Applying Visual Analytics on Traditional Data Mining Process: Quick Prototype, Simple Expertise Transformation, and Better Interpretation

Publication Type:
Conference Proceeding
Proceedings - 4th International Conference on Enterprise Systems: Advances in Enterprise Systems, ES 2016, 2017, pp. 208 - 213
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ES2016 paper _ XZGX.pdfAccepted Manuscript1.69 MB
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© 2016 IEEE. Due to a lack of experience, business might not be confident about the completeness of their proposed data mining (DM) project objectives at early stage. Besides, business domain expertise usually shrinks when delivered to data analysts. This expertise ought to contribute more throughout whole project. In addition, the outcome from DM project might fail to transform into actionable advice as the interpretation for the outcome is hard to understand and, as a result, unconvincing to apply in real. To fill the above three gaps, Visual Analytics (VA) tools are applied in different stages to optimize traditional data analytics process. In my practice, VA tools have offered both an easy access to generate quick insights for evaluating project objective's viability, and a bidirectional channel between data analysts and stakeholders to break the background barrier. Consequently, more applicable outcomes and better client satisfaction are gained.
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