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and results for the year. These expectations will assist in identifying anomalies and significant audit areas in <br />order to assess risk. <br />3. Data acquisition <br />Sufficient planning, a strong initial risk assessment, and an adequate understanding of your systems will serve as <br />the foundation necessary to prepare our draft data request list. We will initially request information in written <br />format and conduct follow-up conversations helping CLA practitioners share a mutual understanding of the type <br />of data requested and the format required. If there are going to be any challenges/obstacles related to obtaining <br />data, or obtaining data in the preferred format, they will generally be discovered at this point. <br />4. Technical data analysis <br />Technical analysis of the data requires the skillful blend of knowledge <br />and technical capability. Meaningful technical analysis provides the <br />engagement team with a better understanding of the organization. The <br />additional clarity assists the engagement team to better assess what is <br />"normal" and, in turn, be better suited to spot anomalies, red flags, and <br />other indications of risk. Analytics generally fall into five categories, <br />each looking into the data set in a different way and deployed with a <br />different purpose. <br />5. Interpret results and subsequent risk assessment <br />Training People <br />Trends and anomalies will be identified through the performance of the <br />above referenced analytics. Comments regarding the interpretation of <br />those trends and anomalies will be captured. When trends are <br />Grouping <br />identified, they are reconciled against expectations. For anomalies identified, the approach to further audit <br />procedures will be considered. <br />6. Response and document <br />The last process is to capture responses and determine that our procedures are properly documented. <br />Abstracts, charts, or summaries of both trends and anomalies are retained in audit documentation to support <br />our identification of risks. Our analysis can be tailored and customized to help analyze an array of information, <br />including client -specific and proprietary data. Key benefits of data analytics include: <br />• Built-in audit functionality including powerful, audit specific commands and a self -documenting audit trail <br />• 100% data coverage, which means that certain audit procedures can be performed on entire populations, <br />and not just samples <br />• Unlimited data access allows us to access and analyze data from virtually any computing environment <br />• Eliminates the need to extrapolate information from errors (a common effort when manually auditing data) <br />and allows for more precise conclusions <br />The below figure illustrates typical data analytics scenarios. <br />City of Santa Ana RFP No. 24-009 Page 39 of 63 <br />