Towards a completeness prediction based on the complexity and impact

As long as companies arrived at a maturity, where business processes have stagnated or even the diversity of business processes, research is directed towards the improvement of data quality, that is to say, improving dimensions quality for data, the first problem is to check if this improvement in one dimension will be held or not given its cost and its impact, the second to find a centrality to improve total quality without penalizing one dimension our current paper addresses the first problem to a dimension that is completeness. This improvement will be have a cost that we cannot calculate clearly, but we can measure her complexity, because the complexity increases with the cost, the approach here is implemented under a Java EE environment. © Springer International Publishing AG 2018.