Big data can enable part-automated decision making. By by-passing the possibility of human-error through the use of advanced algorithm, information can be found that otherwise would be hidden. Banks can use big data analytics to spot fraud, government can use big data analytics for cost cuts through deeper insight, the private sector can use big data to optimize service or product offering as well as targeting of customers through more advanced marketing.
Organization across all sectors and in particular government is currently investing heavily in big data (Enterprise Ireland, 2014). One would think that an investment in superior technology that can support competitiveness and business insight should be of priority to organization, but due to the sometimes high costs associated with big data, decision makers struggle to justify the investment and to find the right talent for big data projects.
Due to the premature stage of big data research, the supply has not been able to keep up with the demand from organizations that want to leverage on big data analytics. Big data explorers and big data adopters struggle with access to qualitative as well as quantitative research on big data.
The lack of access to big data know-how information, best practice advice and guidelines drove this study. The objective is to contribute to efforts being made to support a wider adoption of big data analytics. This study provides unique insight through a primary data study that aims to support big data explorers and adopters. Author keywords: Big data, data growth, Hadoop, modern data warehousing, advanced analytics, in-memory analytics