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Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The main steps in forensic analytics are data collection, data preparation, data analysis, and reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
Analytical procedures include comparison of financial information (data in financial statement) with prior periods, budgets, forecasts, similar industries and so on. It also includes consideration of predictable relationships, such as gross profit to sales, payroll costs to employees, and financial information and non-financial information, for examples the CEO's reports and the industry news.
Data preparation is the first step in data analytics projects and can include many discrete tasks such as loading data or data ingestion, data fusion, data cleaning, data augmentation, and data delivery. [2] The issues to be dealt with fall into two main categories:
The definition of an operational analytics processing engine (OPAP) [8] can be expressed in the form of the following six propositions: Complex queries: Support for queries like inner & outer joins, aggregations, sorting, relevance, etc. Low data latency: An update to any data record is visible in query results in under than a few seconds.
Today, forensic accountants work closely with data analytics to dig through complex financial records. Data collection is an important aspect of forensic accounting because proper analysis requires data that is sufficient and reliable. [24] Once a forensic accountant has access to the relevant data, analytic techniques are applied.
For example, a conventional accounting asset such as goodwill is not an REA resource. There is a separate REA model for each business process in the company. A business process roughly corresponds to a functional department, or a function in Michael Porter's value chain. Examples of business processes would be sales, purchases, conversion or ...