Build or Buy? Understanding the True Costs of a Data Science Platform

Whitepaper

As organizations increasingly strive to become model-driven, they recognize the necessity of a data science platform. According to a recent survey report “Key Factors on the Journey to Become Model-Driven”, 86% of model-driven companies differentiate themselves by using a data science platform. And yet the question of whether to build or buy still remains.

This paper presents a framework to facilitate the decision process, and considers the four-year projection of total costs for both approaches in a sample scenario.

Read this whitepaper to understand three major factors in your decision process:

  • Total cost of ownership - Internal build costs often run into the tens of millions
  • Opportunity costs - Distraction from your core competency
  • Risk factors - Missed deadlines and delayed time to market

Read Whitepaper

Latest resources

Guide

The Practical Guide to Managing Data Science at Scale

Report

2020 Critical Capabilities for Data Science and Machine Learning Platforms

Whitepaper

Model Monitoring Best Practices

Brief

Accelerate Adoption of SAS Data Science Use Cases in the Cloud Using Domino

Dun & Bradstreet seal