Getting Started with Structured Data Analytics: Cardinal Knowledge of Thy Data



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Businesses use structured data analytics to understand business trends, predict sales, guide investments, and control risks. IT uses analytics to manage Web stores, plan production capacity, and highlight data security concerns. During the past five years, structured data analytics -- data mining, data profiling, data forensics, risk analysis, and predictive modeling -- gained prominence in IT. Analytics now takes its place alongside applications, transaction processing, business intelligence, and content management. In this guidance document, the first of a multipart series, Research VP Joseph M. Bugajski introduces the framework for understanding data, which is a reasonable first step for the analysis of data.

Table of Contents

  • Summary of Findings
  • Guidance Context
    • Problem Statement
    • Guidance Applicability
    • Related Guidance
  • The Gartner Approach
  • The Guidance Framework
    • Pre-Work
      • Essential Incompleteness
      • Use Cases
      • Data Lineage
      • Review Elementary Probability and Statistics
      • Powerful Data Samples
      • Practical Considerations
    • Framework: Cardinal Knowledge of Thy Data
      • Step 1: Review — Become Familiar with the Data Structures
      • Step 2: Profile — Learn About Data Elements in Each Field
      • Step 3: Correlate — Study Inter-Field Relationships Using Count Tables
      • Step 4: Analyze Metadata — Compare Models to Analyses
      • Step 5: Know Thy Data — Step Back and Marvel at Knowledge of Thy Data
    • Follow Up
  • Risks and Pitfalls
    • Don't Get Gun-Shy
    • Avoid Analysis Paralysis
    • Prioritize the Priorities
    • Beware the Black Holes
    • Watch for Saboteurs
    • Speak Sans Forked Tongue
    • Sample Wisely
    • Seek Quality, Not Quantity
    • Vanquish the Vexing
  • Conclusion
  • Recommended Reading
  • Notes
  • Recommended Reading
© 2011 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartners research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

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