**6.1 Recommendation**

The chapter discusses the contribution of data mining cleaning on a dataset. This is achieved by discovering the errors and inconsistencies in the dataset and utilizing datasets stored in various resources. The authors discuss the importance of the management in organizations for attaching the vitality of data sourcing and strategic decision-making. The management must ensure that the correct, timely and accurate data is used in strategic decision-making to generate the ever-elusive competitive advantage. Furthermore, due to the key roles of the available data, big data has become a strategic resource. The data security required to be enhanced at all strategic decision-making levels to avoid unauthorized person (s) must be explored as future work.

#### **6.2 Conclusion**

Most organizations rely on data-driven decision making; therefore, the information system is closely related to business process management to leverage their processes for competitive advantage. Nowadays, the amount of data is constantly increasing, but the data quality is decreasing as much of the data collected is messy or dirty. There are various data cleansing approaches to solve this challenge, but data mining cleansing remains a tool to deal with the criteria of big data. Some of the approaches are not suitable for big data as there is a significant amount of data that needs to be processed simultaneously. Despite the availability of existing frameworks for data cleansing for big data, the value and veracity of the data are often disregarded while developing the approaches. Moreover, data mining is undeniably required to verify and validate the data before it can be subjected to an analysis process.
