Big Data Analytics: How Large Amounts Of Data Are Harnessed
Big Data Analytics: How large amounts of data are harnessed
In this era of information technology, every organization doing business in different fields knows how important data or information is. So now there is an emphasis on collecting data from various organizations, both domestic and international. This data collected mainly through big data analytics is utilized.
With the advancement of technology, the help of big data analytics is taken for new ideas on various topics. Based on the information or data analyzed with the help of big data analytics, various organizations make their future work plans.
Key To Big Data Analytics
Big Data Analytics is the process of analyzing large amounts of data or datasets to identify patterns, trends or relationships between different types of data.
It uses some statistical analysis techniques like clustering and regression. With the help of these techniques, new relationships between different subjects are basically extracted from the dataset. The results obtained through big data analytics are useful in taking decisions on any organizational issue.
Around the year 2000, discussions about the possibilities of Big Data started in different parts of the world. This is because software and hardware started to improve. It also makes working with large datasets easier. Since then, mainly from e-commerce company Amazon to smartphone manufacturers started collecting data.
Database software such as Hadoop, Spark, and NoSQL began to be used to store and process this massive amount of data. Over time, data from sensors, networks, financial transactions, smart devices, or the Internet also begin to be collected by consumers. Data engineers are now using advanced technologies like machine learning to analyze this data. As a result, it is possible to solve various complex problems very easily.
How Big Data Analytics Works
The purpose of Big Data Analytics is to collect the necessary data from large datasets, process it and analyze it by discarding the unnecessary data. For this, the data is processed in several steps. They are:
1. Collect Data
Different organizations collect data in different ways. For this purpose, various technologies like cloud storage or mobile applications are used in addition to gathering the information of customers who come directly to the showroom. That is, all organizations have multiple sources for data collection. A portion of the data collected is stored in a data warehouse for analysis by business intelligence tools.
Again, the data warehouse does not hold the initially collected unstructured data. These data or information are stored in the storage system called 'Data Lake'. This data remains in the storage lake until needed.
2. Data Processing
After collecting and storing the data, they need to be properly sorted. Especially when the amount of data is huge and messy, it is very important to organize the data. As the amount of data stored is increasing every day, keeping the data properly organized is a big challenge for any organization.
A method of data processing is called batch processing. Batch processing methods are useful when the time between data collection and analysis is long. Batch processing can handle large blocks of data.
On the other hand, an effective method for working with relatively small amount of data is 'Stream Processing'. This method takes less time in data collection and analysis. So stream processing is very useful for making decisions based on results in less time. However, stream processing is more complex and costlier than batch processing.
3. Data Cleaning
No matter how small or large the dataset is, it is important to format the data properly to get accurate results when working with the data. For this, all duplicate or unnecessary data has to be cleaned. Otherwise, the analysis may lead to incorrect or misleading results.
4. Analyzing Data
Extracting results from data is time-consuming. Once the data is ready for analysis, valuable results can be derived from the collected data through the use of modern analytics technology. Some important parts of big data analytics are:
• Data Mining: The dataset is sorted through data mining. As a result, it is possible to determine the relationship or what kind of trend, pattern or consistency exists between different data.
• Predictive Analytics: This process involves analyzing all the past data of an organization and making predictions about the future. By doing this it is possible to identify potential opportunities and risks.
• Deep Learning: Through deep learning it is possible to extract the required results even from a lot of complex and unintelligible data. Deep learning technology through artificial intelligence can find patterns in data with the help of different algorithms just like humans.
Benefits Of Big Data Analytics
Analyzing large amounts of data quickly and effectively will keep any organization ahead of its competitors. It has many benefits like making decisions in less time or identifying potential opportunities and risks by analyzing different types of data from multiple sources. Due to these advantages any organization can achieve growth in the fastest time. Some other benefits of Big Data Analytics are: