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Big Data Analysis


Big Data is not just the ability to store large amounts of data, more important is what we can do to the data in that large volume, how we use the data with such large volumes. One of its uses is for data analysis needs. Big Data Analysis or Big Data Analysis can be done in order to assist the decision making process (Decision Support) and strategy (Strategic Business) of an organization, business institution, or company.

Here is an example of big data implementation in addition to data analysis:

  1. Sentiment analysis
  2. Predictive analysis
  3. Social media analysis
  4. Customer segmentation analysis
  5. Marketing analysis / campaign (campaign analysis)
  6. Analysis of product or service performance
  7. Analysis for Fraud Detection (Fraud Detection)
  8. Analysis in the framework of customer management (Customer Relationship Management)
  9. Financial Risk Management (Finacial Risk Management)
  10. Machine learning
  11. And so forth


As an example:

  1. Retail companies can use information from social media such as Facebook, Twitter, Google+ to analyze how the behavior, perceptions of customers of a product or brand of the company.
  2. Manufacturing companies can monitor the condition of the equipment at any time (real-time), so as to estimate the best time to replace equipment. Because replacing too quickly will be a disservice / waste of money or if it’s late will cause production to be disrupted due to equipment breakdown.
  3. Manufacturing companies can also monitor new launching products through social providers to find out if there is an after-sales issue so as to prevent the failure of the warranty causing major publications that could damage the product and company image.
  4. Advertising companies can use information from social media to find out responses to newly launched promotions / advertisements.
  5. The hospital can record patient medical records so that the big data can be used to analyze the patient’s illness
  6. Governments can use information from social media to find out the level of public satisfaction with the government
  7. Financial Services can use big data analysis to see which insurance applications can be processed immediately, and which ones need to be validated by a visit by an insurance agent
  8. Banking services may use customer recording transactions to identify potential criminal activities such as money laundering, or to record employee habits in order to detect possible fraud.
  9. The sports team can use big data for tracking ticket sales, knowing the player’s condition and the probability of injury and even team strategy.
  10. Financial institutions can use big data analysis to quickly identify potential fraud before it becomes large, thus minimizing the risk of financial loss.
  11. Governance can take advantage of big data analysis to improve state security by being able to detect, prevent and combat cyber attacks.
  12. The health industry can use large data analysis to improve patient care services and find better ways to manage resources and personnel.
  13. Telecommunication companies can take advantage of big data analysis to prevent customer churn, and also plan the best way to optimize both new and existing wireless networks.
  14. Marketing can use big data to perform sentiment analyzes to determine the level of customer satisfaction with the marketed products and services.
  15. Insurance companies can use big data analysis to categorize insurance applications that can be processed immediately, and which ones need to be validated by visits by insurance agents.
  16. Space and geophysical  can use big data to analyze the atmosphere and weather leads to an understanding of the Earth’s climate.
  17. And many more

How to Conduct Big Data Analysis?

Here are some types of methods or techniques in doing big data analysis:

  1. Text Analysis, is the process of analyzing text data (unstructured-data) such as blogs, emails, forums, tweets, forums and other forms.
  2. Data Mining, is a process of finding meaningful relationships, patterns, and trends of a large set of data.
  3. Machine Learning, is the establishment of an application or framework in the understanding of data and included in it is data analysis that is part of data mining
  4. Predictive Analysis (Predictive Analytics), included in data mining
  5. Statistical analysis, which is part of the data analysis and included in the data mining section
  6. NLP (Natural Language Processing)

So the discussion of big data analysis, may be useful for you.

Thanks (^_^)

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