Data Analysis Technology Assurance Committee New York State Society of CPAs Presented by: Mudit Gupta, CPA

1. Data AnalysisTechnology Assurance CommitteeNew York State Society of CPAsPresented by: Mudit Gupta, CPA

2. Notice • The Presenter is not a lawyer. No legal advice is rendered in this presentation.

3. Outline • Definition • Stages of Data Analysis • Key elements of Data Analysis • Benefits and Uses of Data Analysis • Data Analysis Tools

4. 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

5. Stages of Data Analysis • Data Acquisition • Data Processing • Reporting and Output

6. Data Analysis – ExplainedKey Elements • Size & Nature of Data • Business & IT Source of Data • Problem Logic • Expected Results

7. Key ElementsSize & 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

8. Key ElementsSize & Nature of data • Nature of the data • Field formats • Field value limitations • Default values • Field reference values

9. 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

10. 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.

11. Key ElementsProblem Logic • Filtration criteria • Key fields • Summarization criteria • Elimination criteria • External relationships

12. Key ElementsExpected Results • Sample client output • Knowledge of granularities, classifications and presentation

13. Benefits & Uses of DA • Benefits • Ability to process large sets of data efficiently and accurately • Uses • Audit • Fraud detection (SAS 99) • Litigation support • Data Quality • Computer Science • Physics

14. DA Tools • Off the shelf • ACL (www.acl.com) • IDEA (www.audimation.com) • SAS (www.sas.com) • Tableau (www.tableausoftware.com) • Traditional Programming Languages • SQL (www.mysql.org, http://msdn2.microsoft.com/en-us/sql/default.aspx) • C# (http://msdn2.microsoft.com/en-us/vcsharp/default.aspx) • C++ (http://msdn2.microsoft.com/en-us/visualc/default.aspx) • Desktop Software • Microsoft Excel (www.microsoft.com) • Microsoft Access (www.microsoft.com) • Helpful support utilities • Monarch (http://www.datawatch.com/) • Textpad (http://www.textpad.com/) • Notepad

15. Case Study • Run through a market value reconciliation using SQL • Obtaining Source Files • Loading them in SQL • Understanding the reconciliation logic • Re-performing the logic • Reporting and client discussion

16. Useful Links • http://en.wikipedia.org/wiki/Data_analysis • http://www.indatacorp.com/Products/eDiscovery/services.aspx • http://www.ey.com/global/Content.nsf/US/AABS_-_Specialty_Advisory_-_IDS_-_Services • http://www.ey.com/us/tsrs

17. Questions?

18. 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 CPAs.