Most businesses today struggle with operationalizing data science and machine learning pipelines which prevents them from realizing the full value and monetizing it within their organizations. But why is that? And how can you successfully overcome common challenges? Read on to learn how to best democratize, collaborate, and operationalize data science across your organization to accelerate business value.
Top Challenges for Data Science:
- Organization: Lack of alignment between data science, business, and IT
- Process: Lack of coordination in the development, deployment, and maintainability of models
- Technology: Incompatible systems, data access challenges, and infrastructure
How to Solve These Challenges:
- Democratize data science by allowing collaboration via automation, reusable templates, and a common collaborative framework for cross-functional teams
- Focus on monitoring, managing, updating, and governing processes with ML Ops (i.e. DevOps for data science)
- Choose a platform that provides orchestration of open source technology and governance across the end-to-end analytics lifecycle
To learn more about how to solve these top data science challenges and make data science collaborative and available for everyone at your organization, refer to this Data Science for Everyone infographic.
As these top challenges illustrate, data science isn’t just about algorithms. To be successful in your data science initiative, you need to rethink and optimize your strategy concerning people, processes, and technology.
For technology you need to:
- Generate meaningful features from input data that map to real-world factors
- Build trustworthy models that are unbiased, transparent, and make intuitive sense
For processes, you need to:
- Understanding the business decision to be made and ensuring you are asking the right analytic questions for your business
- Use quality and governed data to optimize your model accuracy
- Explore your data using visualizations and Auto ML
For people, you need to:
- Infuse your data science & machine learning models into business operations to improve outcomes
- Apply your technology and processes to optimize operations, deliver personalized offers, and deliver exceptional customer experiences
Here are some best practices to accomplish this and gain a competitive advantage with data science: Download this infographic to learn 7 Best Practices for Useful Data Science.
With these challenges and best practices in mind, use TIBCO® Data Science to create operational solutions for your business. And, please look for the next blog in this series to see an example of how to apply these best practices to design smarter apps with machine learning.