Implementation of Big Data in Business

Although the term big data has often been heard and spoken, many of us are still wondering: What is meant by big data? What are the uses of big data? What are big data technologies? Why is big data needed in various fields?

What is Big Data?

There is no standard definition of big data. Broadly speaking, big data is a collection of data that has a very large amount or complex structure so that traditional data processing technologies can no longer handle it properly. Currently the term big data is also often used to refer to fields of science or technology related to the processing and utilization of that data.

The most important aspect of big data is actually not just how much data can be stored and processed, but what use or added value can be obtained from the data. If we can't extract the added value, then the data will just be useless garbage. This added value can be used for various things, such as improving operational fluency, accuracy of sales, improving service quality, market predictions or projections, and so on.

In the field of informatics there is a term "Garbage in Garbage out" or garbage input will produce garbage output as well. The point is that if the input we provide to the system is of low quality input, then the quality of the output will of course be low as well. The input referred to here is data.

For this reason, ensuring the quality of input and output in every stage of data processing to get a quality final output is a must in the implementation of big data.

Big Data Analytics

When we talk about big data, we usually mean big data analytics. This is quite natural, because when a big data project starts, of course the end result is expected to get useful insights, which can help decision making.

Data Analytics itself is a series of processes for extracting information or insights from data sets. The information can be in the form of patterns, correlations, trends, and so on. Data analytics often involve quite complex data processing techniques and algorithms such as data mining and statistical calculations.

In Big Data Analytics, the level of difficulty is even greater because the processed data is obtained from various sources with different shapes and types, and with great size and speed. Therefore, Big Data Analytics uses more advanced techniques and algorithms such as predictive models and machine learning to see trends, patterns, correlations and other insights.

In general, big data analytics is divided into 4 categories, namely:

Descriptive Analytics

This analysis is used to answer questions about what is going on. Almost all organizations have implemented this type of analysis.

Diagnostic Analytics

After knowing what happened, usually the next question is why it happened. This type of analysis uses drill-down data to find more in-depth reasons for what is going on.

Predictive Analytics

Predictive analysis provides predictions about what will happen based on existing data. This type of analysis uses machine learning and artificial intelligence techniques and algorithms to generate predictive models based on historical data.

Prescriptive Analytics

Utilizing descriptive and predictive analysis, this type of analysis provides insight to be able to obtain results that are in accordance with what has been predicted.

Implementation of Big Data in Business

Human habits and business competition in today's increasingly open era make making the right decisions the key to staying in business. Data is one of the determinants of success in decision making.

Customer Profiling

Patterns and customer profiles can be studied through data created by customers when interacting with products, either directly, through the website or using applications. Currently, customer profile data can be expanded further to include geolocation information, and even social media data that they create.

The more data collected, and the more sophisticated the data processing process, the more accurate and detailed information about customer profiles can be obtained. Manufacturers or service providers can provide appropriate recommendations to customers so as to increase sales and customer loyalty.

Product Development

Building a product from an idea that is ultimately well received by the market is a challenge. Big data can provide deep insights

to identify market needs, see customer responses through comments on forums or social media, evaluate product sales performance in the market quickly, optimize distribution chains, to optimize marketing strategies.

The better the data management and the faster availability will be able to continue to create sustainable products that provide good value for customers and users.

Price Optimization

Price can be the key for customers to determine which products to buy. However, the price war can also have a bad effect on the product itself. Big data can provide maps and price patterns that exist in the market, so that producers can determine optimal prices and price promotions according to market needs.

Big Data for Telecommunications

Telecommunications is one sector that inevitably has to deal with big data. Moreover, nowadays telecommunication services are arguably the heart of our digital world. If data is often referred to as 'the new oil', then the telecommunications service provider is like having a very productive oil mine.

There are many sources of data that exist in a telecommunications company. Call it network operational data, conversational transaction data, internet connection data, customer data, and product data. If all of these data can be integrated properly, it will be able to provide insight that can be used for network optimization, improving services, making products and promotional programs, and increasing customer loyalty.

Big Data for Health

Data in the health sector is one example of big data because of its volume, complexity, diversity and demands for timeliness. Besides that, health services also involve various parties, including various hospitals, labs, clinics, and health insurance. Therefore, the health sector is a sector that has big challenges in the field of big data.

Data integration, data accuracy and data acquisition speed are very important in the health sector, because it involves patient safety. Not only that, the number of medical personnel and hospitals is still very less compared to the potential of patients, especially during a pandemic like today. Insights obtained through big data can be used to help overcome these problems, including for more accurate diagnosis, personalization of medicines, improvement of hospital services to optimization of hospital operations.

Artificial Intelligence (Artificial Intelligence) and Big Data

After the implementation of big data in terms of data management and analysis can be done well, the next challenge is how with that data we can train machines to learn so that they can work and provide insight automatically, quickly and accurately. So Artificial Intelligence, Machine Learning and Deep Learning reappear and become a new trend today. So what is the difference between AI, machine learning and deep learning? In terms of scope, deep learning is part of machine learning, and machine learning is part of artificial intelligence. The third point is how to make machines or computers smart. The main goal is to reduce human intervention in providing insight or in performing various human jobs.