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- Big Data allows you to make more informed and accurate decisions based on data<\/li>\n
- Business planning, by knowing customer behavior<\/li>\n
- Knowing market trends and consumer desires<\/li>\n<\/ul>\n
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How Big Data Works<\/h2>\n
Big data works by collecting, storing, and analyzing enormous and complex data to find patterns and trends not seen with traditional data analysis. This is done using technology capable of handling data on a large scale, such as cluster computing and distributed storage. Then, data analysis algorithms such as machine learning and statistics are used to extract useful information from that data.<\/p>\n
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Data Collection<\/h4>\n
Big data can come from a variety of sources, such as transaction systems, application logs, sensors, social media, and more. Then the data is collected using technologies such as web scraping, APIs, and other data collection tools. Data can be collected in real-time or over a longer period of time. The collected data can be stored in various formats, such as databases, files, and data lakes. Data storage must be done with technology capable of handling data at scale. Data collection must be carried out in a secure manner and in accordance with applicable data regulations.<\/p>\n
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Data Accessed<\/h4>\n
Big data must be managed properly in terms of access rights. Some data may only be accessible to certain people or may only be used for specific purposes. Users must pass the authentication process before they can access the data. This process is usually done using a username and password or other authentication methods such as tokens or encryption keys. After passing the authentication process, the user must obtain permission to access the specified data. This process is carried out by checking the user’s access rights on the system. Data access to big data must be done in a secure manner and in accordance with applicable data regulations. The auditing process is used to record user activities performed on data, such as access, modify, and delete data.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.11.3″ _module_preset=”default” border_width_bottom=”2px” border_color_bottom=”#343434″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.11.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.indosplash.com\/wp-content\/uploads\/2023\/01\/Web-Server-Data-Data-Center-Browser-Report.png” title_text=”Analisa Data” disabled_on=”on|on|off” _builder_version=”4.11.3″ _module_preset=”default” width=”20%” module_alignment=”left” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/www.indosplash.com\/wp-content\/uploads\/2023\/01\/Web-Server-Data-Data-Center-Browser-Report.png” title_text=”Analisa Data” align=”center” disabled_on=”off|off|on” _builder_version=”4.11.3″ _module_preset=”default” width=”50%” module_alignment=”left” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.11.3″ _module_preset=”default” text_text_color=”#343434″ header_4_text_color=”#343434″ global_colors_info=”{}”]<\/p>\n
Data Analysis<\/h4>\n
The results of managing the data that has been processed. The analysis carried out can be in the form of descriptive (data depiction), diagnostic (looking for cause and effect based on data), predictive (predicting future events) or prescriptive analytics (recommending the choices and implications of each choice). From the results of this analysis, it will be a reference for policy or strategy making in the future.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.11.3″ _module_preset=”default” border_color_all=”RGBA(0,0,0,0)” border_style_all=”none” border_width_bottom=”2px” border_color_bottom=”#343434″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.11.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.indosplash.com\/wp-content\/uploads\/2023\/01\/Admin-Service-Server-Computer-Analytics.png” title_text=”Aplikasi Analisa Data” disabled_on=”on|on|off” _builder_version=”4.11.3″ _module_preset=”default” width=”20%” module_alignment=”left” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/www.indosplash.com\/wp-content\/uploads\/2023\/01\/Admin-Service-Server-Computer-Analytics.png” title_text=”Aplikasi Analisa Data” align=”center” disabled_on=”off|off|on” _builder_version=”4.11.3″ _module_preset=”default” width=”50%” module_alignment=”left” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.11.3″ _module_preset=”default” text_text_color=”#343434″ header_4_text_color=”#343434″ global_colors_info=”{}”]<\/p>\n
Data Analysis Applications<\/h4>\n
After identifying the problem, it is a process to determine what you want to look for from the data and how the results of the analysis can be used to solve the problem. It then applies the results of the analysis into a model that can be used to make predictions or make decisions. Models can be created using algorithms such as regression, classification, or clustering. Before the implementation of the model, it must be validated, which is a process to evaluate the quality of the model created by using data that was not used when creating the model. Model validation is used to find out how well the model can be generalized to new data. It then implements the model into the system or application used by the end user. Model implementation can be done using technologies such as APIs or software libraries. And continuous monitoring and maintenance is carried out to monitor the performance of the model and make improvements if needed.<\/p>\n
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