{"id":935,"date":"2018-03-15T13:55:44","date_gmt":"2018-03-15T13:55:44","guid":{"rendered":"https:\/\/www.digitalnest.in\/blog\/?p=935"},"modified":"2025-01-30T11:17:37","modified_gmt":"2025-01-30T11:17:37","slug":"difference-big-data-data-analytics","status":"publish","type":"post","link":"https:\/\/www.digitalnest.in\/blog\/difference-big-data-data-analytics\/","title":{"rendered":"Difference between Big Data and Data Analytics"},"content":{"rendered":"
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[et_pb_section fb_built=”1″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”]\"Difference<\/p>\n

The technical names, jargons everything seems to be similar, but when the deeper roles and responsibilities are considered Big Data<\/strong><\/a> and Data Analytics<\/strong><\/a> are actually confusing. Though both involved in the same data-driven field and the same technology, Big Data and Data Analytics are actually different.<\/p>\n

The buzzword \u201cBig data<\/strong>\u201d has been used by most of the organizations and it caught most of the attention.\u00a0Let\u2019s get to sorting out these two terms, the distinct skill sets required for them and what it all means. For it is important for aspirants to know them to move ahead.<\/p>\n

What is Big Data?<\/strong><\/h2>\n

\"What<\/p>\n

The name itself implies what Big Data is, it generally refers to an immense volume of Data be it a structured, unstructured and semi-structured. Depending on the three factors Velocity, Volume and Variety, it required more analyzing.\u00a0\u00a0The data streams at high speed each day through different mediums and channels like mobile, internet, social media, etc. and used for future for the business to take better strategic decisions.<\/p>\n

What is Data Analytics?<\/strong><\/h3>\n


\n\"What
\n<\/strong><\/h3>\n

Data analytics seek to provide operational insights of the business. It considers historical data and then draws out inferences from them to find better solutions to complex business problems. Data analysis is done at a more basic level and the problem related to data is specifically scanned through and parsed out with a specific goal in mind.<\/p>\n

What is Difference between Big Data and Data Analytics?<\/strong><\/h4>\n

Big data is the collection of raw and huge data whereas Data Analytics is pr\u00e9cised and segregated form of Data which is ready to use.<\/p>\n

For an instance, Big Data is like a library whilst Data Analytics is like a particular book in it. A user might not know what to read and the particular questions in Big Data and in this choosing the right one and focusing the right thing.<\/p>\n

Another notable difference between the two is that Big Data employs complex technological tools like parallel computing and other automation tools to handle the \u201cbig data\u201d. Data analytics use predictive and statistical modelling with relatively simple tools.<\/p>\n

Application of Big Data and Data Analytics<\/strong><\/h5>\n

Data is the chief thing of any activity performs today in the data-driven world. Many businesses are adopting the data-focused approach to be successful.<\/p>\n

Big Data<\/a><\/h6>\n

Retail<\/em><\/h4>\n

Understanding customer pace is the main thing in the business, rather than being competitive, for which the proper analysis is always required to segregate the data. This segregated data is widely used by the companies which include customer transaction data, weblogs, loyalty program data, social media and store-branded credit data.<\/p>\n

Communications<\/em><\/h4>\n

Sales and Communications service is all about finding the customers, gaining new ones and expanding the current customer bases. That can be achieved only with the combining and analyzing lots of customer and machine-generated data.<\/p>\n

Financial Services<\/em><\/h4>\n

Everything regarding the financial sector such as retails banks, insurance firms, private wealth management advisories, institutional banks and venture funds makes use of this big data. The major challenge experienced by all of them is the large amounts of multi-structured data set in multiple systems which can be take care only by Big Data. This is used in various ways such as fraud analytics, consumer analysis, operational and compliance analytics.<\/p>\n

Data Analytics<\/h6>\n

Fraud Detection<\/strong><\/em><\/p>\n

For the loans in the banking sector or any other matter of finance, data analytics help in finding the right customers who applied for loans and making the process easy for finding the probability of customer default.<\/p>\n

Proper Spending<\/em><\/strong><\/p>\n

There would not be any need of spending a large amount of money spent on little work. Using the Data Analytics would target the needed expenses where tax payer\u2019s money would have a major impact on the kind of work.<\/p>\n

Health Care<\/em><\/strong><\/p>\n

The biggest challenge of the medical sector is treating many patients at all once. Considering the quality of healthcare\u2019s improvement, Machine and instrument data use has grown drastically so as to optimize and track treatment, patient flow as well as the use of equipment in hospitals.<\/p>\n

Tools used in Big Data and Data Analytics<\/h3>\n

Data Analytics is entirely dealt with\u00a0R programming<\/a>,\u00a0Python coding, Tableau, Rapid Miner, MS Excel, Google Fusion Tables<\/p>\n

Big Data is something which deals with\u00a0Hadoop,<\/a>\u00a0Hive,\u00a0HBase,\u00a0Spark and Scala<\/a>. A basic knowledge of Java is always required for the MapReduce Programming, clustering and\u00a0Tableau-Hadoop\u00a0connectors.<\/p>\n

Roles of Big Data professionals and Data Analysts<\/h4>\n

Big Data Professionals Roles<\/strong><\/p>\n