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Insights of Big Data Analytics

  “Time and money are your scarcest resources. You want to make sure you’re allocating them in highest-impact areas. Data reveals impact, and with data, you can bring more science to your decisions.”

– Matt Trifiro​

  • Big data analytics is the study of large amounts of data to display hidden patterns, correlations and other insights
  • Big data analytics assists organizations to harness their data and use it to discover new opportunities and enlighten their business decisions
  • Big data analytics helps to analyze data sets and draw conclusions from it almost immediately with today's technology
  • Big data analytics is a form of advanced analytics, which includes complex applications with elements such as predictive models, statistical algorithms that is powered by high-performance analytics systems
  • Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by traditional analytics programs

Importance of Bid Data Analytics:

Big Data Analytics have tremendous applications across all industrial standings, in personal and industrial synopsis that has successfully advanced not just organizational productivity but the economy as a whole.
Big Data Analytics got its value in the following ways:

1. Reduction of expenses:

  • Big data analytics had become more easy by implementing advanced technologies such as Hadoop and cloud-based analytics, which brings significant cost advantages when it comes to storing large amounts of data.

  • Also, they can identify more efficient ways of doing business of managing and minimizing indirect costs.

Analytics platforms make tests less time-consuming and less expensive than traditional analytics. Data Analysis Makes Expense Reduction More Straightforward.

2. Decision making:

  • Companies are utilizing the data collected from consumers to generate actionable insights that promotes the brand to enhance its customer loyalty and profitability.

  • Companies are influencing the data to automate processes, optimize selling strategies and strengthen the overall efficiency of their businesses.

Big Data analytics are used to reduce network errors, optimize resources, and improve customer experience by analyzing real-time data. This has helped the brand to increase its output ratio.

3. New products and services:

  • Big data analytics come with the ability to measure customer needs and satisfaction through analytics by giving the customers what they want.

Big data analytics provide a wide range of applications in manufacturing, healthcare, government and retail industries as it allows you to automatically convert unstructured data into meaningful insights that feed the production and leads to build predictive models.

Features of Big Data Analytics:

Big data analytics can be achieved only by collaborating with various technologies that work together to help you get the most value from your information. So the big players are,


1. Machine Learning:

In the machine learning process, the more data you provide to the system, the more it can learn from it, returning all of the clues you were looking for, and that is why it works so well with big data analytics.


2. Data Management:

Big data management refers to the efficient handling, organization or use of large volumes of structured and unstructured data belonging to an organization.


3. Data mining:

Data mining refers to the activity of going through big data sets to seek relevant or suitable information.


4. Hadoop:

Hadoop supports the storage and processing capabilities of extremely large datasets in a computing environment that is distributed across branches.


5. In-memory analytics:

In-memory analytics helps improve the overall speed of a Big data system and provides users with faster answers for queries that take a long time to process in a large database.


6. Predictive analytics:

Predictive models used to forecast future probabilities which are applied to businesses to analyze current data and historical facts in order to understand customers better and to identify potential risks and opportunities for a company.


7. Text mining:

Text Mining is used to process unstructured information to extract meaningful numeric indices from the text, thereby making the information contained in the text accessible to the various data mining algorithms.

“The goal is to turn data into information, and information into insight.” 

– Carly Fiorina