A Survey of Current End-user Data Analytics Tool Support

There is a large growth in interest in big data analytics to discover unknown patterns and insights. A major challenge in this domain is the need to combine domain knowledge - what the data means (semantics) and what it is used for - with data analytics and visualization techniques to mine and communicate important information from huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics and visualization applications. However, delivering such big data analytics application requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillset usually take a long time to build and have high running costs. An alternative is to provide domain experts and data scientists with tools they can use to do the exploration and analysis directly with less technical skills required. We present an overview and analysis of several current approaches to supporting the data analytics for end-users, identifying key strengths, weaknesses and opportunities for future research.