What Is Big Data Analytics – In various activities, data analysis can be used and has a positive effect. For example, if the village head wants to hold an independence event on August 17, if he has data on average residents, the Augustan event can be adjusted to suit the residents’ tastes. Starting from the competitions that will be held, the entertainment stage and joint dining parties will be more smoothly managed. So you need a lot of data, right? Big data is one of the most discussed things, as well as big data analytics.
The need for data with the context above is a small form in implementing its use, now how about describing data in bigger terms. Imagine the Indonesian government wants to implement new regulations for society. Without detailed community data, these regulations could be far off the mark. This is where the role of Big Data Analytics becomes very important.
What is Big Data Analytics?
The meaning of Big Data Analytics is the concept of processing large amounts of data. Usually the amount of data is seen in digital memory capacity. In Big Data analysis, this amount of memory can reach tens of terabytes for one analysis requirement. If the analysis carried out involves several stages, the amount of data will increase significantly.
Due to the difficulty of processing large amounts of data, the use of technology is becoming more common. Before there was help from the technological side, processing big data (Big Data) was completely done manually. As a result, the process is slow and can take several years. This can be clearly seen from the government data collection process if you want to see concrete examples.
The government will delegate lower-level regional heads to collect community data. The regional head’s superior will collect the data and aggregate it per region. From high-level regional heads, data will be taken by the relevant ministry and the analysis process begins.
This manual process is certainly not possible for ordinary companies. Only large companies that can pay high salaries for research teams and data collectors will carry out this Big Data analysis. For ordinary and small companies, the analysis they collect is usually with smaller data sizes.
Fortunately, technology is now getting better. Data can be processed automatically by coding, retrieval can be done via the internet and the analysis process is also more detailed thanks to AI. So it is not surprising that many startup companies can carry out Big Data analysis more easily in the current era.
Big data in the modern era usually involves human behavior by monitoring habits. For example, expenditure data, data on where people live, data on how long people work and even posting trends on social media can be data for analysis.
If the number of individuals whose data is taken reaches hundreds of millions, you can see partners, patterns and a clear picture of the market. For businesses, this can be a source of inspiration and data for developing effective business strategies.
Types of Big Data Analytics
For certain needs, you will collect different data and carry out different processing too. This is also the same as Big Data analysis. In this section, let’s discuss the following 4 commonly used types of Big Data Analytics:
1. Descriptive Analytics
Descriptive Analytics is a Big Data analysis technique that is useful for uncovering target market behavior patterns. This analysis model makes the data look simple and easy to read. Average analysis uses data over a long period that periodically changes over time. From these changes, you can see changes in data patterns and draw conclusions from there.
Although it is more often used to look for trends with past data, this analysis model is also able to provide more detailed information. Because of its detailed form, the information used can help prepare predictions of company profits, profit levels and sales expectations.
Examples of this analysis model are summary statistics, clustering and market basket analysis which are based on certain rule associations. For a concrete example of the use of this analysis, please see Dow Chemical Company. In short, this company uses past operating data to plan facility use. This is what makes Dow Chemical Company’s offices and labs more efficient every year.
2. Diagnostic Analytics
As the name suggests, Diagnostic Analytics is a model that attempts to diagnose problems. The model aims to collect detailed data about the causes of a problem. If the cause can be discovered, you can definitely avoid it in the future.
Data researchers will use techniques such as drill-down, data mining, data recovery, churn reason analysis and customer scoring analysis to explore these causes. In the business world, this analysis is generally used to find reasons why customers leave or no longer buy products. If you find the cause, a new strategy can be developed to prevent the cause and retain loyal customers.
3. Predictive Analytics
This type of Big Data Analytics is more used for analyzing future events. Using existing data, someone can make accurate predictions about the future. Especially if the data is specific and there are a lot of them. Generally, these two factors are enough to predict something accurately.
This model is more often used for business and economic needs. Using past data, patterns will be visible. From this pattern, variables will be calculated and adjustments made. As a result, the analysis will show a picture of future conditions with a certain amount of accuracy. If the accuracy is high, the results can be used for decision making and certain preparations.
An example of this is PayPal which uses funds usage data from individuals to create a picture of patterns. So if in the future there are expenses that don’t fit that pattern, PayPal can be alerted to fund leaks or account theft.
4. Prescriptive Analytics
This analysis model is the most complex and least frequently used. The problem is, this Big Data Analytics model uses Descriptive Analytics combined with Predictive Analytics. Apart from trying to see future situations with past data, Prescriptive Analytics tries to analyze decision making to deal with those conditions. This decision making will use patterns from Descriptive Analytics.
The data used is also larger. Not only internal data (profit size, sales) but also external data (social media insight and brand perception) will be used. From here, the data will be more difficult to process. However, the results will be very useful in making the best decisions.
Why Use Big Data Analytics?
The benefits of Big Data Analytics can be said to be many times greater than ordinary business analysis. Using technological capabilities, data from various sources can be obtained in large quantities. In statistical studies, the more variables and amount of data involved, the stronger the accuracy of the analysis. Using the results of this accurate analysis, various benefits can definitely be felt. Let’s look at the various benefits in the following sections:
1. Helps Manage Risk
Using accurate analysis of Big Data, companies can run business with little risk. Every time they want to make a decision, companies can read the results of Predictive Analytics to read future situations. From here, companies can make decisions to avoid or take advantage of the situation.
Companies that can arrange decisions according to accurate predictions will definitely not experience failure. The risk is still there. Every predictive analysis must have an error rate, even if it’s only under 1%. Even so, it is better than making business decisions without solid data. Reducing risk can increase a company’s ability to generate profits and grow faster.
2. Become a source of new product development
Product innovation can also be done using Big Data. For example, when the Indofood brand wanted to create a new instant noodle flavor. They will analyze what the market is trending and popular in the community. From this trend, unique flavors such as chicken geprek noodles are produced.
This taste comes from the culinary popularity of geprek chicken among the people of that era. In addition, the trend of spicy eating challenges such as Fire Noodle from Korea is also a hit on social media. The combination of this data resulted in an innovation in the Hype Abis Ayam Geprek series which is not only super spicy but also has the taste of geprek chicken.
3. Make Decision Making Faster and Accurate
The use of Big Data analysis will involve automatic computer calculation technology. If you use the right platform and program, calculations can be done in seconds. In addition, many large companies create algorithms to retrieve user data automatically. (What Is Big Data Analytics)
If calculations and data collection can run quickly, the amount of analysis results produced is also high. Big companies can see the analysis of the customer situation every hour and make a strategy based on it. If the analysis accuracy is high, the decision will be more effective. Because decisions can be made quickly, companies are more flexible and adaptive to changes in the market.
4. Improve Customer Service
In a service business, customer service is the main thing. Using Big Data analysis, companies can create simulations and service programs that are most relevant to customers. For example, on the YouTube website. Customers here are users who come to watch. If YouTube’s service is good, visitors will spend a lot of time on this platform and make money from the ads they watch.
To ensure good visitor service, YouTube has created a very sophisticated recommendation algorithm. This algorithm takes visitor viewing data, search history on Google, amount of free time watching videos and also the types of videos watched. From this information, new recommended videos will be offered on the front page. If customers always find video content interesting, they will definitely spend more time on the platform.