For Industrial IoT applications, Big Data will increasingly be replaced with contextualized, structured data. And here’s why.
Industrial systems have data continually streaming from devices, controllers, historians, databases, and industrial computers. Data can also be locked up in closed, proprietary systems.
The promise of The Connected Enterprise is to transform this data into actionable insights to increase productivity and create new business value – including faster time-to-market, operational productivity, asset performance, and enterprise risk management.
How Much Is Too Much?
It is easy to amass Big Data in industrial applications.
Because it’s possible, the thought right now is to gather every bit of data from everywhere, store it in a data lake or database, and then utilize artificial intelligence (AI), machine learning and data science to extract actionable insights.
The downside can be too much data and too little insight.
Data volumes can quickly get into petabytes (a petabyte is 1015 bytes or 1000 Terabytes). Here’s how quickly that adds up: An oil and gas company can collect 500 GB of data per day from a single compressor, and data volumes can easily exceed a petabyte in one year.
If you’re downloading and playing one petabyte of MP3 encoded songs it will take about 2,000 years to play your entire list.