This paper introduced a solution to evaluate the quality of data for business decision making purposes.The quality of data is evaluated in each data processing phase of the big data architecture with the help of quality metadata and quality policies. The solution may be adapted to different contexts, enabling the user to select the applicable quality attributes, evaluate them and apply them in a suitable way into a certain situation. The solution is also extendable; it allows inserting new data sources and data sets for data extraction, as well as new metrics and algorithms for data evaluation.The metadata enables location, retrieval and management of all the data sets, and the quality attributes and their values in metadata enable detection of the quality and value of data in a certain situation. The solution was demonstrated with a case example where a company finds out the level of customer satisfaction regarding the quality of a product utilizing social media data. The solution was implemented with an industrial partner
using a standard interface, which facilitated independent work of the company and the research organization, and functioned as a good communication tool for agreement with the integration. Several development targets were identified when demonstrating the solution. First of all, support for automating the quality attribute evaluation is required. The (semi-) automated adaptation of the organizational and decision making policies is required as well. However, the more knowledge the company achieves, the more the decision making process can be automatized with the help of quality policies. At this moment the quality evaluation is limited to only a few quality attributes; the purpose is to extend the quality evaluation to include more quality attributes.One of the most important development targets is, however, to include other data source types, such as customer feedback data, product data and market analysis, to the quality evaluation to achieve ‘customer insight’ into business decision making.