The Internet of Things (IoT) tech with its deeper and ever stronger roots into the human’s everyday lives – from smart wearables and watches through to smart TVs and smart home appliances, is seeing a massive rate of adoption. The markets are oozing with IoT presence, IoT today has even penetrated households in third world countries. Businesses, financial institutions, governments, and other organizations are increasingly utilizing the technology to track everything from machine performance through to maintenance requirements to an individual’s productivity.
For example, IoT sensor devices are used in a production factory to track raw materials available to auto-replenish them to avoid factory floor downtime and machine readiness to automate predictive maintenance. Industries like healthcare use IoT devices to remotely monitor a patient to provide effective patient care, to perform robotic surgery for precision cuts, or to dispense patient medication.
With all these growing sensors and connected devices, the amount of data gathered on a daily basis is in hundreds of petabytes to tens of exabytes. This abundant ever-growing data always accompanies preparation, management, and storage challenges. To harness business value from all this IoT and big data organizations must be quick in tackling this disparate and unstructured array of data.
Today, any data project, whether big or small always takes up to 80% of the time in data preparation, the more the data, the more time-intensive the data preparation process becomes.
After the huge size of the data, comes the complex nature of this data. Not all data is useful for an organization, converting this data, which comes in many formats like textual data, image data, video clip etc., into metadata to gain insight is extremely complex. Often, organizations face the need to timestamp or geotag the data or combine it with more structured sources, such as CSV files. Today’s organizations must innovatively implement their resources to prepare the increasingly complex IoT data.