Data science and machine learning have become a very important part of many of our customers business. Indeed machine learning has influenced our daily lives and for many businesses machine learning has become the new normal for competitive advantage
. IBM SPSS Modeler has long had machine learning and allows non-coders to access data science and machine learning. As we publicly announced in in November 2017
, IBM is investing heavily into IBM SPSS Modeler. We are excited to let you know that we are previewing this in a Web seminar
on IBM SPSS Modeler for Data Science Experience Local on March 15. This is live
webinar where you can ask me questions.
To start with, we are rebuilding the existing interface to a new user friendly web-accessible one. We are also completely redoing the visualization in IBM SPSS Modeler. There is an example of this below. The new visualization is quite interactive – allowing the end user change the look and feel of the chart, the type of chart and the scaling of the chart on the fly. IBM SPSS Statistics
also will be receiving a new interface as well.
We are also integrating the new capabilities of IBM SPSS Modeler into Data Science Experience – both on cloud and in Data Science Experience Local. These new capabilities are planned to be released by the end of March. Up to now, Data Science Experience has focused on data scientists who can code in R, Python or Scala via notebooks or R Studio. While an open source only approach serves the needs of many organizations, many organizations also have data scientists or citizen analysts who either do not know code or prefer the productivity one can obtain in using software like IBM SPSS Modeler. IBM is committed to bringing Data Science to All, enabling both the non-coder and the coder to collaborate within the same platform.
We are also releasing Decision Optimization for Data Science Experience in March as well – allowing data scientists to use Python create optimal decisions based on IBM’s leading optimization technologies within Data Science Experience Local. We also plan to include the text analytics capabilities of Watson Explorer into DSX Local later this year. This combination of open source, visual modeling productivity text analytics and decision optimization in a single platform is a truly differentiating capability for IBM.
IBM also sees data science as a team sport
– where data scientist coders, data scientist noncoders/citizen analysts, data engineers, business analysts and developers can collaborate to obtain value. Data science brings the most values when the results of the advanced analytics can be used to directly influence business decisions and operational systems. A team is always needed to bring an IT system into production – and this is also true for data science. The future strategy of data science and IBM rests on the combination of both Data Science Experience and IBM SPSS Modeler.
Many clients have told us they are not interested in coding capabilities – they only want noncoding solutions for data science. We thus are committed to keeping IBM SPSS Modeler as a separate product. However, the same capabilities found in Modeler are planned to be fully integrated into Data Science Experience – and for many enterprise Data Science Experience will be the way they access Modeler in the future. We are now publicly exposing our roadmaps at ibm.biz/AnalyticsRoadmaps and anyone can keep up to date on our roadmaps by checking this licnk.
To find out more about our plans please join me on the webinar on March 15