Today I am going to make you aware about the Big Data Analytics i.e. what is Big Data Analytics and why it is so important now days.
Big Data Analytics
Big Data Analytics is the process of examining large and varied data(unstructured data) sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.
While the size of big data is still not defined that which data set considered as big data the definition also changes with time. Most of the practitioners or the analysts consider data set from 30-50 terabytes (10 12 or 1000 gigabytes per terabyte) to multiple petabytes (1015 or 1000 terabytes per petabyte as big data analytics.
Complexity of big data analytics is because of its unstructured nature of data generated by latest technologies like from the web logs, radio frequency Id(RFID),sensors, embedded in devices ,machinery , vehicles ,internet searches, social networks such as Facebook , LinkedIn , smart phones , portable computers, other cell phones ,GPS devices, call center records. If big data analytics is to use effectively then we should combine the big data with the structured data from conventional business application such as ERP (Enterprise resource planning) or customer relationship management (CRM).
In the nearby future the rate of growth of big data will continue to increase. Unlike big data analytics complexity or variability the reason behind its rate of growth or speed aspect is nature of modern online and real-time data capture devices, systems and networks. There is continuous development of new big data analytics tools and the technologies most of these technology heavily based on the massively parallel processing (MPP) databases its main task is to distribute the large data set processing across various servers. In order to work with the massive amount of unstructured data generated in big data analytics environment particular database query tools are also developed.
Big Data Analytics Importance
With the help of big data analytics company gain more insight about their business, products, customers, competitors and so on. As a result of which efficiency of company increases, company sales increases, better customer service provided by the company and products and services are also improved. But for gaining this big data should efficiently and effectively captured,processed and analyzed. There are several examples where big data analytics is efficiently used:
• In order to return the stream of telemetry manufacturing companies uses sensors in their products.This sensors are also used for providing the services like on star that helps in delivering of security, communication and the navigation services. Telemetry use the failure rates,Usage pattern and other opportunities related to the product that helps to make better product and as a result of which assembly and development cost reduced.
• Smartphones and other GPS devices provides advertisers with an opportunity to focus on new customers when they come close or together in a store, coffee shop or restaurant. This helps in generating the new revenue for service providers and by means of it businesses get opportunity to target more new customers.
• Retailers also keep record of their customers by making continuous observation of social media and the web log files created from their e-commerce sites. This analysis helps the retailers to understand the behavior of their customer like who buy their product, when they buy their product, how often they buy the product from their store. By revealing these information retailers can do micro customer segmentation, targeted marketing campaigns and supply chain efficiency also improved.
• Information technology logs required to improve the IT troubleshooting and detection of security breach if any. Prevent speed, effectiveness and future occurrence.
• For improving the customer interaction and the satisfaction voluminous historical call center information required.
• Analysis of social media content to understand the customer behavior about company and its customers and so do improvement in the products , services and enhance the customer interaction.
• Making efficient use of financial market transaction information to assess the risk quickly and to do corrective action.
• It also helps in the detection and prevention if any fraud occur in the organization that deals with the financial transactions such as investing, banking shopping. Insurance and health care claims.
Challenges Faced by Big Data Analytics
Big Data Analytics understanding and Utilization:
It’s a challenging task in most of the organization to analyze the data and to find out the best use of data depending on the company strategy, tactics and industry. It also required continuous analysis because the landscape of data changes at an ever-increasing rate and the executives develop different strategies of carrying out the analysis on the available information.
Complex, New and Emerging Technologies:
The technology used for use of big data is new for most of the organization therefore organization need to give proper training on the new technology to efficiently utilizing the big data at fast pace.
Requirement for IT, Data Analyst and Management Resources:
Around 140000 to 190000 extra workers required that have sound knowledge about deep analytical and the need of 1.5 million more data literate managers that either retrained or hired. Therefore any organization that is planning for taking big data analytics initiative must to retrain the existing company people or to hire new expertise people to bring success into opted big data initiative.
Disposal and Archiving of Big data :
Since big data is voluminous and have varying content and structure but over time big data analytics might lose its values. There is a need to use of new tools and technologies and methods for archiving the big data and deleting existing big data without affecting the thing that you are using for satisfying the current business needs.
Cloud based solutions:
A new era in business software application where the data of organization managed and stored in data centers across the globe. Several solutions available for this like ERP, Document management, CRM, Data Warehouses, Business Intelligence and many more. But apart from all these solutions available the main concern is keeping the data safe and proper management of company confidential data. Cost saving and tremendous flexibility opportunities provided by these solutions but company need to more alert about security of data and complete management of an enterprise’s big data analytics model.
Privacy, security and Regulatory considerations:
Due to the complexity and high volume of big data,it is challenging task for most of the organization that deal with the big data to get efficient access on the data and to secure the data appropriately ,so that the unauthorized user not able to gain access to the confidential company data. Management of unstructured data such as emails is quite problematic, therefore require to continue improve security measures and technologies. The companies that use their businesses globally have different privacy laws as the US and other countries will have different privacy laws. Hence it is very important for most of the organization to closely integrate the big data analytics, data security and regulatory functions.
Big Data Analytics Development Strategy
Author: Poonam Kaushal
A blogger and passionate writer who always try to introduce new concepts to readers.Involved in blogging since 2014 and believe in “No pain No gain” if you have to achieve something then you need to work hard.Working as freelance blogger in many companies and also managing her own blog that you all know savenlike.com.