big data analytics
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Contents

What is big data?

Big data analytics examines massive amounts of information to uncover hidden patterns, correlations and alternative insights. With today’s technology, it’s attainable to research your information and acquire answers from it quickly – an endeavour that’s slower and fewer economical with a lot of ancient business intelligence solutions.

History:

The thought of big data has been around for years; most organizations currently perceive that if they capture all the information that streams into their businesses, they will apply analytics and acquire important worth from it. however even within the 1950s, decades before anyone expressed the term “big data,” businesses were victimisation basic analytics (essential numbers in a spreadsheet that were manually examined) to uncover insights and trends.

Top 10 Usage of Big data analytics:

  1. Understanding and Targeting Customers
  2. Understanding and Optimising Business Processes
  3. Personal Quantification and Performance Optimisation
  4. Improving Healthcare and Public Health
  5. Improving Sports Performance
  6. Improving Science and Research
  7. Optimising Machine and Device Performance
  8. Improving Security and Law Enforcement
  9. Improving and Optimising Cities and Countries
  10. Financial Trading
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big data analytics

How does it work?

Big Data analytics does n’t work alone, it is a combination of multiple technologies that make Big data Analytics possible they were,

  1. Data management
  2. Data mining
  3. hadoop
  4. In memory analytics
  5. predictive analytics
  6. text mining

Data management:

Data has to be top quality and well-governed before it may be faithfully analyzed. With data always flowing in and out of a company, it is important to determine repeatable processes to make and maintain standards for data quality. Once data is reliable, organizations ought to establish a master data management program that gets the whole enterprise on an equivalent page.

Data mining:

Data mining technology helps you examine massive amounts of knowledge to find patterns within the data – and this information is used for more analysis to assist answer advanced business queries. With data mining software, you’ll be able to sift through all the chaotic and repetitive noise in data, pinpoint what is relevant, use that data to assess seemingly outcomes, then accelerate the pace of making informed decisions.

hadoop:

our previous article Everything you need to know about Apache Hadoop covers about this section.

In-memory analytics:

By analyzing data from system memory (instead of from your hard disk drive), you’ll be able to derive immediate insights from your data and act on them quickly. This technology is in a position to get rid of data prep and analytical process latencies to check new scenarios build|and build} models; it isn’t solely a straightforward way for organizations to remain agile and make better business decisions, it additionally permits them to run iterative and interactive analytics scenarios.

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Predictive analytics:

Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to spot the probability of future outcomes based on historical data. It’s all about providing the best assessment of what is going to happen in the future, thus organizations will feel a lot of assured that they are creating the simplest potential business decision. some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.

Text mining:

With text mining technology, you’ll analyze text data from the web, comment fields, books and alternative text-based sources to uncover insights you hadn’t noticed before. Text mining uses machine learning or natural language process technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to assist you to analyze large amounts of data and find out new topics and term relationships.