Data Analytics … Definitions and Stages

Data Analytics

Data Analytics … Definitions and Stages

‎The process whereby data is identified, consolidated and quality checked, and put into a format where analysis can ‎be done with the goal of identifying useful information that better supports corporate decision making.

‎Data analytics involves qualitative and quantitative methodologies and procedures to retrieve data out of ‎data sources and then inspect the data (in accordance with predetermined requirements) based on data type ‎to facilitate the decision-making process.‎

A data type specifies the type of value (i.e., text, number, picture, date, string, etc.) and the applicable ‎mathematical, relational, and non-relational or logical operation methodologies So that can be applied without resulting ‎in an error.‎ So Data is processed with analytic and algorithmic tools to reveal meaningful information.‎

‎For-profit entities, not-for-profit entities, and government agencies (federal, state, and local) utilize Data Analytics to ‎reach a conclusion based on evidence and reasoning to make well-supported decisions and formulate strong ‎business models. So Organizations can also use business analytics to rule out proposed strategic plans and ‎models which would not be beneficial or work for the organization.‎

‎Data Analytics utilizes to evaluate operational, financial, and other data to identify any deviations from ‎the norm (e.g., Anomaly detection, potential risks) and opportunities for enhancement or advancement.‎

Data analytics contains five stages as follows:‎

(1) Define questions

Identifying goals and objectives of what the organization is trying to achieve. So Key performance indicators (KPI) must be identified to assist with measuring whether an organization is progressing ‎towards its goals and objectives. Examples of KPIs include:

  • Current ratio.
  • Net profit margin.
  • Budget variance.
  • Debt to equity ratio.
  • Payment error rate.

Clearly defined goals and objectives assist the IT team in selecting, So the most appropriate technology source to use ‎for the analysis.‎ Early adoption of goals and KPIs helps keep the analysis on course and avoid worthless analysis.‎

(2) Obtain relevant data

Obtain relevant data (commonly referred to as information discovery), So Access to every piece of data available allows for: ‎

  • Valuable analysis.
  • More precise correlations.
  • Construction of meaningful analysis models and forecasts.
  • Identification of actionable insights.‎

(3) Clean/normalize data

  • Cleaning data consists of, but is not limited to, flushing out useless information and identifying missing data.‎
  • Data governance assists with ensuring data are accurate and usable.‎
  • Normalizing data involves storing each data element as few times as necessary. It results in a reduction in data and ‎strengthened data integrity for use of a specific purpose.
  • We accurately determine the data on which business events are based and correct any anomalies.

(4) Analyze data

When we analyze the data we have collected, we determine whether the data is the exact data required. So the definition includes, but is not limited to:

  • We assess whether we need additional data.
  • Collecting new and/or different data.‎
  • Revising the original question.
  • Formulating additional questions.‎

‎Data analytics methods in internal auditing include the following application types For example:‎

  • Descriptive analysis is the most basic and most used method. It concentrates on reporting actual results.‎
  • Diagnostic analysis provides insight into the reason certain results occurred.‎
  • Predictive analysis involves applying assumptions to data and predicting future results.‎
  • Prescriptive analysis concentrates on what an organization needs to do in order for the predicted future results to ‎actually occur.‎
  • Anomaly detection to identify unusual patterns or deviations from the rule or expected results.
  • Network analysis consists of analyzing network data and statistics to find patterns.‎
  • Text analysis involves the utilization of text mining and natural language algorithms to find patterns in unstructured ‎text.‎
The managers generally will select data to trace to supporting source documentation, So such as invoices, ‎contracts, and payments, and perform the following additional procedures:‎

  • Review and confirm the details of the data selected.‎
  • Analyze the findings and determine compliance or non-compliance with the policy.‎
  • Analyze the findings for accuracy.‎
  • Identify internal controls requiring enhancement or, if no controls exist, assist with the creation of a control.‎

(5) Communicate as results for Data Analytics

Prior to issuing the final communication, we should discuss conclusions and recommendations with ‎appropriate management at an exit meeting.‎

  • The discussion provides management with an opportunity for clarification and an expression of views.‎
  • The primary purpose of an exit meeting is to ensure the accuracy of the user information.‎

Data visualization or graphic illustrations (e.g., charts, graphs, network analysis, etc.), written repetition (e.g., summaries), and ‎itemized lists (bulleted or numbered) are good ways of emphasizing information. Using visual aids to support a discussion of ‎major points results in the most retention of information.‎ Language should be fact-based and neutral. But if the objective is to persuade an individual to ‎accept recommendations, words with strong or emotional connotations should use. So we use a very strong word or a word that is inappropriate for the specific recipient that may lead to an unwanted response.

Therefore, we choose carefully meaningful language to attract the specific recipient.

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