So, What is Data Analysis? It is a process of cleaning, transforming and to modeling data to discover meaningful and useful information for business decision making. Data analysts use data analysis to extract information from data and make decisions based on the given data.
An example of data analysis
When we make decision for our future based task or when we make some organized meetings, we consider our past actions regarding that situation. We think about happened last time or what will happen by choosing that option or particular decision. An analyzation is based on making decisions based on past or future to make useful decisions.
Now as same we call data analysis for business purpose. Sometimes analyzing is the only way to grow your business. If your business is falling behind, it’s crucial to reflect on past mistakes, identify any loopholes that can be leveraged, and analyze the data to make informed decisions.
Even if your business is growing you have to analyze for your forward movement and decisions for your company. So, you have to analyze the data and make useful decision that will help your business in future.
There are many data analyzing tools and there are very much of job vacancies for data analyzing. Data analyzing tools makes the work a lot easier. It is easy to manipulate and process easily. Helps to identify the patterns and helps to interpret.
There are many techniques and methods to analyze the data: text analyzation, statistical, diagnostic, predictive, prescriptive. Every method has their own specialization. Data analyzing process is the process of gathering information and finding the pattern in it.
Gathering information can help you to finalize your decision and can help you get conclusions. The process includes: data requirement, data collection, data cleaning, data analysis, data interpretation, data visualization.
How big data analyzed?
So, how big data should be analyzed? Basically, big data is the process of uncovering the trends, patterns and relations between large amount of data to help make the data useful and make informed decisions.
Tools and other extensive database technology help conduct this analysis, enabling the extraction of useful material. Each and everyday customers, buyers, businessmen create a huge amount of data. Every time we use phone, emails, buying things online this process creates huge amount of data for the organization controlling the base you are using.
Employees, supply and demand chains, finance terms, marketing exchanges these all are the big data with extremely large volume of information in multiple forms. Organizations have recognized the advantages of collecting big data. But there is no value of big data to be collected if not put in use.
But thanks to rapidly growing technologies that can use big data to extract useful and meaningful information into actionable insights. At times there were unstructured data which now software and other hardware capabilities help them to handle the data. Engineers even now look for ways to integrate the vast amount of data and complex information created by sensors, networks, smart devices, and web usage etc.
Big analyzation includes collecting, processing, cleaning and large datasets to help organization operationalize their big data. The big data analyzation works as: collect data, process data, clean data and then analyze data.
Collection of data
Data analysts gather and structure collected data, which is then stored in warehouses where business intelligence tools and solutions access and work on it.
Process of data
Once collected, one should organize it properly to obtain exact results. Batch processing is the one option to process data. Stream is another way to collect data and organize them for quicker decision making. Stream process is often more expensive and more complex.
Cleaning of data
Data analysts need to scrub any kind of data to improve it and obtain stronger results. They must format all the data correctly and eliminate duplicate or irrelevant data. Wrong data could mislead and create flawed insights.
Analyzation of data
big data takes a lot of time to bring it up into a usable state. Data mining, predictive analytics and deep learning.
Big data analytics tools and technology: big data analytics cannot be riddle down with a single tool or technology. Major players for analyzing big data are:
Hadoop
it is an open-source framework that efficiently stores and process big database. This framework is free and handle large amount of structured data.
NoSQL database
these are non-relational data management system that does not require a fixed scheme. It makes them a big raw, unstructured data.
Spark
open-source cluster computing framework that uses implicit data parallelism and also provides interface for programming entire clusters. Spark handles both batch and stream processing for fast computation.
Tableau
it is an end-to-end data analytics platform which allows you to prep, analyze and collaborate and even share your big data insights.
The big benefits of big data analytics are that it can save your cost savings, product development, market insights. It even helps your future in market-based sector or the organization. Big data analytics is important because it lets organization use colossal amounts of data in multiple formats and it would be from multiple sources.