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Big Data Analytics Introduction: Q&A

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    Big Data is creating new contingency for organizations to derive new value and create competitive advantage from their most valuable asset: information. Here are some question related to basics of Big Data.

   1. What is the term Big Data?

    Answer: 

            The term covers each and every piece of data your organization has stored till now. It includes data stored in clouds and even the URLs that you bookmarked. You company might not have digitized all the data. You may not have structured  all the data already. But them , all the digital, paper, structured and non-structured data with your company is now Big Data.

            In short, all the data whether or not categorized - present in your servers is collectively called Big Data.

2. What are advantages and disadvantages of Big Data

    Answers:

  •  advantages:

  1. Big Data analysis derives innovative solutions. Big data analysis helps in understanding and targeting customers. It help optimizing business processes.
  2. It helps in improving science and research.
  3. It improves healthcare and public health with availability of record of patients.
  4. Anyone can access vast information via surveys and deliver answers of any query.
  5. It helps in financial trading's, sports, polling, security/law enforcement, etc.            
  • Disadvantages:

  1. Traditional storage can cost lot of money to store big data.
  2. Lots of big data is unstructured.
  3. Big Data analysis violates principles of privacy.
  4. It can be used for manipulation of customer records.
  5. It may increase social stratification.

3. Tools used for big data

    Answers:

  • NoSQL: Databases MOngoDB, CouchDB, Cassandra, Redis, BigTable, Hbase, Hypertable, Voldemort, Riak, ZooKeeper.
  • MapReduce: Hadoop, Hive, Pig, Cascading, Cascalog, mrjob, Caffeine, S4, MapR, Acunu, Flume, Kafka, Azkaban, Oozie, Greenplum.
  • Storage: s3, Hadoop Distributed File System.
  • Server: EC2, Google  App Engine, Elastic, Beanstalk, Heroku,
  • Processing: R, Yahoo! Pipes, Mechanical Truck, Solr/ Lucene, ElasticSearch, Datameer, BigSheets, Tinkerpop.

4. What is the need of Big Data Analytics

    Answers:

  • As one of the most "hyped" terms in the marked today, there is no consensus as to how to define big data. The term is of ten used synonymously with relate3d concept such as Business Intelligence (BI) and data mining.
  • It is true that all three terms is about analyzing data and in many cases advanced analytics. But big data concept is different from the two others when data volumes, number of transactions and the number of data sources are so big and complex that they require special methods and technologies in order to draw insight out of data (for instance, traditional data warehouse solutions may fall short when dealing with big data).

5. List applications of Big Data

    Answers:

  1. Healthcare:
    1. Data analysts obtain and analyze information from multiple sources to gain insights . The multiple sources are electronic patient record; clinical decision support system including medical imaging, physician's written notes and prescription, pharmacy and laboratories; clinical data; and machine generated sensor data.
    2. The integration of clinical data; public health and behavioral data helps to develop a robust treatment system, which can reduce the cost and at the same time, improve the quality of treatment.
  2. Telecommunication:
    1. Low adoption of mobile services  and Churn Management (a term describes an operators's process to retain profitable customers) are few of the most common problems faced by the Mobile Service Providers (MSPs). The cost of acquiring new customers is higher than retaining the existing ones. 
    2. Customer experience is correlated with customer loyalty and revenue.With the diffusion of Smartphone, based on analysis of real-time location and behavioral data, location-based/ context-based services can be offered to the customers when requested. This  would increase the adoption of mobile services.
  3. Financial Firms:
    1. Currently, capital firms are using advanced technology to store huge volumes of data. but increasing data sources like internet and Social media require them to adopt big data storage systems.
    2. Capital markets are using big data in preparation for regulations like EMIR, Solvency  II, Basel II etc, anti-money laundering, fraud mitigation, pre-trade decision-support analytics including sentiment analysis, predictive analytics and data tagging to identify trades.
  4. Retail:
    1. Evolution of e-commerce, online purchasing, social-network conversations and recently location specific smartphone interactions contribute to the volume and the quality of data for data-driven customization in retailing.
    2. Major retail stores might place CCTV not only observe the instances of theft but also to track the flow of customers. It helps to observe the age group, gender and purchasing patterns of the customers during weekdays and weekends.
  5. Law Enforcement
  6. Marketing
  7. New Product Development
  8. Banking
  9. Energy and Utilities
  10. Insurance
  11. Education
  12. Agriculture
  13. Media and Entertainment