Browsing by Author "Qadadeh, Wafa"

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    Governmental data analytics: an agile framework development and a real world data analytics case study
    (Inderscience Enterprises Ltd., 2021) Qadadeh, Wafa; Abdallah, Sherief
    Data is a key asset for organisations. Investment in data analytics has increased significantly over recent years to facilitate data-driven decisions. However, organisations face many challenges during the adoption of data analytics projects. According to Gartner, only 15–20% of data science projects get completed. One challenge is the lack of business understanding; even more so in government organisations where profit is not the main target. We propose a framework to help organisations (and in particular, government organisations) define the objectives of their data analytics projects. While many published frameworks have been used by organisations to implement data analytics efficiently, the literature has shown a gap between the objectives defined in research and those in real projects. This gap contributes to a lack of business understanding and is the main focus of this paper. The proposed framework introduces a systematic technique for business problem identification. To validate our framework, we used our proposed framework to help a governmental organisation in implementing their first data analytics initiative
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    Using Data Mining in CRM to Understand the Insurance Market
    (The British University in Dubai (BUiD), 2016-11) Qadadeh, Wafa
    Understanding customers’ interests is an important concept for designing marketing campaigns to improve businesses and increase revenue. The rapid growth of high dimensional databases and data warehouses, such as Customer Relationship Management (CRM), stressed the need for advanced data mining techniques. In this paper we investigate different data mining algorithms, specifically K-Means, SOM, and CHAID using the TIC CRM dataset. While K-Means has shown promising clustering results, SOM has outperformed in the sense of: speed, quality of clustering, and good visualization. Also we discuss how both techniques segmentation analysis can be useful in studying customer’s interest. CHAID helps us to predict new target for customers’ interest based on their demographic data.