The use of freely available online data is rapidly increasing, as companies have detected the possibilities and the value of these data in their businesses. In particular, data from social media are seen as interesting as they can, when properly treated, assist in achieving customer insight into business decision making. However, the unstructured and uncertain nature of this kind of big data presents a new kind of challenge: how to evaluate the quality of data and manage the value of data within a big data architecture? This paper contributes to addressing this challenge by introducing a new architectural solution to evaluate and manage the quality of social media data in each processing phase of the big data pipeline. The proposed solution improves business decision making by providing real-time, validated data for the user. The solution is validated with an industrial case example, in which the customer insight is extracted from social media data in order to determine the customer satisfaction regarding the quality of a product.
Anne Immonen, Pekka Pääkkönen, Eila Ovaska (VTT): Evaluating the Quality of Social Media Data in Big Data Architecture