What do you focus on as a data scientist whose mission is to create new value by utilizing big data?
How has forecasting accuracy and the work of data scientists changed with the development of the NEC's proprietary analytics engine?
The biggest feature of the heterogeneous mixture learning technology is that it can automatically discover patterns and hidden rules in the vast amounts of data that it processes. For instance, in the product demand projections that I'm currently working on, demand depends on many different conditions, such as weather, day of the week, time, temperature, whether an event is being held and so on. When these conditions are combined with the hundreds and thousands of products and stores located around the country, we end up having to carry out a huge amount of data partitioning in order to create an accurate forecasting formula.
For the data scientists faced with the job of partitioning all this data, an analytics engine that automatically creates the necessary forecasting formulas is revolutionary. In addition, because the conditions upon which the data partitioning is performed and the contents of the forecasting formulas are easy to understand and visible, the engine can also be used to improve operations post deployment. Heterogeneous mixture learning technology has helped to both increase the accuracy of forecasts and the speed of analysis.
What is the future of prediction and forecasting, and what are your thoughts on the future roles of data scientists?
The evolution in analysis technologies, the increase in sensing devices, and the development of IoT will lead to the mainstreaming of big-data-based prediction and forecasting in fields such as public safety, including urban infrastructure and traffic, facility deterioration and resource demand predictions, and health care. NEC will continue to use advanced big data analytics to create new social value and support business growth.
Personally, I will try to accumulate more experience and knowledge, and proactively absorb new technologies and trends to become an even better data scientist. For example, when I am shopping at a convenience store in my free time, I tend to look at the product layouts and the available products. I look at things as both a data analysis professional and as a consumer. I think that both of these points of view are equally valid and useful, and I would like to use them to make the best proposals for our customers.