5. Future Work

Among the most recently published of the literature, both Roberts et al. and Wu et al. visualised telecommunications data. The future work discussions in each of these papers emphasise the need for more features to be added to their software in an attempt to extract all the value from the data. Each additional useful feature provides the user with an advantage over their competition. This feature-rich desire is prevalent throughout the literature. We also observe the desire for deeper, more meaningful data evaluation in the future work of the field­ – quantitatively evaluating the current visual features and their impact on real-world scenario data. The final outstanding action noted in the future work is to improve the current features that have been implemented.

Another interesting classification of the literature presented here could be based on user-tasks. We have identified this as future work. We also believe that a survey of machine learning techniques combined with visualisation is a promising direction for future work given the rise in popularity of machine learning. At present, commercial breakthroughs appear to be kept secret to provide a competitive edge. However, as the field evolves, it would make a good survey topic in itself.

These directions for future work highlight the differences between the academic and business agenda. Businesses will focus on the development of software with maximised utility, whether that involves focusing on current features and refining them or adding new features that complement the existing system. Academics place more emphasis on brand new ideas as opposed to finding the perfect existing solution to data problems. Therein lies the dichotomy of interest. We also note the lack of literature focusing on company performance­ – this could be due to the classified nature of business performance whereby the company want to keep this information away from the public. We believe this is a direction rich in future work. As the availability of data increases, businesses will be more inclined to utilise the analytical potential of data visualisation.

As the field of data visualisation matures, we can expect more businesses to begin the adoption process. Early adopters of visualisation such as IBM will enjoy a competitive advantage over the later adopters. As the adoption rate increases, the field should advance at a faster rate due to higher levels of popularity and interest.