Hi ! I only need the keypoint for me put in powerpoint

MAIN FINDINGS AND DISCUSSION

Big Data depend on volume, velocity and variety (Walsh, 2019). For example, Juan Zhang, Xiongsheng Yang, and Deniz Appelbaum describe big data with four "Vs": massive volume, high velocity, large variety, and uncertain veracity ("Toward Effective Big Data Analysis in Continuous Auditing," Accounting Horizons, June 2015, http://bit.ly/2p9GtOm). A relative definition is also what is considered "big." A collection of data can be considered high if the information system is capable or is not able to execute the task (Miklos Vasarhelyi, Alexander Kogan, and Brad M. Tuttle, "Big Data in Accounting: An Overview," Accounting Horizons, June 2015, http://bit.ly/2pcqqQJ). Like we know, to integrate big data and big data analytics which we need a huge volume of data filtering in real time from a number of places and once data is collected, it is organised data like a table or unstructured data such as text, pictures or binary programming. For examples, Big data include the number of ad views, the customer service telephone call data or an entire patient's medical history (Tang & E. Karim, 2017).

There is a lot of information in the data sets. The details will remains useless, without data analytics. Analysis of the results also can be predictive or prescriptive. According to Dagiliene, L. and Kloviene, L. (2019) , both for social science, accounting and auditing sectors, which are seen as promising and demanding, the implementation is Big Data Analytics intensive functionally. What is predictive ? Predictive analytics make a "best guess" on what will be next by simulation, master-learning and data mining. For examples, companies can use predictive analysis to improve their business risk analysis. However, prescriptive analysis will allow for priority behaviour and decision-making by the company. For example, the company use detailed analytics to define firstly improved financial reporting controls. If the company using all of these analysis, the company will identify risks in better way and develop plans for their management.

Referring to AICPA, the internal auditing process need to include reports in support of the auditing requirements of the AICPA because in that ways the companies will strengthen the quality of their audit evidence. For example, a Sarbanes-Oxley Act 2002 (SOX) company has to give its external auditors audit documentation justifying their ability to retain effective financial reporting controls. As companies use a number of built-in SaaS platforms, however, cyber-safety surveillance of the environment and ongoing reporting are required to prove governance. SOX compliance also requires (Walsh, June 2019).

The combination of traditional data and big data will be done when the big data integration happen and it is most important step to integrate big data into audits because of that two data most important data for audit procedures while it is given different information. Traditional accounting data are primarily quantitative and organized, big data often contain unstructured and semi-structured data, which provide more supporting data and information. Because of its difficulty, auditors also have to get different kinds of evidence (Tang & E. Karim, 2017).

Data analysis also can allow auditors to deliver risk-related information more proactively on a timely basis. If information is generated and properly delivered on major risk operations, action can be taken to deal with these problems. This will be achieved with data analytics before normal processes are in order and processing must not be disturbed regularly. Data analysis helps predictive analysis by auditors to recognise patterns of data and to recognize data which do not follow planned patterns.

According to Wang & Cuthberston, 2015, big data analytics may be used to prove whether control procedures work as planned. Data analytics may be used for example, to verify authorization, run limit experiments and review duties segregation. The companies need to know more about big data that will be use in their audit report. The continuous monitoring and on-going security programmes of the company allow a strong cyber-security programme to protect themselves against fraud (Walsh, March 2019).

Increased data connectivity and manipulation and the reasonable use of data analytics tools should improve audit quality and efficiency by:

1. Enhanced market awareness through a detailed review of a company's statistics and use of graphic outputs like monitoring systems instead of text or numerical information helps customers to better identify trends and cycles of business.

2. This increased knowledge of risk allows users to recognise risk associated with a customer and enables tests to be more centred on those fields. This is further strengthened by encouraging users to invest more time on risk areas through reviewing routine results.

3. Improved consistency across group audits, whether outside or internal, with all auditors using the same technologies and procedure, allowing group auditors to perform separate component auditing tools and to do group-wide monitoring. Reasonable approval of all component businesses is required but allowing for a more integrated approach to group auditing.

4. Better output by using computer systems to process large amounts of data very quickly and to analyse them, save time during the audit and enable users to spend more energy on judgmental and risk areas. In certain cases, data analyses are used to evaluate even larger samples and coverage of audit processes can be increased, the chance of sampling reduced or removed.

5. For example, sensitivity analysis on management assumptions may be more easily handled by users as part of testing.

6. increased prevention of fraud by the opportunity to analyse all data and to test customs segregation

7. Data analytics can share information with users by adding value and giving the management real advantages when they can be presented with valuable information from another viewpoint. The information may be shared with users.