Analytics

Big Data Analytics: Definition, Steps, and How It Works

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Data has now become an integral part of the business world. As we know, most companies are currently analyzing and processing large amounts of data (big data) in each of their business processes, along with increasing digital transformation. This big data management and analysis will later play an important role in the company’s decision making.

Big data itself is a term to refer to large amounts of data that exceed the processing capacity of conventional databases. This is because the volume of data is huge and beyond the capacity of traditional database architectures.

In fact, there must be a certain management so that data can be useful for business growth. This management includes the process of collecting and storing data (consumption), sorting (observing), improving (enriching), mapping (mapping), to analyze it to obtain results that are useful to the company.

We can call this entire process by the term Big Data Analytics or Big Data Analytics.

What is Big Data Analytics?

Data analysis is certainly an important part of all businesses. Because with this data, companies can use it as a reference for business decisions, formulating business strategies and future business plans, to design more effective marketing and sales strategies.

Along with the development of the business, of course, the data that this company has is still increasing. At a certain point, the data will be large, so it is usually difficult to process it. This is what we call big data.

Therefore, big data analysis or big data analysis is the whole process of collecting, sorting, refining and analyzing large amount of data. Big data analysis provides many benefits to the company and the target market as a whole.

An example is the integration of data that characterizes interest trends in sales leads (prospects). For example, when a lead advertises itself on social media like Instagram ads.

It is not impossible if the ad matches his preferences. For example, when he looks for references to contemporary fashion that is currently viral. Therefore, it will benefit both parties, both the customer and the company.

In addition, several other benefits of big data analytics include:

  • Saving time and energy due to the automation of business processes
  • It can reduce total production costs (cost of goods) and help the sales forecasting process
  • Help define market direction more precisely
  • Accelerating decision-making processes in the company

Big data analysis steps

After knowing the definition and benefits of big data analytics, now you need to understand the steps and steps involved in implementing it. Starting with Payumoney, there are six steps to implementing big data analytics. Each of these steps is explained below:

Data analysis

The first step is data mining. In fact, there are two things that are the focus of big data analysis, namely data mining and data mining. Data mining is the process of identifying data based on valuable insights or information from databases.

Meanwhile, data mining is the process of gathering data from web pages into a database. In general, data mining is a process that companies use more than data extraction.

Collecting data

The next step is data collection or data collection. This is a very important process, as a company’s data will continue to grow and accumulate over time. This data set is to provide accurate information required by the company.

data store

The next step is to save the data. Because when storing large amounts of data, you certainly can’t do it carelessly. You need good storage or storage space and provide an efficient infrastructure.

There are many software that companies can use to store large-scale data. Some examples are Hadoop, Cloudera and Talend.

Data cleanup

Not all data you collect and store is useful data. This is why you need to perform a data wipe or data wipe process. In fact, out of 100% of the data you collect through market research on social media and the internet, there is about 30-40% false data that is useless.

So, you need to filter or clear the data first. The goal is to facilitate the analysis process.

data analysis

The core of the big data analysis process is of course the data analysis itself. When analyzing large amounts of data, the data analyst must find patterns to determine customer needs. For example, market orientation or market needs and demands.

data consumption

The final stage of big data analysis is data consumption. In general, this process varies depending on the industry and the company’s business position within it. For example, the data consumption needs of retail companies will be different from those of companies operating in the food sector.

How Big Data Analytics Works

To understand how big data analytics work, companies usually combine several programs or software to obtain optimal results. Among others:

Learning the machine

The first is machine learning. To collect data, companies need artificial intelligence-based machines such as search engines. This engine quickly searches and learns the data you retrieve and defines the data more easily and accurately.

Data management

Next is data management. Its purpose is to check the data and ensure its accuracy. This is very important so that the data used is of high quality and not fake data.

Data analysis

With data mining, data analysts can go into various data, flag things that are important, and construct the data as a solution to influence the company’s decision-making process.

Hadoop

It is a technology that is useful for storing large amounts of data. Hadoop itself is an open source software that companies can use to quickly transfer and visualize data.

In-memory analysis

As the name suggests, in-memory analytics uses in-memory technology. so that data analysts can quickly obtain insights. In addition to the ability to quickly analyze data, in-memory analytics can also create new algorithms, create new models, and eliminate erroneous analyses. Therefore, this technology can create different scenarios for assessment and teaching materials.

Predictive analytics

This predictive analytics uses statistical algorithms, data and machine learning to generate data based on usage history. Its function is to generate predictions of what will happen in the future so that companies can make more profitable business plans.

Text extraction

This technology helps data analysts analyze data from web posts, comment fields, books, and more based on text. In general, companies use text mining on blogs, social media (Twitter, Facebook, Instagram, etc.), surveys, and even emails to find viral topics that can be used as marketing communication tools.

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