Big Data Analytics Tools – In today’s data-paced era, understanding data analysis techniques to turn useful information is one of the drivers of career success. Moreover, currently the volume of data in the world is very large, but only 0.5% of the data is actually used and analyzed for research. The term large data volume is better known as Big Data.
Big Data itself is the application of special techniques and technologies to process very large data sets. These data sets are generally very complex making it difficult to analyze using ordinary tools. This is where Big Data Analytics used to be, which became one of the types of large volume data analysis techniques.
Big Data Analytics is the science of extracting actionable insights from Big Data to help people or organizations make better decisions. Data analysis techniques use machine learning (Machine Learning), artificial intelligence / AI (Artificial Intelligence), statistics, and natural language processing (Natural Language Processing) to find patterns in data and leverage tools from data visualization and computer and human interaction to make these patterns understandable to the user.
So that with Big Data Analytics we can make better decisions, model and predict the future. What are the techniques that Big Data Analytics has? Let’s see the following discussion!
Why is Data Analysis Important?
Basically, data analysis refers to the process of extracting and using raw data that makes use of specialized computer systems. This system then processes, sorts, and transforms the data into forms and patterns that can be understood.
The results of data processing (commonly called solutions) have many uses for companies. From optimizing productivity to decision making. Therefore, now there are various data analytics tools that can help you analyze and process data. What are the types?
Top 9 Big Data Analytics Tools in 2023
Along with the rapid development of information technology, the importance of data analysis is even more crucial. Some companies have implemented this process by using various data analytics tools.
However, the use of data analytics tools cannot be done haphazardly. Need compatibility with the needs of the company and the system used.
Basically, data analysis refers to the process of extracting and using raw data that makes use of specialized computer systems. This system then processes, sorts, and transforms the data into forms and patterns that can be understood.
The results of data processing (commonly called solutions) have many uses for companies. From optimizing productivity to decision making.
1. Data Mining and Text Mining
Data Mining and Text Mining are types of data analysis techniques that are often used by Big Data. The way Data Mining works is to understand patterns and find something meaningful from a collection of Big Data by combining statistical methods and Machine Learning.
An example is when there is customer data then the data is processed to determine which segment is most likely to be attracted to a product offer.
Meanwhile, Text Mining will dig up information from a set of unstructured text documents to identify concepts, topics, keywords and other attributes in the data.
With this technology, Data Scientist can analyze writing on the web, comment fields, books, and other parts of the web.
Like on Blogs, Twitter, Instagram and even text-based emails to find information that is useful for creating company relationships with prospective customers.
2. Association Analysis
The first Big Data Analytics technique is one of the most frequently used data analysis techniques in the field of Data Science today. This data analysis technique can identify the behavior of specific events or the process by which associations arise in each event.
The concept is to connect between items in large numbers to form a data network from which insight or information can be obtained. This analysis requires input in a certain format in the form of binomial data (true or false).
3. Predictive Analytics
Predictive Analytics is a data analysis technique that provides predictive results about something that will come so that companies can decide what steps to take.
This model is used to identify outcomes based on historical data that has been used and prediction algorithms to assist in determining the probability of what will happen next.
An example is the recommendation system used on e-commerce sites, from visitor data and customer transactions, it can be estimated what items visitors/customers are interested in buying.
When you want to become a data practitioner, you need to study data analysis. Data talent candidates must be able to analyze various types of data with appropriate methods.
This ability is not obtained instantly but is learned gradually and routinely. With today’s advances in technology. You can easily find materials and examples about data analysis. Apart from that, you can also take Data Science courses such as DQLab.
4. Machine Learning
In analyzing data, this AI-based engine makes it possible to quickly and automatically produce models that can analyze larger and more complex data.
As well as providing results that are generally more accurate even on a large scale like Big Data. The potential of Machine Learning in handling large amounts of data.
And its ability to extract knowledge from Big Data makes Machine Learning produce a very useful application such as a spam detector.
5. Tableau
As the forefront in data analysis, Tableau is software with Big Data capabilities that need not be doubted.
Is well known, the function of big data is to collect and process customer information so that entrepreneurs can find out the needs of consumers and become closer to them.
As a tool for big data, Tableau can connect data from various sources. Starting from Microsoft Excel-based data, internet networks, and data
The results of Tableau data processing also vary, which can be in the form of data visualization in the form of charts and dashboards. What’s more, the results can be updated in real time.
If needed, Tableau also provides a download feature with various file formats according to your needs. You can also share data analysis solutions on social media or with clients if needed.
6. Lookers
Looker is a tool that is more suitable for users who have experience reading or working with data. With the available extract, load, transform (ELT) process, users can shape and change data as needed.
One of these data analytic tools can convey data directly to the tools and applications you use. Looker also allows users to ask tricky questions, as long as they use common business language.
7. Domo
Domo is a cloud-based business management suite. This software is able to combine data sources such as databases, spreadsheets, and of course cloud-based software.
Data speed and understanding is the focus of Domo. The business intelligence capabilities of this software enable collaboration through its visualization and commenting features.
This software also supports use via mobile devices. Thanks to that, users can access it easily and anywhere.
8. Knime
Knime or Konstanz Information Miner can combine various components to explore information (data mining) and machine learning. This open source tool will help you analyze and display data through visual programming.
9. Sisense
Sisense is able to simplify data analysis. Therefore, the results of the processed data can not only be understood by the analysis team, but also more ordinary users.
This tool offers a variety of features. Starting from setting the dashboard display according to comfort and needs, interactive visualization, to solid analysis capabilities.
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