Thought Leadership

How to Operationalize Data Science in the Enterprise: The Five Challenges to Address

The end-to-end process for launching a data science project is daunting – and many enterprise projects never make it to production.  The process is similar in most organizations and consists of: Data collection, last mile ETL, feature engineering, and machine learning. However, while the process is understood by most teams, the actual execution is very complex and involves a high-level of operational risk.
We recently published a complete guide to operationalizing data science. In this guide, we identified five complex issues to be addressed, for a business to derive value from operationalizing data science.

Highlights from the paper:

Issue 1: Quality

There are two groups in the data science process who are not aligned operationally:
1) Data engineers build data pipelines with SQL or GUI-based tools, 2) Data scientists build machine-learning scoring pipelines using Python or R.  Software engineers must often reimplement much of the work from these two groups before production can start.

Issue 2: Integrability

Data and scoring pipelines may have been developed and implemented on different technology platforms and are difficult – or impossible – to integrate.

Issue 3: Maintainability

Data science pipelines must be maintained.  The traditional approach is to manually re-create the entire data science process, which increases the amount of maintenance efforts.

Issue 4: Scalability

Limited computation resources constrain data scientists to use smaller sample data sets, that do not represent the larger data sets needed for scoring, and the process may not be scalable.

Issue 5: Portability

Developing one data science process that works well for two different environments – development vs. production –  is a nontrivial task.

Download the Paper

This white paper describes a holistic, platform-level approach to the problem of data science automation.  To learn more, please check out the complete white paper here.

Walter Paliska

Walter brings 25+ years of experience in enterprise marketing to dotData. Walter oversees the Marketing organization and is responsible for product marketing and demand generation for dotData. Walter’s background includes experience with both software and hardware companies, and he has worked in seven different startups, including three successful bootstrap startups.

Recent Posts

Best Practices for a Robust Enterprise Data Architecture

In today’s fast-changing business world, leveraging data is no longer optional — it’s essential for…

1 week ago

How AI is Reshaping the Lending Industry

A Guide for Chief Risk Officers Navigating the AI Revolution. Introduction For Lenders, the way…

2 weeks ago

GenAI Security: Striking the Right Balance Between Innovation and Protection

Generative AI (GenAI), particularly large language models (LLMs), are reshaping how we work and transforming…

3 weeks ago

Stop Skimming, Start Diving: Unleash Deep Insights with AI-driven Sales Analytics

If you use Salesforce, you’ve likely been swimming in a sea of sales data. Leads,…

3 weeks ago

AI Model Comparison: GPT-4o, Llama 3.1, and Claude 3.5 on Bedrock

Evolution of AI Models The generative AI landscape has experienced remarkable progress in recent years,…

1 month ago

Unlocking Business Insights with Generative AI Text Analysis

1. The Role of Text Analysis in Modern Enterprises Companies deal with large volumes of…

1 month ago