In our previous blog, we discussed best practices and challenges for applying data science. Now that we have some best practices in mind, let’s think about a specific use case: app development.
Today’s users demand more information than ever before, faster than ever before.
To deliver that information in real time, you need to design smarter apps. And to do that you need to operationalize your data science initiatives. Here are 6 steps to create smarter apps and put your data science projects to work:
- Personalize experiences: Modern consumers expect experiences tailored to them. With data science and machine learning, you can target the right customers with personalized discounts offered at the right time within your application.
- Monitor promotional activities in real time: In order to react when it counts, companies must monitor promotional campaigns as they happen. Analyze promotions and campaign performance in real time to better serve and engage with your audience.
- Embrace citizen data scientists: As discussed in the best practices blog, making data science for everyone is critical for successful data science. Empowering citizen data scientists can help your organization better target segments and adjust key variables to optimize campaigns.
- Reuse data science: Another way to encourage collaboration across business, IT, and data science teams is to make data science reusable by non-technical employees. To increase productivity and scalability of data science, it’s best to create an internal marketplace with reusable templates to accelerate model creation. Furthermore, data scientists can embed these templates into their dashboards and visualizations to empower citizen data scientists.
- Infuse machine learning within critical business systems: Machine learning models only become valuable once they are infused into your business systems to drive growth. Integrate data science pipelines with your website and customer service applications.
- Model operations and monitoring: Keep your models up-to-date as new data comes in to ensure that your machine learning models are delivering the most accurate predictions. Continuously check that models are running properly in production and automatically refresh and recalibrate as data drifts and models decay.
Don’t wait to put these lessons into practice.
Start designing smarter apps with Spotfire® Data Science today and deliver the insights that users crave.