ID Number: G00210138



Getting Started with Structured Data Analytics: Cardinal Knowledge of Thy Data
1 April 2011
 
Joe Bugajski  

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.





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Table of Contents

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
Tables
Table 1.
Count Table (G 1 )^ for a Data Sample Table and Sample Item Inventory
Table 2.
Food Value Counts
Table 3.
Size Value Counts
Table 4.
Store Number Count Table
Table 5.
Sub-Table, Food Type, and Size
Figures
Figure 1.
Structured Data Analytics: Cardinal Knowledge of Thy Data
Figure 2.
Data Model Transformations Cause Information to Be Forever Lost
Figure 3.
Rudimentary Statistics
Figure 4.
Equation to Estimate Sample Size to Achieve a Given Error Margin
Figure 5.
Equation to Estimate Sample Size to Achieve a Given Level of Confidence
Figure 6.
The Data Access Dilemma
Figure 7.
Framework Step 1
Figure 8.
Framework Step 2
Figure 9.
Shannon Entropy of an Element s j of a Set S
Figure 10.
Framework Step 3
Figure 11.
Count Tables
Figure 12.
Linearly Increasing Cardinality
Figure 13.
Exponentially Increasing Cardinality
Figure 14.
Asymptotically Increasing Cardinality
Figure 15.
Step 4: Analyze Metadata
Figure 16.
Step 5: Know Thy Data




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Resource Id: 1616214