Analytic Processes and Tools

Big data analytics is the process; it is used to examine the varied and large amount of data sets to uncover unknown correlations, hidden patterns, market trends, customer preferences, and most of the useful information which makes and help organizations to take business decisions based on more information from Big data analysis. It is possible to process data very quickly and efficiently. That includes analyzing the data and make use of the result. This makes a lesser effort and more efficient where it is not possible with more traditional business intelligence solutions.

Concept of Big data Analytics Tools

Big Data Analytics tools are very important for enterprises and large-scale industries because of the huge volume of data that will be generated and managed by modern organizational tools using Bigdata tools. Big Data Analytics tools help businesses in saving time and money and also in gaining insights to make data-driven decisions.

Big Data analytics is the complete process of collecting, gathering, organizing, and analyzing huge sets of data (known as Big Data) to observe/identify the patterns and also other useful information needed for business decisions. The process – Big Data analytics helps organizations to better understand the information which is present within the sets of data. The guy who works as an Analyst working with Big Data typically will have the knowledge that comes from analyzing the data. There are different types of tools are available under Data Analytics that help to improve the data analyzing process that are data analysis, data cleansing, data mining, data visualization, data integration, data storage, and management.

For the process of big data analytics, there is a need for very High-Performance Analytics. Hence to analyze such a huge volume of data, specialized software tools are required for the Big Data analytics process and applications for predictive analytics, data mining, text mining, forecasting, and data optimization.

Benefits of Big Data Analytics

There are many advantages to using Big Data Analytics. This process of big data analytics with high-performance in predictive analytics, data mining, text mining, forecasting that data, and optimization helps the enterprises in getting benefit in many areas, which includes new revenue opportunities in business, for more effective marketing of the products, in providing much better customer service, in improving operational efficiency and also can make decisions on competitive advantages over market competitors.

To be Specific on the Big Data Analytics process, it enables enterprises to break down/narrow their huge volume of data to the most relevant information and analyzes it to inform critical business decisions. This proactive approach to business is transformative because it gives analysts and decision-makers the power to move ahead with the best knowledge and insights available, often in real-time.

How Big Data Analytics works and its key technologies

As mentioned earlier, a big data analytics process is not a single activity that encompasses a huge volume of data. Instead, its advanced analytics can be applied to large data, but in reality, several types of different technologies work together to achieve the most value from information. Below are the biggest and important technologies involve in the big data analytics process:

• Data management
• Data mining
• Hadoop
• In-memory analytics
• Predictive analytics.
• Text mining

Below is the list of some of the top tools used to store and analyze Big Data. These Big Data Analytics tools can be further be classified into two Storage and Querying/Analysis.

1. Apache Hadoop: Apache Hadoop, a big data analytics tool that is a java based free software framework. It helps in the effective storage of a huge amount of data in a storage place known as a cluster. The special feature of this framework is it runs in parallel on a cluster and also has the ability to process huge data across all nodes in it. There is a storage system in Hadoop popularly known as the Hadoop Distributed File System (HDFS), which helps to splits the large volume of data and distribute it across many nodes present in a cluster. It also performs the replication process of data in a cluster hence providing high availability and recovery from the failure – which increases the fault tolerance.
2. KNIME: KNIME Analytics Platform is one of the leading open solutions for data-driven innovation. This tool helps in discovering the potential & hidden in a huge volume of data; it also performs mine for fresh insights or predicts the new futures. The KNIME Analytics Platform tool is a very much helpful toolbox for data scientists.
3. OpenRefine: OpenRefine is introduced as Google Refine. This tool is one of the efficient tools to work on the messy and large volume of data that all include: cleansing data, transforming that data from one format to another, and also to perform extending it with web services and external data. The open refine tool helps explore large data sets easily.
4. Orange: Orange is famous open-source data visualization and helps in data analysis for beginner and as well to the expert. This tool provides interactive workflows with a large toolbox option to create the same, which helps in the analysis and visualizing of data. An orange tool has many and different visualizations that include bar charts, trees, scatter plots, dendrograms, networks, and heat maps.
5. RapidMiner: RapidMiner tool operates using visual programming, and also it is much capable of manipulating, analyzing, and modeling the data. RapidMiner tools make data science teams easier and productive by using an open-source platform for all their jobs like machine learning, data prep, and model deployment. Because of its uniformity in the data science platform makes accelerates in the building of complete analytical workflows in a single environment which helps in dramatically improving efficiency and short duration of time to value for data science projects.

Some of the examples/areas using Big Data Analytics Tools

Big data analytics tools are very much in need of business/enterprises which depend on quick and agile decisions to stay as competitive, and most likely big data analytics tools are important while business decisions are based on their previous business data. Below are some of the different types of organizations that can make use of this technology:

• Travel and hospitality: In the travel and hospitality business, it is a very important and key factor to keep customers happy, but to make customers satisfy is harder to gauge. For example, in Resorts and casinos, will be having a very short span of an opportunity to turn around customer experience. Hence applications of Big data analytics give the ability to collect customer data, apply statistical analytics to better understand and improve these businesses.
• Retail: Nowadays, customer service has emerged as a huge tree compared to past decades; knowledgeable shoppers always keep searching and expect retailers to understand exactly what they want and when those products need it. Here Big data analytics technology comes into the picture and helps the retailers meet customer demands.
• Government: There are few and particular government agencies always face some big challenge like how to prepare the budget for the public without any compromise on quality or productivity. Hence many agencies use big data analytics, which helps them in streamlining operations while giving the agency a more correct view of criminal activity to avoid preparing a feasible and good budget.
• Healthcare: Big data analytics can also be used in the healthcare industry. Maintaining the Patient records, their insurance information and health plans, and also all other types of information which are difficult to manage. Hence it is so important application of big data analytics technology in the healthcare industry.