{"id":2045,"date":"2016-10-11T08:19:13","date_gmt":"2016-10-11T02:49:13","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=2045"},"modified":"2016-10-13T17:25:16","modified_gmt":"2016-10-13T11:55:16","slug":"how-to-use-big-data","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/how-to-use-big-data\/","title":{"rendered":"How to use Big Data"},"content":{"rendered":"

<\/a><\/p>\n

Traditional data management systems were not able to cope with the data explosion that came with social media. Firms like Google, Linkedin and Facebook were built around big data. In recent years, big data has made its way into several business verticals.<\/p>\n

In case your organization os stumbling to get started, here are 3 applications of big data:<\/p>\n

Email marketing segmentation analysis<\/h1>\n

There is intense competition between different forms of marketing to get a customer\u2019s attention. Piquing a customer\u2019s interest has become a tough job due to the short attention span of people browsing the net. People are overwhelmed by the number of different e-mails they receive from advertisers. Thus it has become crucial to be relevant today. In email marketing and segmentation, big data is used by marketers and advertisers to group the customers according to their preferences, demographics, behaviours and purchases. Thus you can send them relevant emails instead of spamming their inboxes.<\/p>\n

 <\/p>\n

<\/a>
\nsource: www.hynter.com<\/span><\/p>\n

Real time analytics<\/h1>\n

Real-time analytics is also known as real-time data analytics, real-time data integration, and real-time intelligence. It makes use of all available enterprise data and resources which are available. It comprises of\u00a0dynamic analysis<\/a>\u00a0and reporting, based on data entered into a system less than one minute before the actual time of use. In customer relations management, real-time analytics can provide up-to-the-minute information about an enterprise’s customers, thus enabling businesses to make quicker and more accurate decisions.<\/p>\n

Sentiment analysis<\/h1>\n

Recently, the term \u201copinion mining\u201d has been trending on the internet. Opinion mining is also called sentiment analysis. It is the process of determining the tone behind the series of words used to understand the perspective, attitude, thinking and beliefs of the people.<\/p>\n

In essence, it is the process of determining the emotional tone behind a series of words used to gain an understanding of the attitudes, opinions and emotions expressed within an online mention.<\/p>\n

The wide-spread use of social media and the proliferation of mobile devices have created a perfect environment for customers to express their opinions and views about anything and everything at any time. This opinion or sentiment data, generated through social channels in the form of reviews, chats, shares, likes tweets, etc., often includes comments that have become beneficial for businesses looking to improve products and services, make more informed decisions, and better promote their brands.<\/p>\n

Below given are the steps to incorporate big data into your organisation:<\/p>\n

<\/a><\/p>\n

Step 1: Assemble all project objectives<\/h1>\n

You can hire Data teams to work along with your business to note down your business requirements and goals. Data scientists perform analysis using data and models, and they are expected to understand what the business users are trying to achieve. Enterprise owners should have a detailed understanding of what the objectives and goals of their businesses are.<\/p>\n

Step 2: Gain sponsors<\/h1>\n

Big data projects are large and complex. They take time to develop and succeed. Hence, a project development team and executive sponsors are needed.<\/p>\n

Step 3: Expand the existing system<\/h1>\n

Although many of the relational database management systems do not go along with big data technologies, it is not necessary to replace them all. Firstly, you can make note of all the existing functionalities you have currently, then<\/strong>\u00a0identify and prioritise additional data sources you require. After this, you can determine which big data technology is best suited to your requirements.<\/p>\n

Step 4: Consolidate incremental release<\/h1>\n

A lot of big data projects do not succeed due to companies trying to incorporate too many functionalities at once. Once you\u2019ve established priorities and a project team, begin to work on incremental releases and incorporate new data at a time. This approach will let you adjust your operation incrementally and understand how to use data to influence actions throughout your organisation.<\/p>\n

Step 5: Link customer data to company process<\/h1>\n

Each new data set presents an opportunity to change the way you deliver products and services. Incorporate data-driven decisions into the organisation at all levels. It can start from product development to product promotion and advertising. Thus, you can use big data to match the customer needs accurately.<\/p>\n

Big data is also being used successfully for algorithmic business models<\/a> using context-aware technologies<\/a>. This is the logical next step for your business once you have started using big data to provide you with the basic customer insights.<\/p>\n","protected":false},"excerpt":{"rendered":"

Traditional data management systems were not able to cope with the data explosion that came with social media. Firms like Google, Linkedin and Facebook were built around big data. In…<\/p>\n","protected":false},"author":2,"featured_media":2124,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[126],"tags":[147,148],"class_list":["post-2045","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","tag-big-data","tag-big-data-analytics"],"yoast_head":"\nHow to use Big Data - Artificial Intelligence, ROBOTICS, AUTOMATION<\/title>\n<meta name=\"description\" content=\"Firms like Google, Linkedin and Facebook were built around big data. 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