{"id":354,"date":"2020-11-17T11:12:18","date_gmt":"2020-11-17T11:12:18","guid":{"rendered":"http:\/\/www.rnm.in\/blog\/?p=354"},"modified":"2025-01-30T10:55:45","modified_gmt":"2025-01-30T10:55:45","slug":"internal-audit-beyond-the-data","status":"publish","type":"post","link":"https:\/\/www.rnm.in\/blog\/internal-audit-beyond-the-data\/","title":{"rendered":"Internal Audit – Beyond The Data"},"content":{"rendered":"\n
While data analytics serves as a powerful tool used in organization to gain insight into operations, they may also be a source of risk. <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Feasibility\nto reveal secrets hidden in voluminous data makes use of the data analytics\ndifficult to resist. There are tools enabling the organization to bring\nefficiencies in operations, improve decision, enhance agility, identify market potential\netc.<\/p>\n\n\n\n

Internal\naudit is regular user of data analytics during the ordinary course of\ndischarging its responsibilities to the organization. Internal audit uses data\nanalytics for its own use as well reviews the data analytics done by functional\nunits.<\/p>\n\n\n\n

With\never-increasing volumes of data on hand and entity\u2019s dependency on that data, certain\nrelevant questions need to be answered.<\/p>\n\n\n\n

Assessing the Risks<\/strong><\/p>\n\n\n\n

Existence\nof possibility of things going wrong justifies usage by internal audit function\nof the data analytics in the organization. Majority of chief audit executives\n(CAEs) opines that their organization’s net residual data analytics risks are \u2018moderate\u2019\nto \u2018extensive\u2019.What are these risks?<\/p>\n\n\n\n

    \n
  • If the data\nunder consideration is erroneous, incomplete, disorganized, old, or inconsistent,\nthe conclusions based on itwouldn\u2019t serve the desired purpose for the entity. As\nsuch most worrisome is the completeness and accuracy of the data which is\npresented to management based on which strategic decisions are taken. Management\ngenerally takes the information presented on face value and without doubting the\naccuracy and completeness of data.<\/li>\n<\/ul>\n\n\n\n
      \n
    • Thereis\nconcern about data quality which is addressed through audit. The auditor evaluates\nthe risks around the completeness, accuracy, integrity, and security of data.\nFor instance, since data warehouse is part of the data analytics process, look\nat risks and controls are seen in entirety around the path the data takes viz.\nsources of raw data, the methods and technology used of transferring the data\nto the WH, the controls over the warehouse, and the transfer to the end user. If\nthere are inaccuracies or issues with the data at any point along this path,\nthen the end result may be misleading and any decisions \/ inferences based on\nthis data may also be flawed. <\/li>\n<\/ul>\n\n\n\n
        \n
      • Further, the\ndata may be sound, but the algorithms used to summarize it may be faulty. They\nmay require some additional activities like edit check, doing something unwarranted,\nwithout the business unit being aware. Though this may not necessarily influence\nthe result; then again, it might. <\/li>\n<\/ul>\n\n\n\n
          \n
        • Additionally,\nconcerns are raised about the integrity of data collection process itself –\nethical; used for the purpose for which it was collected; anddata collected in\na way to provide objective results or to prove a point.<\/li>\n<\/ul>\n\n\n\n
            \n
          • Auditor\nneeds to be careful ofbiasedhandling the data andshouldn\u2019t be carried away by\nthe initial impressions while performing subsequent analysis and actions. If done,\nauditor may go down wrong path, getting a result that appears accurate while\nnot realizing we are unintentionally overlooking other data.<\/li>\n<\/ul>\n\n\n\n
              \n
            • Higher the\nvolume of data the organization has, the greater is enticement it providefor\nhacker to prey into it and eventually compromising security and privacy. <\/li>\n<\/ul>\n\n\n\n

              Methodologies to be Used<\/strong><\/p>\n\n\n\n

              While\ndealing with a diverse range of complex risks, certain tried and tested audit\napproaches shall provide best results. <\/p>\n\n\n\n

              Generally,\nthe timing of analytical reviews depends on the nature of the data.If the data pertains\nto operational, technical, or regulatory risks, the frequency of reviews is taken\ninto consideration during audit planning process.<\/p>\n\n\n\n

              The\ntechniques used to conduct the audit can also be standardized. Based on the\nsource of the data and it\u2019s usage, the auditors need to look at it, since management\nmay be making critical decisions based on it. Auditors rely on a structured\napproach to audit the data analytics process and reuses approaches that have\nworked well in different departments.<\/p>\n\n\n\n

              A\ntraditional approach applies to the controls recommended to address various\nfindings like: <\/p>\n\n\n\n

                \n
              • Input controls – completeness, accuracy, and\nreliability of the data; <\/li>\n\n\n\n
              • Processing controls – reconciliation of changes made\nto normalize\/filter the data; and <\/li>\n\n\n\n
              • Output controls – accuracy, based on inputs and\nprocesses. <\/li>\n<\/ul>\n\n\n\n

