Internet of Tomorrow: Sensor Data Analytics and Challenges

Manufacturing industry is preparing for a future in industrial internet of things (IIoT) and Analytics. There are huge benefits of exploiting data in multiple areas, including business process improvement, rapid customer driven product development, supply chain efficiencies etc., to gain better market insights and to improve sales and customer service. Accenture estimates that IIoT could add $14.2tn to the world economy by 2030 by improving productivity and reducing operating costs.

Collecting, storing and analyzing data from industrial sensors, network logs, connected devices and machinery using Internet of Things has become more feasible because of the emergence of big data technologies. International Data Corporation (IDC) has forecasted that there will be 28 billion sensors in use by 2020, with $1.7 trillion in economic value in an article published in Jun 2015.

SENSORS

Industrial sensors in combination with other smart devices can be used to detect real time physical data for just about anything (example: gravitational acceleration, humidity, pressure, photosensitivity, vibrations, sound levels, chemical concentrations, the list goes on) and the output is called the sensor data. This mammoth volume of data produced and transmitted from sensing devices can provide a lot of information. This data needs to be analyzed using statistical and other techniques to provide insightful information to all stakeholders.

DATA OCEAN

Let us take an example of any rotating machine (turbine, propeller or motors) the sensor can detect the vibration having a constant frequency pattern and any defects that occur within the sub-components of this equipment will have a direct relation to change in the frequency pattern. This will help service engineers to act pro-actively and provide required predictive maintenance. This sensor information if appropriately channelized have opportunities to improve the products and applications that are existing today and innovate future solutions for financial benefits. However, the technology comes with its share of challenges and hurdles that we need to overcome on an ongoing basis.

CHALLENGES

One of the challenge is to store, interpret and provide real time analysis of the data. Although this has become more efficient and easier than ever with the technological advancements in Big Data Analytics. There is large amounts of other information that is collected which may not be useful but just add to the noise and increase the cost for storing and analyzing this data.

In addition, using the available packages in cloud storage and analytics, this information can be easily available and accessible by unintended audiences. Therefore, it is extremely essential to build the required level of security and privacy into the products.

IMPLEMENTATION

The key for businesses to implement IIOT is to identify implementation feasibility and value add using secure storage methods for their data as well as to understand the risks in sharing the data.

It is important for organizations to identify and define their problem clearly, which has a potential to be solved using sensor data analytics. Organizations should introspect their current business models and the viability to integrate their IIOT platforms with their operational models. Product, Design and Supply Chain leaders in accordance with experienced Technical Service teams play a key role to choose the right cloud architecture if trained adequately to incorporate security solutions into products.

Security in sharing sensor data based on the requirements segmented at various levels

1. At Source of the sensor – Local Level (ex: factory)
2. Intermediate cloud – Partners Level (ex: supply chain partners)
3. Final Cloud Application – Global Level (ex: shared cloud platforms)

BOTTOM LINE

The Internet of Tomorrow using sensor data analytics bring in unlimited opportunities that are to be harnessed within the manufacturing industries. Ensure right investments are made with clear understanding of the business needs and appropriate resource requirements. Privacy and Security challenges need to be addresses with strong security measures at each level of the connected devices and cloud architecture technology.

Note: The views expressed in this article are my own.