Best Practices: Scaling Data Science Across The Enterprise

Industry analyst reports
DataRobot Best Practices Scaling Data Science Across the Enterprise Resource Card v3.0

Executives and data scientists alike are frustrated by the challenges of turning new data science initiatives into business impact at scale. To successfully scale data science, firms need to better align data science, IT, and business teams to work together more effectively.

This Forrester Research report lays out five best practices that application development and delivery (AD&D) professionals should use to drive business value with new data science initiatives across their organizations.

DataRobot Best Practices Scaling Data Science Across the Enterprise Resource Card v3.0

Five Best Practices To Scale Data Science Across The Enterprise

  • Prioritize Projects That Target Large, Well-Defined Business Outcomes
  • Build Hybrid, Scalable Teams
  • Conjoin Data Science And Data Engineering
  • Build Platforms To Create Collaboration, Reduce Rework, And Accelerate Adoption
  • Map Out Each Stage Of The Project Life Cycle
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
    Deena Narayanaswamy
    Deena Narayanaswamy

    Head of Data Insights, Avant

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
    Evariant