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Big Data Analysis in Digital Marketing

Big Data Analysis in Digital Marketing. Anna Flehantova , Ph D, associate professor, Poltava University of Economics and Trade Ukraine anna.flegantova@gmail.com. Book: https://books.google.com.ua/books?id=NLZYCwAAQBAJ&printsec=frontcover&hl=uk#v=onepage&q&f=false. What is Big Data?.

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Big Data Analysis in Digital Marketing

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  1. Big Data Analysis in Digital Marketing Anna Flehantova, Ph D, associate professor, Poltava University of Economics and Trade Ukraine anna.flegantova@gmail.com

  2. Book: https://books.google.com.ua/books?id=NLZYCwAAQBAJ&printsec=frontcover&hl=uk#v=onepage&q&f=false What is Big Data?

  3. Big Data The term has been in use since the 1990s Big data is data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Lately, the term "big data" tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set https://en.wikipedia.org/wiki/Big_data

  4. Volume The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not. Variety The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion. Big data can be described by the following characteristics Veracity The data quality of captured data can vary greatly, affecting the accurate analysis Velocity In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. https://en.wikipedia.org/wiki/Big_data

  5. What is Big DataAnalytics?

  6. Benefits of Big Data Analysis for Modern Enterprises With high-performance data mining, predictive analytics, text mining, forecasting, and optimization, enterprises that utilize Big Data Analytics are able to drive innovation and make the best business decisions. Companies that take advantage of all that Big Data Analytics solutions have to offer are better positioned to optimize machine learning and address their Big Data needs in groundbreaking ways. Big Data Analytics enablesenterprises to analyze their data in full context quickly, and some offer real-time analysis. https://www.ngdata.com/what-is-big-data-analytics/

  7. Specifically, Big Data Analytics enables enterprises to narrow their Big Data to the most relevant information and analyze it to inform critical business decisions. This proactive approach to business is transformative because it gives analysts and decision makers the power to move ahead with the best knowledge and insights available, often in real time. This means that companies can improve their customer retention, develop better products, and gain a competitive advantage by taking rapid action to respond to market changes, indications of critical customer shifts, and other metrics that impact business. Enterprises utilizing Big Data Analytics with fidelity also have the ability to boost sales and marketing results, discover new revenue opportunities, improve customer service, optimize operational efficiency, reduce risk, and drive other business results. https://www.ngdata.com/what-is-big-data-analytics/

  8. Types of Big Data Analytics Tools Big Data Analytics tools are important for companies and enterprises because of the sheer volume of Big Data now generated and managed by modern organizations. Big Data Analytics tools also help businesses save time and money and aid in gaining insights to inform data-driven decisions. There are various types of tools that may fall under the umbrella of Big Data Analytics or serve to improve the process of analyzing data: data storage and management, data cleaning, data mining, data analysis, data visualization, data integration, and data collection. https://www.ngdata.com/what-is-big-data-analytics/

  9. Trends in Big Data Analytics Big Data Analytics is changing the way the world does business, which also means that it is changing technology and business practices. Robert L. Mitchell, Computerworld contributor and chief editor of TechBeacon.com, explains that Big Data technologies and practices move quickly and states that “top emerging technologies and trends should be on your watch list.” https://www.ngdata.com/what-is-big-data-analytics/

  10. Cloudera Hadoop MongoDB Mitchell compiled a list of the hottest trends in Big Data Analytics, based on input from IT leaders, consultants, and industry analysts Hive Tableau Spark http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  11. Hadoop The name Hadoop has become synonymous with big data. Hadoop is an open-source software-framework for distributed storage of large datasets on computer clusters. In layman terms, this means that you have the ability to scale your data without the worry of hardware failures. Hadoop provides large amounts of storage for all sorts of data along with the ability to handle virtually limitless concurrent jobs or tasks. http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  12. What is Hadoop ?

  13. Cloudera Cloudera is a company that makes a commercial version of Hadoop. Now, although Hadoop is a free and an open-source project to store large amounts of data, the free version of Hadoop is not easy to use. Thus, a number of companies have developed friendlier versions of Hadoop, and Cloudera is the most popular of them all. http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  14. What is Cloudera?

  15. MongoDB MongoDB is a good resource to manage data that is frequently changing or data that is semi-structured or unstructured. Most often, it is used to store data in mobile apps, product catalogs, real-time personalization, content management, and applications that deliver a single view across multiple systems. http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  16. What is MongoDB?

  17. Hive This software facilitates managing and querying large datasets residing in the distributed storage. Apache Hive provides a mechanism that helps project structure into this data and then query it using HiveQL – an SQL-like language. At the same time, this language allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL. http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  18. What is Hive?

  19. Spark An open-source data analytics cluster computing framework, Apache Spark fits into the Hadoop Distributed File System (HDFS). Spark promises a performance that is up to 100 times faster than HadoopMapReduce. http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  20. What is Spark?

  21. Tableau Tableau is a data visualization tool whose primary focus is on business intelligence. With Tableau, you have the ability to create bar charts, maps, scatter plots, and more without programming. http://bigdata-madesimple.com/top-6-big-data-tools-to-master-in-2017/

  22. What is Tableau?

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