How to Avoid a Phased Approach When Integrating Big Data With IoT

McKinsey reports that by 2025 the IoT industry will have amounted to $6.2 trillion. The IoT ecosystem being developed right now will rely on data generated by devices equipped with sensors and actuators. But the big question is, are companies ready for the huge explosion of data? Taking into account that a self-driving Google car generates 750mb of raw data per second, the average big organization will be dealing with petabytes of data per day. With increasing data pools, challenges such as data harvesting, storage and analysis become more complex. Many companies employ a phased approach or what is called the batch processing method to deal with the huge flow of data. In this DIY model, companies optimize their data warehouses and employ tools such as Hadoop and MapReduce to provide enough storage and computing power to the new influx of data. The ultimate aim for organizations is to effectively use this data to gain new business insights.

But the problem is it often takes days to process data through the phased approach. The value of any data diminishes with its age with the drop in the value being close to exponential at the beginning. And so companies utilizing the basic phased model to integrate Big Data with IoT do not fully realize the business value these sources can generate. Big Data Streaming Analytics offers real-time processing of data in motion, i.e. as the data arrives, it is analyzed. Based on the most recent data, it provides predictive analytics and immediate actions possible after processing data. Hence, you get real-time intelligence to develop your business. Such real-time streaming analytics needs a big data management platform that can operationalize a predictive model and can evolve and improve with time.

If companies are looking forward to make the most out of the data collected in real-time, they should expunge the use of traditional data warehouses with the phased approach and make use of a big data management platform which has the following features:

  • The data must be collected and streamed from all sources in real-time. Machine data is generated and streamed in real-time to data processing platforms like Hadoop which have an enhanced scalability feature. Relational data, on the other hand, can be streamed in real-time, or in a micro-batch.
  • The platform must be able to ingest millions of events per second. These data streams are compared with multiple data streams of historical value. Data redundancy is eliminated, anomalies are detected and user-defined action is triggered when a specific set of conditions is met. Analytic models are tested and validated, and the ones with the highest predictive powers are separated.
  • The most suitable predictive model is selected by the platform and implemented into operation in real-time.

So, by utilizing a proper platform for Big Data Streaming Analytics, companies can fully realize the potential of the IoT scenario by improving operations, cutting preventable losses, and above all, by exploiting missed opportunities.

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Vik is our Brand Journalist and Head of Online Marketing / PR with 11+ years of international experience in IT B2B. He's also a guest blog contributor to business2community, SitePoint, Journal of mHealth, Wearable Valley and other e-zines.

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