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and results for the year. These expectations will assist in identifying anomalies and significant audit areas in order to appropriately assess risk. 3. Data Acquisition Sufficient planning, a strong initial risk assessment, and an adequate understanding of your systems will serve as the foundation necessary to prepare our draft data request list. We will initially request information in written format and conduct follow-up conversations helping CLA practitioners our staff share a mutual understanding of the type of data requested and the format required. If there are going to be any challenges/obstacles related to obtaining data, or obtaining data in the preferred format, they will generally be discovered at this point. 4. Technical Data Analysis Technical analysis of the data requires the skillful blend of knowledge and technical capability. Meaningful technical analysis provides the engagement team with a better understanding of the organization. The additional clarity assists the engagement team to better assess what is "normal" and, in turn, be better suited to spot anomalies, red -flags and other indications of risk. Analytics generally fall into five categories, each looking into the data set in a different way and deployed with a different purpose. S. Interpret Results and Subsequent Risk Assessment Trends and anomalies will be identified through the performance of the above referenced analytics. Comments regarding the interpretation of those trends and anomalies will be captured. When trends are identified they are reconciled against expectations. For anomalies identified, the approach to further audit procedures will be considered. 6. Response and Document The last process is to capture responses and determine that our procedures are properly documented. Abstracts, charts or summaries of both trends and anomalies are retained in the audit documentation to support our identification of risks. Our analysis can be tailored and customized to help analyze an array of information, including client specific and proprietary data. Key benefits of data analytics include: • Built-in audit functionality including powerful, audit specific commands and a self -documenting audit trail • 100 percent data coverage, which means that certain audit procedures can be performed on entire populations, and notjust samples • Unlimited data access allows us to access and analyze data from virtually any computing environment • Eliminates the need to extrapolate information from errors (a common effort when manually auditing data) and allows for more precise conclusions The below figure illustrates typical data analytics scenarios.