Data Analysis Technology Assurance Committee New York State Society of CPAs Presented by: Mudit Gupta, CPA Notice The Presenter is not a lawyer. No legal advice is rendered in this presentation. Outline 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 presentation 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 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 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 Useful Links http://en.wikipedia.org/wiki/Data_analysis http://www.indatacorp.com/Products/eDiscov ery/services.aspx http://www.ey.com/global/Content.nsf/US/A ABS_-_Specialty_Advisory_-_IDS_-_Services http://www.ey.com/us/tsrs Questions? 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.