                For\ninstance, the Data Warehouse (DW) has teams of personnel dedicated to operating\nand maintaining data.DW is installed with defined routes from the sources of\ndata to the warehouse and from the warehouse to the end users. It is advised to\nverify:<\/p>\n\n\n\n

                  \n
                • Employees \/ users possess required expertise to ensure the completeness, accuracy, integrity, and security of the data.<\/li>\n\n\n\n
                • Evidence of documentation and communication of processes and controls pertaining to the use and security of data.<\/li>\n\n\n\n
                • Evidence of testing the operating and design effectiveness of controls pertaining to appropriate and relevant access and change management.<\/li>\n\n\n\n
                • Changes to the control environment and supporting databases are tracked and monitored.<\/li>\n\n\n\n
                • The analyses are supported by built-in quality and effectiveness checks to ensure they (and the data) mirror the changes and evolution of the business. <\/li>\n<\/ul>\n\n\n\n

                  In\nrelation to data analytics, certain personnel oriented controls are critical \u2013\nespecially management oversight and user education. If users are intended to be\ngiven flexibility for creating their own reports\/analysis, they must understand\nhow to use the tools correctly to evaluate the inputs and outputs. They must be\nable to validate the date to verify its completeness and accuracy using data\nand tools.<\/p>\n\n\n\n

                  Certain Finer Points<\/strong><\/p>\n\n\n\n

                  It\nis appropriate to use proven methodologies during the process of auditing the\nbusiness units’ data analytics use.However, as each auditdo present their own\nunique challenges \u2013 their subtleties must be recognized, understood, and\nresolved. <\/p>\n\n\n\n

                  Business\nunits perform data analyses in different shapes and forms, using different\nalgorithms and basing their analyses on different assumptions. Riskis there when\nthe internal auditor or the business unit itself incompletely or incorrectly\nunderstands or agrees on such foundational issues. Here certain concerns needs\nto be addressed: <\/p>\n\n\n\n

                    \n
                  • Validity of the assumptions<\/li>\n\n\n\n
                  • Perform integrity checks<\/li>\n\n\n\n
                  • Age of review of algorithm.<\/li>\n\n\n\n
                  • Influence of one data event on subsequent activity.<\/li>\n<\/ul>\n\n\n\n

                    (a). Internal auditors may commit a mistake if they do not validate key assumptions with facts (i.e., confirmation of key data points and the underlying assumptions) before continuing with testing. The auditor shall reach completely inaccurate conclusions by taking the wrong path early in testing. The root cause for the error in reporting mostly is – not sufficiently validating assumptions and initial results. The issue is a huge hit to the integrity of the testing and audit process.<\/p>\n\n\n\n

                    (b). Another challenge is in knowing exactly what to audit. On a micro level, the auditor may look at a specific department to understand the objectives of the deliverables\/reports, the sources of the data, and the distribution of the data. It is important to review the process undertaken to produce reports: how the data changes through the cycle and how the changes are accounted for. <\/p>\n\n\n\n

                    (c). Prioritization is important on a macro level. Every department <\/p>\n\n\n\n

                      \n
                    • Generate, use and analyze data to produce a result, <\/li>\n\n\n\n
                    • Has goals and objectives;<\/li>\n\n\n\n
                    • Report on how it performs against those goals.<\/li>\n<\/ul>\n\n\n\n

                      Auditors\nhave to work with the departments to identify reports used in management’s\ndecision-making process. That will help in understanding which activities to\nreview and why.<\/p>\n\n\n\n

                      (d). Internal audit is rendered ineffective, if its findings cannot be explained in a way that resonates with the business unit that has been audited. Internal auditors must consider the learning modalities of their audit clients when discussing the findings; people hear, see, and experience things differently. <\/p>\n\n\n\n

                      The auditor may find it convenient to submit a simple\nwritten, text-heavy report, it may be more effective to use visually appealing,\nconcise images in support of the text. A verbal presentation\u2009\u2014\u2009in support of\nthe written report\u2009\u2014\u2009that includes concrete examples of the findings or the\nrisks that may accompany the findings is also likely to make a more lasting\nimpression. This gives clients multiple ways to absorb and understand the\nrecommendations, based on the way they process information.<\/p>\n","protected":false},"excerpt":{"rendered":"

                      While data analytics serves as a powerful tool used in organization to gain insight into operations, they may also be a source of risk. Feasibility to reveal secrets hidden in voluminous data makes use of the data analytics difficult to resist. There are tools enabling the organization to bring efficiencies in operations, improve decision, enhance agility, identify market potential etc. […]<\/p>\n","protected":false},"author":3,"featured_media":361,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[17,14],"tags":[19,18,92,85],"class_list":["post-354","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-assurance","category-general","tag-audit","tag-internal-audit","tag-risk-assessment","tag-risk-management"],"yoast_head":"\nInternal Audit: Going Beyond the Data - Rnm India<\/title>\n<meta name=\"description\" content=\"Internal audit is not just about looking at data. 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