following advantages: