Data science and machine learning are certainly the hot topics for many in businesses and other organizations. However getting value data science is not always a very easy process. Part of the challenge is ensuring you have the right team and right methodology working on your project. There are certainly pitfalls that come in mind. For instance, what might happen if the data you use to build your model has significant differences than the data you use to deploy the model in production?
Another challenge often resolves around technology. Certainly it is easy to get started using a tool such as IBM SPSS Modeler. But what if you have a team of people who have different skill sets and interests including creating code – how can you ensure you can have them all involved? Or you may have gotten started – e.g. you have built models on your computer – but are having trouble to get those models integrated into a production or operational process. Finally, how can you translate the predictions of a model into a business decision that reflects your organization’s goals and constraints?
To hear a fuller discussion on these challenges and how to address them, please register for the upcoming two part webinar series: “Take the Lead”: Activate Your Data Science Practice on June 7 and 14. I will be joined by others from IBM, as well as Chris Fregly from PipelineAI, Ventana Research, and two IBM customers. At this series you will hear from experts about how to take steps in your model building process to avoiding issues in production .
You will also hear about a commissioned study conducted by Forrester Consulting, The Total Economic Impact of IBM SPSS Modeler. This study documents key business metrics including:
• 480% ROI
• 7 month payback period
• 54% of benefits in business growth
• 40% productivity improvement for data scientists
You will also about the value of IBM’s data science offerings for on-premises/private cloud: IBM Data Science Experience, IBM SPSS Modeler and Decision Optimization. You will see how an integrated platform such as IBM Data Science Experience (with the integrations of IBM SPSS Modeler and Decision Optimization) can help address the technology challenges. You will also see an intuitive data preparation capability called data refinery that lets you easily prepare your data and record what you did so it can be run again in the future.
Finally you will hear about how IBM Lab Services can help you to get started on your data science journey. You can get more information and find last minute details here.
To register for this event, please go here.
P.S. If you are in the Seattle area and want to find out more about IBM SPSS Modeler and Data Science Experience Local, sign up for an IBM Data Science Workshop that will be held on May 31.