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					Data Analysis
Technology Assurance Committee
 New York State Society of CPAs

        Presented by:
       Mudit Gupta, CPA

   The Presenter is not a lawyer. No legal advice is
    rendered in this presentation.

   Definition
   Stages of Data Analysis
   Key elements of Data Analysis
   Benefits and Uses of Data Analysis
   Data Analysis Tools
          Data Analysis - Defined
   Data Analysis (“DA”) as it pertains to Technology
    Assurance; is an analytical and problem solving process
    to identify and interpret relationships amongst
    variables. It is used primarily to analyze data based on
    pre-defined relationships

   DA is independent of the tool used

   DA needs a specific mindset
         Stages of Data Analysis

   Data Acquisition

   Data Processing

   Reporting and Output
        Data Analysis – Explained
              Key Elements

   Size & Nature of Data
   Business & IT Source of Data
   Problem Logic
   Expected Results
                Key Elements
            Size & Nature of data

   Size of the data
     Number of records in the dataset
     Number of fields in each record of the dataset
     Maximum length of a record
                 Key Elements
             Size & Nature of data

   Nature of the data
     Field formats
     Field value limitations
     Default values
     Field reference values
               Key Elements
        Business & IT Source of Data

   Helps in appropriate field definition
     e.g. Trade Id is defined as alphanumeric
     e.g. Social Security Number is a required field

   Helps in a better mapping to the end result
     Different dimensions of data e.g. account balance by
      currency, by account, by exchange
     Saves time due to early identification of erroneous
      source data
               Key Elements
        Business & IT Source of Data

   Business Source ~ Functional Data
       e.g. Trade reconciliation data is likely to contain
        trade details, position and account balances.

   IT Source ~ Administrative Data
       e.g. Access Control List (ACL) is likely to contain
        user information, entitlements and audit trail.
                Key Elements
                Problem Logic

   Filtration criteria
   Key fields
   Summarization criteria
   Elimination criteria
   External relationships
                Key Elements
               Expected Results

   Sample client output

   Knowledge of granularities, classifications and
            Benefits & Uses of DA
   Benefits
       Ability to process large sets of data efficiently and
   Uses
     Audit
     Fraud detection (SAS 99)
     Litigation support
     Data Quality
     Computer Science
     Physics
                                DA Tools
   Off the shelf
        ACL (
        IDEA (
        SAS (
        Tableau (
   Traditional Programming Languages
        SQL (,
        C# (
        C++ (
   Desktop Software
        Microsoft Excel (
        Microsoft Access (
   Helpful support utilities
        Monarch (
        Textpad (
        Notepad
                    Case Study
   Run through a market value reconciliation using
     Obtaining Source Files
     Loading them in SQL
     Understanding the reconciliation logic
     Re-performing the logic
     Reporting and client discussion
               Useful Links
           About the Presenter
Mudit Gupta, CPA is an Information Systems Auditing
 Senior Consultant at the Ernst & Young LLP's
 Technology & Security Risk Services (TSRS) group in
 New York. In 2004, Mudit obtained his B.S. in
 Accounting and Computer Science at Rutgers
 University. His expertise is in IT audits of Fortune 100
 clients. Mudit is a member of the American Institute of
 Certified Public Accountants and the Technology
 Assurance Committee at the New York State Society of

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