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Big Data Analytics : Definition, Types, Benefits and Application

Big data itself is a term for large amounts of structured and unstructured data sets that cannot be processed by conventional methods. While data analytics is a term that includes various techniques and methods of processing raw data to get conclusions about certain information or issues. So that big data analytics can be interpreted as a method and process of processing big data to produce certain insights such as market trends, customer preferences, and other unstructured data as input for the company. Big data analytics covers the entire process of collecting, structuring, and analyzing data from various company data sources.

Types of Big Data Analytics

The types of big data analytics are more or less the same as the types of data analytics in general, namely:
Descriptive Analytics

This type provides a summary of previous data into an easy-to-read form. Descriptive analytics contains an explanation of what has happened over a period of time, such as statements of earnings, sales, revenue, etc.
Diagnostic Analytics

This type focuses on analyzing why something happened. Diagnostic analytics takes more diverse data inputs to find facts than and hypothesize. Here data recovery, data mining, and tracing are carried out.

Predictive Analytics

Predictive analytics is a type of data processing to predict possibilities that will occur in the near future, for example such as predictions of customer trends and market trends.
Prescriptive Analytics

Prescriptive analytics combines information obtained from previous analyses to provide suggestions or determine actions to be taken next. The process utilizes machine learning technology and artificial intelligence.
Benefits of Big Data Analytics

Big data analytics provides many benefits for companies, especially in finding new opportunities which will certainly also increase profits. In addition, there are also other benefits such as:

Increase Productivity and Efficiency

Big data analytics can collect and process large amounts of data so that more inputs are obtained that can increase the product’s work activities, both product improvement and development (upgrade).

Big data analytics can also work automatically with the use of AI and machine learning so that it can reduce the risk of data processing errors.

Accelerate Decision Making

With the use of technology, large amounts of data can be processed quickly, including when there are new data sources. This allows the root or core of the problem to be found quickly, so decision-making for the next step will be more efficient.

Knowing Market Conditions

Big data analytics includes analysis of market data or markets along with ongoing trends. With these inputs, the company can produce products that can compete and compete even superior to its competitors in the market, because customer personalization has been carried out.

Improving Customer Experience

Big data analytics is able to improve the user experience by analyzing the digital footprint that customers leave behind to reveal a lot about behavior, needs and what customers like or dislike. Thus, the company can renew its products to meet these needs.

Reducing Costs

Big data analytics uses the power of technologies such as the cloud that has unlimited storage, to store its data, thereby reducing the cost of using many storage devices.

In addition, using tools such as Hadoop and Spark can identify ways to do business that are more efficient and also cost-effective. AI and machine learning can also predict the costs required.

Steps to Implement Big Data Analytics

  1. Data extraction : collection of data from website pages into the database
  2. Data mining : identification of information from the database
  3. Data collection : data storage in a database that continues to grow
  4. Data storing: data storage in storage (storage space) with large capacity and infrastructure with the latest analysis engine
  5. Data cleaning : filtering and deletion of unnecessary data
  6. Data analysis: processing various types of data from various sources to generate insights
  7. Data consumption: the use of data that has been processed according to the needs of certain companies or agencies

Examples of Application in Various Fields

  1. E-commerce : predicting market and customer trends and pricing
  2. Marketing : analyze the most efficient marketing media for the products presented and can reach more customers
  3. Education: improving the quality of learning including material and effective learning methods for various levels of
  4. education
  5. Medical : knowing the patient’s condition by historical analysis on the patient’s medical records
  6. Media and entertainment: provide recommendations for movies, videos, music, etc. according to user preferences based on search history or the most frequently viewed
  7. Banking : offers a wide range of services based on customer income and expenses
  8. Government : assist in the establishment of laws or regulations according to the situation and needs of the community

This is the explanation of Big Data Analytics. Let’s optimize digital skills..

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