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How to apply best practices for data science

September 9, 2019 by Shannon Peifer

TIBCO How to Apply Best Practices for Data Science

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:

  1. Organization: Lack of alignment between data science, business, and IT
  2. Process: Lack of coordination in the development, deployment, and maintainability of models
  3. Technology: Incompatible systems, data access challenges, and infrastructure

How to solve these challenges:

  1. Democratize data science by allowing collaboration via automation, reusable templates, and a common collaborative framework for cross-functional teams
  2. Focus on monitoring, managing, updating, and governing processes with ML Ops (i.e. DevOps for data science)
  3. 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, contact us. 

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 experience

With these challenges and best practices in mind, use Spotfire® Data Science to create operational solutions for your business.

Categories: Visual Data Science

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