Typical Industrial Use Case: - Machine and Sensor Data Analytics

“This use case mainly focuses on the Real time streaming data that are coming from the sensors installed inside mines. Machines with high configurations (like memory storages, RAM capacity) have sensors installed on it. Every machine is installed with 2000-3000 sensors and each sensor will produce 1 to 2 MB of data per Sec. The entire mining area has more than 1000 machines.

All the systems are monitored by centralizing administrative network. So the size of data that is going to generate per machine is 20 Terabytes/hour. This is going to be a big challenge to process and do analytics on this huge real time geographical data (Temperature, Humidity, Seismic activity, Radiation activity). In this use case, two types of analytics involved are predictive and prescriptive.”

Processing Peta bytes of streaming data and predicting the weather patterns based on Statistical algorithms in a fraction of seconds may give the solution for identifying the potential Risk Analysis Patterns. Here the weather prediction and disaster risk analysis engine provides the required solution for the use case. This is the best solution provided by the BDA to the Mining Industry for Disaster Risk Management.

An Enterprise distribution of Hadoop provides enterprise class architecture for BDA and for its applications. These are Cloudera distributed Hadoop (CDH), Horton works, IBM Big Insights and MapR. For Real Time Online analytics, IBM had come up with IBM Info sphere streams. The enterprise editions provide scalable, secure, distributed environment for real time and offline data analytics.

Type of Analytics With BDA

Using BDA, we can perform different type of Analytics, namely Predictive, Prescriptive, Descriptive and Cognitive. Among all, Predictive is important to get the useful insights from the data. Predictive analytics are mainly performed by applications which needs to predict patterns in Real Time Analytics scenario. Predictive analytics improve fraud detection and speed up claims processing.

Cognitive Analytics working on cognitive computing systems can help business organizations, in terms of fast, accurate results by using Neural and Robotics like environments and setup. These systems learn and interact naturally with people to extend what either humans or machine could do on their own.

Typical live example is IBM Watson computer won the Jeopardy game in 2011 which is a purely super computer which won against former two champions of the Jeopardy game. The IBM Watson computer, purely works on cognitive system and it works on NLP (Natural Language Processing).

Industries in which big data had made Big Difference

The Industries such as Telecom, Retail, Finance, Healthcare, Banking, Energy and Automobile Sectors widely use Big data and its analytics and getting benefit in the current global market. Data volumes and data generation speed are significantly higher than it was before. All this new kind of data require a new set of technology to store, process and to make sense of data.

Predictive analytics improve fraud detection and speeds up claims processing. As a result of these, analytics gives more effective marketing, better customer service and new revenue generating opportunities in different industrial domains.

Comparing to traditional methods and approaches, the Big Data is adding so many features to present market trend in terms of sales and revenues. The Typical applications of these Big Data Analytics include weather predictions, Geospatial pattern recognition, Disaster management and Space Technology. One of the main functions of an ETL tool is to transform structured data. The transformation step is the most vital stage of building a structured data warehouse.