Aside from ease of use and data quality problems, one of the key problems data scientists face is actually making sure that the patterns they are finding are being executed upon. Many times these insights are not recognized because the predictive models tend to be really hard to understand. All of the formulas, mathematics, and strange data science patterns take time to comprehend, especially for business analysts who aren’t familiar with advanced machine learning principals.
Take a look at data scientist Joe here…
Turning predictive insights into actions, and then performing those actions
So here lies the question, “How can I improve the number of times my models are actually used?” This can be done with a framework known as operationalization. Essentially, you’re doing something today so that that your business will get the best outcome tomorrow. But this involves human decision making, and those predictive models take time for humans to understand.
The implication of our results with the help of data observation
If you present your predictions in an environment which you, and others are comfortable with, then it’s much more likely that people will accept those results. Using data visualization tools with RapidMiner can help you amplify your results and make a business case for those who may not understand every model that you have created.
Machine learning with Qlik and RapidMiner
Organizations are investing in data visualization to be able to easily apply more machine learning methods and uncover hidden insights within big and disparate data. In a recent webinar, President and CTO Dr. Ingo Mierswa, RapidMiner demonstrates how you can leverage the value of machine learning within your Qlik environment to make more meaningful decisions, drive more actions and significantly improve the likelihood of achieving desired outcomes.
See more on Joe’s story and how RapidMiner can help improve operationalization in your organization. Watch this on-demand webinar now and see the full demo of applying Qlik machine learning with RapidMiner.