6 FAQs about Big Data Analytics

Data Analytics

Big data analytics refers to analyzing an enormous volume of data to gain insights and trends. Data analytics has become crucially important for businesses, and it can help enterprises make better business decisions. Here are frequently asked questions about big data and analytics. 

  • Is Big Data All About Terabytes of Data?

No, size is only one factor of big data. Several aspects like diverse sources and different data types add to the volume. For example, data analytics can correlate points drawn from various sources, either within or outside the organization. 

Also, big data analytics is not only limited to structured data; it also extends to semi-structured data (RSS feeds, XML files) and unstructured data (human language text). It also includes data derived from audio and video files. 

  • Can Data Analytics Be Integrated Into AI Solutions?

Yes, AI solutions can integrate data analytics. The AI solution incorporates machine learning algorithms that eliminate manual and time-consuming data science tasks. 

Since most businesses have moved their systems to the cloud for better security and performance, cloud environments can host AI solutions. They can collect and analyze data from diverse sources in real-time and provide organizations with actionable insights. 

  • Can Data Analytics Help in Failure Prediction?

AI solutions with integrated big data analytics can use predictive models to estimate failure probability. It is a part of advanced analytics known as predictive analytics. The AI system uses historical data with statistical modeling, machine learning, and data mining techniques to predict future outcomes.

Manufacturing companies use predictive analysis to find patterns in data and identify risks. 

  • Can Data Analytics Help With Maintenance Planning? 

Manufacturing industries play a crucial role in our society. Implementation of IoT has helped manufacturing companies achieve sustainable production. However, high maintenance is essential to maintain digitalized manufacturing. AI solutions with integrated data analytics capabilities can continuously enhance maintenance excellence with a different focus. 

The maintenance program can be designed to achieve zero breakdowns and optimality in production. The AI solution can provide early fault detection and improve reliability, performance, and availability of rotating equipment. In addition, the machine learning algorithms can read the machine data and give automatic advice for impending conditions. 

The IMx data collected by machine learning can help you schedule maintenance proactively and avoid unplanned machine downtime. It can also minimize machine repair costs and prolong machine availability. 

  • What Kind of Analytics Goes With Big Data?

The analytics applied to big data is generally known as “advanced analytics.” However, experts mention that advanced analytics can also be described as “discovery analytics,” where you are trying to accomplish something with the data and analytics. For example, it can help organizations know new business facts they did not know before. 

  • What Kind of Analytical Tools Are Used With Big Data?

AI systems used for big data analytics employ various tools to analyze and derive critical information from data sets. The analytical tools used are SQL queries, natural language processing, text analytics, data visualization, artificial intelligence, fact clustering, statistical analysis, and data mining.

There is an extensive arsenal of analytical tools available for different purposes. First, however, you need to determine your analytical requirements and then select tools that meet your requirements.

These were some frequently asked questions about big data analytics highlighted to help you understand what it is, how it works, and different scenarios for its use.


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