Gartner Research

BDBA: A Framework for Big Data Behavioral Analytics

Published: 22 January 2012

ID: G00219658

Analyst(s): Joe Bugajski

Summary

Behavioral analytics extracts patterns from structured and quasi-structured data. Those patterns reflect either the way that data itself tends to behave or the way that entities represented by the data tend to behave. Big data introduces system and infrastructure difficulties that IT specialists must resolve to launch a behavioral analytics system. In the big data behavioral analytics (BDBA) framework, Research VP Joseph M. Bugajski offers guidance for building systems capable of analyzing big data.

Table Of Contents

Guidance Context

  • Problem Statement
  • Guidance Applicability
  • Related Guidance

Gartner Definitions

The Gartner Approach

The Guidance Framework

  • Prework
    • Frame the Problem
    • Illustration of Framed Problem
  • Formulate Business Hypotheses
    • Examples of Business Hypotheses
  • Formulate the Analytical Hypotheses
    • Choose the Correct Analytical Method
    • Decide the Procedure Class and Outcome
    • Examples of Analytics Hypotheses
    • Observe Analytics Limits
  • Build the Data Samples
    • Data Sample Sizing
    • Example Data Design Problems
  • Select the Analytical Methods
    • Design for Big Data Scale
    • Prototype the Analytics System
  • Build the BDBA System
    • Data Sampling and Big Data
    • Analytical-Pipelines and Big Data
    • Data Sampling in an Analytical-Pipeline
    • Support the Solution With the Right Organization
    • Analytical Processing Summary
  • Follow Up

Risks and Pitfalls

  • Design Issues
    • Bad Business Hypotheses
    • Incorrect Analysis Hypotheses
    • Wrong Algorithm Design
    • Poorly Sampled Data
    • Failure to Scale
    • Wrong Algorithms
    • Irrelevant Results
  • Implementation Issues
    • Wrong Platform
    • Wrong Processes
    • Scale Problems
    • Excessive Time or Costs
    • Novel Problems
    • Analytics Failures

Conclusion

Notes

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