Big Data Analytics

Summary

The video presents an innovative proposal for the Ministry of Transportation's Inspectorate General, focusing on the implementation of big data analytics. It highlights how this approach aims to enhance auditing processes by analyzing large, complex datasets from within and outside the Ministry. The proposal includes machine learning programming created by auditors to visualize potential risks, improving decision-making and the identification of problems early. The process involves various stages, from data collection to cleaning, programming, and visualization. Key benefits include better risk management, inventory control, and policy development, all stemming from close collaboration between auditors and auditees.

Takeaways

• 😀 Big Data Analytics is a process that deals with extracting and analyzing large or complex data sets that cannot be handled by regular data processing software.
• 😀 Data Analysis involves examining, cleaning, transforming, and modeling data to find useful information and support decision-making.
• 😀 The introduction of Big Data Analytics at the Ministry of Transportation's Inspectorate General is an innovation aimed at helping auditors process and analyze electronic data related to business processes within the Ministry.
• 😀 Machine learning programming, created independently by auditors, will be used to analyze relevant electronic data and generate insights.
• 😀 The goal is to provide visualizations that will inform leadership about potential risks or problems in activities at an early stage.
• 😀 The workflow involves six key steps: 1) Data Identification, 2) Data Collection, 3) Data Engineering, 4) Data Modeling with Machine Learning, 5) Visualization, and 6) Automation.
• 😀 The first step, Data Identification, requires auditors to collaborate and determine the relevant data objects.
• 😀 In Data Collection, auditors communicate with auditees to collect the necessary data for the analysis.
• 😀 Data Engineering focuses on cleaning and organizing the data to make it usable for analysis.
• 😀 In the Data Modeling step, auditors use machine learning programming to analyze data according to specific theories or their past experiences.
• 😀 The final step, Automation, aims to make the program run automatically and continuously to ensure ongoing effectiveness.
• 😀 The benefits of implementing Big Data Analytics include early identification of risks, optimized inventory control, and providing valuable insights for supervisory decision-making.
• 😀 The success of the Big Data Analytics initiative is attributed to the strong collaboration between auditors and auditees within the Ministry.

Q & A

What is Big Data as described in the script?

Big Data refers to the field that deals with extracting and analyzing information from large or complex data sets that cannot be processed by traditional data management software.

What is Data Analysis, according to the script?

Data Analysis is the process of examining, cleaning, transforming, and modeling data to find useful information and support decision-making.

How is Big Data Analytics applied within the Ministry of Transportation's Inspectorate General?

Big Data Analytics is applied to innovate the auditing process by processing electronic data related to business processes within the Ministry of Transportation and other relevant external data.

What role does machine learning programming play in the audit process?

Machine learning programming is created by auditors to analyze data and provide visualized insights to senior leadership, identifying potential risks or issues early on.

What is the expected outcome of using machine learning programming in the auditing process?

The expected outcome is to generate visual insights for leadership, providing early warnings about potential risks or activities that might require attention.

What are the key stages in the Big Data Analytics process as described in the script?

The stages include: 1. Tipe Legend (data object selection), 2. Senjata of the Hai Gimana (communication with auditees), 3. Data Engineering (data cleaning and structuring), 4. Data Relation Gimana (machine learning programming), 5. Visualization (creating visual outputs and presentations), and 6. Shinobi Thor (ensuring automatic and sustainable program execution).

What is the purpose of the 'Tipe Legend' stage?

The 'Tipe Legend' stage involves the audit team discussing and selecting the data objects that will be analyzed.

What happens during the 'Data Engineering' stage?

In the 'Data Engineering' stage, the audit team cleans and structures the raw data into a usable format for analysis.

What does 'Shinobi Thor' refer to in the context of the Big Data Analytics process?

'Shinobi Thor' refers to the final stage where the program is made to run automatically and continuously for long-term use.

What are the key benefits of Big Data Analytics for the auditees?

The key benefits include early identification of potential risks or issues, enhanced inventory control for work units, and providing insights for policy and supervisory decision-making.