Supporting Decision Making with Big Data: Integrating Legacy Systems and Data

Abstract

In today’s world having the right data to support decision making is critical for organisations. The data required for decision making will not be stored in one or even a few locations; it will not be just one or even a few types and formats; and it will not be amenable to analysis by just one or a few analytics. As the demands of Big Data exceed the constraints of traditional relational databases, evaluating legacy data and assessing new technology has become a necessity for most organisations, not only to gain competitive advantage, but also for compliance purposes. A major challenge is managing the organisation's legacy systems and data to support decision making. How to handle legacy systems and data is too often an afterthought and can have a significant impact on the organisation’s ability to make decisions. At present organisations are mainly analysing internal data - sales, inventory, and shipments using ERP data. Organisations require analysing external data to gain new insights into customers, demands, needs, markets, supply chain and its operations. Big Data represents a fundamental shift in business decision making. There are many factors to consider when dealing with legacy systems and data as part of Big Data. In this paper we discuss the state of the art and issues and problems of how legacy systems and data are integrated with Big Data to support decision making. Our paper gives an overview of some of the business analytics that support business decision making, as well as some of the data management practices needed for success.