Gartner Research

Create a Data Strategy for Machine Learning in Advanced Analytics Initiatives

Published: 10 May 2019

Summary

Organizations struggle to use data effectively and efficiently to support machine learning in advanced analytics initiatives due to growing diversity in data projects. This research guides data and analytics technical professionals on developing a data strategy to support successful deployments.

Included in Full Research

  • Prework: Build a Business Motivation Framework for ML
    • Defining the End Objective
    • Defining the Means Objectives
    • Providing Assessment and Governance to Support the Data Strategy
    • Defining Influencers Critical to the Success of the Data Strategy
  • Step 1: Develop a Targeted Acquisition Strategy
    • 1.1 Determine Where to Get Data
    • 1.2 Select an Approach to Acquiring Internal and External Data
    • 1.3 Enable Data Engineering Pipelines
    • 1.4 Establish Data Science Pipeline
    • 1.5 Enable Data Science Workflows
    • 1.6 Enable Supervised Learning Workflows
    • 1.7 Enable Unsupervised Learning Workflows
    • 1.8 Secure Data Science Pipelines
  • Step 2: Define Data Preprocessing Architecture
    • 2.1 Refine the Architecture With Storage Options
  • Step 3: Connect ML Analytic Engines
    • 3.1 Feed Big Data Analytic Engines to Support ML Initiatives
    • 3.2 Complement With Automated Machine Learning Engines
  • Step 4: Deliver to ML Workloads
    • 4.1 Complement ML Workloads With Pretrained Networks and Packaged Datasets
    • 4.2 Work With Different Technology Approaches to Sourcing ML Workloads
  • Step 5: Perform a Business Process Review of ML Output
    • 5.1 Identify and Prioritize Processes to Review
    • 5.2 Gather and Analyze Current Process Data
    • 5.3 Conceptualize Future State
    • 5.4 Integrate ML Output Into Business Process
    • 5.5 Evaluate Outcomes
  • Follow-Up
    • Manage Data Pipelines and ML Workloads
    • Adopt Flexible Data Quality Strategies for Machine Learning
  • Risk No. 1: Building Data Science Pipelines Can Be Especially Challenging When Dealing With Big Data Without the Right Tools
  • Risk No. 2: Poor Data Quality Will Significantly Impact Performance and Accuracy
  • Risk No. 3: Techniques for Securing Data Science Pipelines Are Still in Their Infancy
  • Pitfall: Bounded Rationality Exists Even Within ML Applications

Analysts:

Carlton Sapp

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