Data is on the rise and more and more companies are adapting to the data-driven approach to running their businesses. Such organizations implement Big Data Analytics to derive key insights from a vast set of collected data. The analysis process constitutes multiple tools and intermediate steps which work together to derive valuable insights for businesses.
This article introduces Data Analytics Big Data and discusses its key features. It further explains the importance of this process in the current market and lists the major subprocesses involved. The article also elaborates on the applications of Data Analytics Big Data and the tools required for its implementation. Read along to learn more about Big Data and Data analytics!
Table of Contents
What is Data Analytics Big Data?
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Big Data refers to a significant amount of data that overwhelms traditional tools when it comes to storage and preprocessing. The current market contains millions of sources that generate massive quantities of data daily globally. Social Media platforms are a major contributor to this collection of data. For instance, Facebook alone produces over 500 terabytes of data, including videos, texts, audio, images, etc., on a daily basis. Traditional tools are not equipped to match such large quantities of data.
Companies which rely on data need to perform analysis on this Big Data to gather valuable insights. Big Data Analytics is a process which organizations use to extract meaningful data insights like hidden patterns, market trends, etc., from this huge collection of structured and unstructured data. It is a form of advanced analytics that relies on complex applications of predictive models and statistical algorithms via analytics systems. Data Analytics Big Data has a number of advantages as it facilitates optimal decision making, improves customer experience and helps in other business aspects.
Key Features of Data Analytics Big Data
The following features make Data Analytics Big Data a popular field:
- Risk Management: Companies especially in the financial sector can utilize Big Data Analytics to identify discrepancies & fraudulent activities. This allows the organizations to narrow down their suspect lists and get to the root causes of financial troubles.
- Product Development & Innovations: Manufacturing companies can make use of this feature to measure the efficiency of their designs and perform analytics to navigate areas for improvement. Similarly, the defence industry also deploys powerful analytics to optimize its product development.
- Quicker & Better Decision Making: Businesses leverage Big Data Analytics to make informed strategic decisions. For instance, a company can use Big Data Analytics to decide whether a particular location is suitable for its new outlet or not. This allows them to make a data-driven decision that incorporates several factors like population, accessibility, demographics etc.
- Enhanced Customer Experience: Big Data Analytics can perform advanced sentiment analysis on tweets and reviews given by customers to the company. This way, negative tweets help the company to address the concern of its customer and provide effective solutions.
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Importance of Data Analytics Big Data
Data Analytics Big Data is the stepping stone for companies that wish to implement a data-driven business model. Since analysis works on extracting valuable data patterns from a mass of raw data, companies with a customer-oriented approach rely heavily on this technology to customize their businesses. Therefore, organizations falling under the Business to Consumer vertical are the frontrunners in applying Data Analytics Big Data to their decision making. Moreover, depending on the business requirements, this technology offers you 3 classes of data analysis namely, Prescriptive Analytics, Descriptive Analytics and Predictive Analytics.
The results of Big Data Analytics allow companies to understand their consumers’ point of view, Moreover, the analysis reports provide a detailed picture of the flaws and shortcomings present in the current products. Companies can design better products using this data and enhance their customers’ experience significantly. Furthermore, such analysis empowers companies to make future predictions and shape their marketing strategies accordingly.
Key Processes Involved in Data Analytics Big Data
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Big Data professionals go through a multi-step process of collecting data, transforming it and performing Data Analytics on the transformed results. Here is a brief on the four key steps involved in Big Data Analytics:
- Step 1) Data Source Identification: In the initial step, Data Professionals need to select the data sources that may contain useful data in raw form. A good practice is to pick Data Sources from a broad variety of platforms to improve analysis accuracy.
- Step 2) Data Preprocessing: All the selected data in step 1 is taken through various phases of preprocessing. This includes filtering of corrupt values, removal of noise, etc.
- Step 3) Data Transformation: The processed data is converted into an analysis-ready form. The results of this step are used as input for the analysis. The main aim of the Data Transformation step is to convert all the data into the same format before running analytical algorithms on it.
- Step 4) Data Analysis: In this stage, statistical and mathematical tools perform a detailed analysis of the data input from the previous step, The objective of this step is to discover hidden and important patterns in the vast sea of collected data.
- Step 5) Data Visualization: Business Intelligence tools like Power BI, Tableau, etc work on the results of Data Analysis and create rich reports for different levels of business teams.
That’s it! The Big Data Analytics process is complete. By understanding and incorporating these processes with various business and operations teams, organisations can leverage the power of Big Data Analytics for their own needs.
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Applications of Data Analytics Big Data
The following sectors rely on Data Analytics Big Data:
- E-Commerce: Companies use Big Data Analytics to predict market trends and accordingly optimize their product price. Moreover, competitor analysis is also an application that helps e-commerce companies to understand the market better.
- Education & Healthcare: Analysis of past results and student performance allows the education system to revise its courses and policies. Moreover, in the field of healthcare, patients can submit their medical history for Big Data Analytics to learn their chances of catching a disease in the near future.
- Entertainment: Recommendation Engines work on Big Data Analytics. These include recommendations for shows, movies, plays and much more. The past viewing history of users acts as an input in such analysis. The results of such engines provide the names of shows or movies that will accurately match the user’s interest
- Banking: The income, expenditure and savings of a user in the recent past can allow Big Data Analytics tools to predict the best possible loan and interest rates for them. Moreover, banks can rely on this technology to catch fraudulent activities and loopholes in their policies.
Key Tools and Technologies used in Data Analytics Big Data
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Data Analytics Big Data is a collective term for the following group of technologies that work together to gather valuable insights from a vast data sea:
- Hadoop: This open-source tool offers a fully managed framework for you to store & process Big Data easily. Using Hadoop, you can work with a large amount of both structured & unstructured data.
- Predictive & Streaming Analytics Tools: The Predictive tools can process huge and complex datasets and further deploy Machine Learning & Statistical Algorithms to provide future predictions. The Streaming tools on the other hand are useful for filtering, aggregating and analyzing data which is distributed across various platforms.
- NoSQL Databases: You will need NoSQL databases to manage distributed data which is present in different formats among various data sources. Since such databases do not have any set schema format, they are ideal to manage raw unstructured data.
- Spark: This open-source tool empowers you to cluster incoming data and create input datasets for batch or stream processing
- Data Repositories: You require huge storage space to analyse terabytes of data at one go. Data Lakes and Data Warehouses are some options that cater to businesses which are implementing Big Data Analytics. Data Lakes are good for storing raw data while a Warehouse is ideal to store data after preprocessing.
- Data Integration and Visualization Tools: Companies need to extract data stored in different sources and transform it into an analysis form before they can initiate Big Data Analytics. Organizations rely on ETL Pipelines to automate this task and provide transformed data into their Data Warehouses. Furthermore, to represent the results of analysis, companies utilize Data Visualization tools which allow them to create comprehensive reports and present insights to business teams.
Conclusion
The article introduced you to Big Data Analytics and explained its key features. It also explained the importance and sub-processes involved in such analysis. The article further elaborated on the steps that you can use to set up Big Data Analytics. Furthermore, the article explained the applications of Data Analytics Big Data in the current context.
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Now, to run queries or perform Data Analytics on your data, you first need to export this data to a Data Warehouse. This will require you to custom code complex scripts to develop the ETL processes. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ multiple sources to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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