DELTASION.com – Technological developments are increasingly developing day by day, of course, they have an influence on all aspects of our lives, from education, work, and government, even the business sector is also experiencing the impact of technological developments. The development of technology has created a shift in how we carry out our activities as usual. If previously you needed to come to the library to get information, now you only need a smartphone connected to the internet network, so you can access hundreds or even thousands of information on the internet.
Technological developments have a direct impact on the aspects of people's lives. The presence of data is said to be as important as oil, making it a commodity that can be processed to produce the information needed. Big data implementation is one of the results of data collection which is currently being discussed by many parties. Big data implementation has been widely applied in various industrial sectors, such as the banking and agricultural industries.
This is no exception to the current business world which relies heavily on technology, for example, ERP (Enterprise Resource Planning) technology which aims to monitor stock availability in warehouses down to sales transactions. Behind all these technological advances, there is one important component that is the basis of everything, namely data. Data needs to be recognized as a very important asset at this time, data is even called ” new oil ” due to the value it has.
One application of data in the business world is usually called big data implementation, big data itself is a collection of data with a very large volume, larger than datasets or even single datasets. This big data needs to be processed further to produce the information needed by the business world. To be able to produce information based on big data, special software, and trained people are usually needed, such as data engineers, data analysts, big data engineers, big data architects, and so on.
On this occasion, we will discuss further the important components of big data, the big data implementation roadmap, as well as examples of the use of big data in everyday life.
Get to know the important components of Big Data Implementation
Important components of big data are:
Before it can produce information, big data requires a data source, namely data that has been or will be obtained in the data processing process. The data source itself is usually owned by each company and is used for data processing purposes, such as in the application of IoT (Internet of Things).
After getting the data sources, of course, the data needs a storage place before it can be processed further. The process of storing data sources is usually called a data lake, which is a data storage that can accommodate quite a lot of data, is a scalable unstructured database, and can store different file formats.
Batch processing is a process that waits for all raw data to be collected before the ETL ( extract, transform, load ) process can be carried out. In the batch processing process, capable software is usually needed to process it. Open-source Hadoop is a popular framework in the world of big data
Real-Time Message Ingestion
When the raw data has been collected, a process called ingestion will be carried out to produce the desired information. This ingestion process also requires special applications, such as Microsoft Azure Event Hub which can process data sources in real-time.
A data store is a place to store data that has gone through a query process and has been analyzed using business intelligence tools. There are many data store choices, but the most widely used are Amazon Redshift, Google BigQuery, and Snowflake.
General Application of Big Data Implementation
The general applications of big data are:
Must be in line with what the company wants
A big data implementation process will be successful if it suits what is needed for both individuals and companies. This is also important to pay attention to for everyone who will implement big data. It is also important to determine the targets to be achieved, the specifications of the implementation of big data itself, as well as the architecture at the company level that will implement big data.
Identify Data Sources
In producing quality information, it is not surprising that quality data is needed. This is what also needs to be considered in implementing big data implementation, identifying and categorizing data so that it can produce data with good quality standards.
Select API Services
Have you ever heard of the term API? If you are someone who is involved in the world of IT, then you should be familiar with the term API. API or Application Programming Interface in everyday language can be described as a server whose job is to deliver data access from one place to another, with the aim being that data transfer can run more efficiently.
In big data implementation, API also plays an important role and even becomes a critical component in it. APIs in big data implementation play a role in providing data sources, as well as in the data integration and visualization process. Apart from that, APIs also play a role in providing access to data storage which has an impact on the speed of data collection, data processing, and the results of data analysis itself.
Designing with a User-Friendly Interface
It cannot be denied that the existence of a user-friendly interface greatly influences the functionality of an application or program, this will certainly make it easier for users to understand the features available in it, including big data implementation. The result of big data implementation is information that can be used for certain purposes, therefore it is necessary to output information that can be accessed or understood by many parties, namely by displaying data in a visualization form that is easy to understand, for example in the form of a dashboard.
Avoid using the data silos concept
Data silos is a term used in data management, a silo is a storage place
commonly used on farms, why silos? Silos are synonymous with individual storage without any connection between other silos. When talking about big data, the use of data silos should be avoided because it can lead to inefficient data management and poor resulting data. Data storage in big data must be in one repository so that it is connected and becomes efficient in its management.
Implement Good Data Governance
Talking about data, the most important thing to know is the security of the data itself. Therefore, the implementation of big data must involve good governance, so that the data used is reliable and can provide the best output.
Stages in Implementing Big Data Implementation
Several stages in implementing big data implementation:
It is very important before implementing big data implementation to carry out a feasibility study so that the resulting output can meet needs. Carry out a process of analyzing the needs that a business wants to achieve, finding out what type of big data is suitable for the industry, as well as calculating costs and ROI ( return on investment ).
Big data implementation is a long-term project, so a feasibility study is needed to minimize existing risks. Also learn about the risks that will occur and how to handle them, ensuring that all stakeholders can work together so that big data implementation can run optimally.
After going through a feasibility study, now you have to determine the technical requirements needed to implement big data, starting from the type of data you want to use (Saas/images/video/SCM/etc.) as well as the hardware settings that must be met. Regarding data security, it must be planned so that the data stored and processed will be secure, for example by following security certifications such as HIPAA, PCI DSS, or GDPR).
Architectural design is also certainly needed in implementing big data, you need to prepare things related to the data model and database that will be used, because these two things have a direct impact on the continuity of the data process until it produces information. Also, plan related to data encryption and user access control so that you get a clear picture of who has access to existing data sets.
Development & Testing
After the entire series of plans has been agreed upon, now the application of big data will enter the development and testing stage. Here big data will be built based on the results of feasibility tests and technical requirements that have been collected. Afterwards, testing needs to be carried out to avoid
system errors/ bugs.
Big data implementation can be said to be ready for use if it has passed the testing stage. At this stage, it is necessary to prepare audit trails to track every use of the big data system itself and ensure that the big data
implementation process can run with the current IT infrastructure. An important stage is the need to provide training to users so they can operate the big data system correctly.
The final stage of a big data implementation process is support, namely the process of carrying out maintenance and safeguarding of the system to avoid existing risks, as well as carrying out updates if there is the latest technology to be implemented.
Real Examples of Big Data Implementation
The use of big data in taxation has the potential to increase tax revenues from a country, this is because big data can track what people have, and how many bank account numbers they have, and can see the types of taxes that have been paid and which have not been paid.
Indonesia as an agricultural country has enormous agricultural potential, this can of course be optimized by using big data, because with big data, farmers will be helped in analyzing weather conditions, soil quality, plant fertility, and others.