No doubt, big data technology can be a key enabler for data-driven decision making. However, there are caveats. Processing technology for unstructured and structured data alone-with or without Artificial Intelligence-will not suffice to catch up the promises made by big data pundits. This article argues that we should be level-headed about what we can achieve with big data. We can achieve a lot of these promises if we also achieve to get our interests and requirements better reflected in design or adaptation of big data technology. Economy of scale urges provider of big data technology to address mainstream requirements, that is, analytic requirements of a broad clientele. Our analytical problems, however, are rather individual, albeit mainstream only to a certain extent. We will see many technology add-ons for specific requirements, with more emphasis on human interaction too, that will be essential for the success in big data. In this article, we take machine translation as an example and a prototypical translation memory as add-on technology that supports users to turn the faulty automatic translation into a useful one.