Big data is now familiar to software developers with a wide project scale. The use of databases is needed to be able to manage, store, manage all information in the form of structured and systematic data. Many large companies require very large data capacity to store data related to the company. For small-scale projects, it […]
Big data is now familiar to software developers with a wide project scale. The use of databases is needed to be able to manage, store, manage all information in the form of structured and systematic data. Many large companies require very large data capacity to store data related to the company.
For small-scale projects, it is generally sufficient to use the help of open source databases such as MySQL, PostGre, MariaDB, and others. However, for software needs that accommodate various types of data, it can result in the data handling process being slow and less effective. The best way to deal with this problem is to use big data.
Big Data Analytics Functions
Big data has several important functions in the process of developing and perfecting an application. The following are some of the functions related to big data:
a. Can determine the cause of a problem, failure in real time
The first function of big data is to determine and analyze the cause of a problem that occurs in the system. Then, with its current use, it can also minimize the occurrence of failures in the data storage process. The results of the analysis can be displayed in real-time.
b. Making a smart and right decision
Big data can also be combined with intelligent technology systems and devices such as IoT (Internet of Things) and AI (Artificial Intelligence). Its job is to provide and store the data and information needed in the development of a product. For example, a smart city or a smart city that uses artificial intelligence and large-scale internet networks that are able to connect every corner of the city, buildings, and other supporting infrastructure.
c. Detect an anomaly or deviant behavior in your business structure
The third function is being able to detect quickly and precisely, the form or process of activities that deviate and stop because of technical and non-technical errors. Big data can also plan several options to reduce and resolve these anomalies more quickly to help your company or organization’s business activities.
Benefits of Big Data in the Business World
Improve business operational systems
To increase the productivity and effectiveness of the business you are starting, of course you need adequate resources. One of them will be data needs that continue to increase. Big data can solve data problems with great needs to help your business operational processes.
Customer Relationship Management (CRM)
You need to maintain and improve good relationships with customers and sales. By doing management using several additional features to assist you in monitoring sales activities, calculating average conversions, and so on.
Optimize app usage experience
The use of mobile devices continues to increase, so there is a need for optimization in terms of software and hardware. In addition, data storage is also very influential on the optimization of an application. With big data, the process of data transfer and management runs faster and more accurately.
Benefits of Big Data in the World of Information Technology
Almost everyone uses what is called social media to access various information and share personal daily activities. Of course, many upload photos, videos and text into these social media applications. All of this information is a type of data that will be recorded and stored in a database system with a large capacity.
You can imagine how much size should be allocated by social media such as Facebook, Twitter, Instagram, etc. To collect data every day. The best solution to overcome this problem is to use big data that has good performance in handling large-scale data.
One of the benefits of smart devices is that they can help human activities more effectively and efficiently.
An example of a smart device is IoT technology which is currently widely applied to electronic devices such as refrigerators, washing machines, air conditioners, and so on. By using a system that has been integrated with the internet network, all forms of activity can be coordinated in one application system only with the help of big data as a provider of information services and data storage.
Furthermore, big data also affects the use of digital media. For example, the use of features on websites and streaming applications such as spotify and netflix. In the database system they use, they are able to record data on music, movies that you have watched and provide recommendations for you.
With the help of AI technology itself, databases can be integrated well and quickly to make it easier to use the application. Another example of the application of digital media is the feature in e-commerce that has implemented AI with big data to make it easier for users to provide product recommendations.
Examples of Application of Artificial Intelligence in Journalism
The rapid changes in technology have forced various industries to continue to adapt, including the media industry, especially in the field of journalism. Artificial Intelligence is one of the strengths that cannot be ignored by actors in the field of journalism.
The role of Artificial Intelligence (AI) in various fields has been found all around us. Starting from detecting credit card abuse, determining what social media will show to its users, and displaying ads according to the situation and conditions of the audience. How is AI applied by media organizations, especially in the journalistic area?
This example of AI application is the result of a Knight Foundation survey that tried to photograph the application of AI in media organizations with the aim of understanding the landscape and identifying the potential for funding AI projects for media organizations in the future. This mapping is considered important because Artificial Intelligence for journalism is like a “superfood”, as stated by Paul Cheung, Director for Journalism, Knight Foundation.
This survey collected 130 AI projects carried out by global and local media organizations from various countries around the world. This survey is focused on projects implemented in the last three years. These projects are derived from the Knight Foundation’s own data as well as interviews, engagements with journalistic technology networks, examples presented at conferences, research on the topic of this survey, and others.
Focus On Increasing Coverage
The survey results reveal that half (47%) of these projects use AI to “enhance reporting/reporting capacity”, for example sorting information from large numbers of documents with machine learning, detecting breaking news on social media, and extracting COVID-19 data from government-owned website.
Another significant AI application in journalism is aimed at reducing variable costs (27%). Examples are AI tools for automating the transcription process, embedding tags in images and videos, and story generation.
In the third position, examples of AI applications that are quite a lot in journalism are recommendation engines, dynamic paywalls, and digitization of news archives aimed at optimizing revenue (12%).
Not just observing the main goal of these AI projects. This survey also tries to capture the position of AI in the news pipeline or news production process. Overall, examples of applying AI in the news creation process at many points, from news gathering, to product development, customer (customer) acquisition and retention.
However, the survey proves that the most significant examples of AI application in newsrooms are still on the news gathering side (39%), automated news generation (13%), and news production (10%).
From the survey, there are four main areas where AI is applied in journalism and media organizations:
- Increased news production (e.g. by automating the process of news creation or auto journalism, content curation, social media monitoring, and transcription).
- Mining customer data to improve audience engagement and monetization (e.g. content personalization and customer management).
- Business intelligence (e.g. reduced infrastructure costs and operational efficiencies).
- Creating new methods to improve investigative data and coverage (e.g. through sentiment analysis, pattern recognition, and data analysis).
While the role of AI on the product and business side in the journalism ecosystem is not significant enough, with the percentage of AI projects touching product development at 6%. Similarly, the proportion of AI for the purposes of customer management (customer management) and paywall optimization (paywall optimization).