Internet of Things (IoT) is the buzz phrase that’s kept the technology journalists busy since mid-2013! With the immense potential that this scenario has, organizations have started using the IoT angle in their work and have also added the IoT title to their executives and gadgets that are a part of the IoT enablement ecosystem.

With the hue and cry around IoT, even the Cloud companies couldn’t escape the IoT storm and in no time platform, software services, and mobility apps became IoT compliant. Cloud companies have taken the onus on themselves to develop effective computing capability for IoT platform.

IoT: The Real Picture

Unlike the complex definition of the Internet of Things, the setup in itself needn’t be as complicated. The effective practical application of the concept can result in greater Return on Investment (ROI) and drive efficiencies and insights for organizations that know how to utilize the same.

Industries across the globe are generating large amounts of data by connecting various devices (electronics, software, sensors). In the real world, connecting the various IT devices is just the first step in implementation of IoT. The important step is collecting data from these devices and drawing business insights by analyzing these data streams.

One of the popular notions is that IoT is an ‘always ON revolution’ where the devices (or things) are always active collecting data and storing them on cloud. For this the organization will need increased bandwidth requirement, large storage space, and increased number of big data analysis professionals for drawing useful insights from the available data.

However, the real picture shows a completely contradicting picture. Any particular sensor/device is switched on only when triggered. This means the system is ‘mostly OFF’ with less data captured than what is portrayed. Also, the local event processing and analytics programs decide what needs to be sent to the cloud. This further reduces the bandwidth of data that is sent to the cloud storage. With the involvement of video upload, the bandwidth of the entire data stream to the cloud is not more than 2-3 Mbps. If all the other overheads are considered, not more than 15-16 Mbps bandwidth is needed.

How will Businesses be affected?

With a number of companies joining the IoT wave, there are a few ways in which businesses will be affected. Here are a few of these predictions:

Emergence of Innovative Delivery Methods

Internet of Things is paving way to making business models that focus on customer needs and pain points. This will help businesses to saturate customers with touch points for brands, and thus businesses will need to innovate methods to deliver their existing product and also the new ones.

User Experience will hold the key

Once the touch points that connect customers to brand become interconnected, there will be a paradigm shift from interface experience to user experience. Smart designs of products will ensure the customer experience is a pleasant one, and the brand loyalty stays.

Services will be Market Differentiators

As Internet of things becomes increasingly popular, the market differentiators will be ‘Services’ rather than products. Services will be the driving factor for customers to map and navigate the emerging markets alongside the traditional ones.

This change from products to services and from utility to experience, will also initiate change in employee and department roles and functions. The marketing, customer experience, and information technology departments will converge, enabling customer satisfaction.

Transitioning to IoT

The six key factors that need to be considered for successfully implementing the IoT ecosystem for your business are discussed below.

Connectivity

The ‘Internet of Things’ need some real good connectivity and that’s why it is better to have a separate network which ensures no interruption from other connected devices. Isolated network will help network engineers to implement protocols, ports and services needed for the network.

Bandwidth

When IoT ecosystem is implemented, there will increased data usage, users, and devices joining the network. Thus, the bandwidth parameters need to be taken into account. Most companies prefer investing in expanding their bandwidth to suit the IoT requirements. As mentioned earlier, the IoT devices are small devices using relatively slower wireless connection, which sums up to small bits of information being exchanged. Thus, the overall traffic from IoT devices will be much lesser than that generated from normal internet usage

Platform

Choosing the right platform for your needs is one of the key factors while migrating to IoT. Xively, Thingsquare, Sensinode are some of the most popular platforms available. Supporting hardware, operating system, and programming are the key deciding factors while zeroing down on the right platform.

Security

Data security is one of the challenges that companies face while implementing IoT ecosystem. A complete network security system should include intrusion prevention systems, firewall, web security, and cloud computing internet intelligence.

Keeping a track of number and type of devices that connect to the network will help serve the security purpose.

Storage

The constant streaming of data from the various devices, user accounts, and web resources will need a huge storage space. Analytics platforms that can collect, organize, and analyze streams of data, needs to be implemented. This data will be further analyzed to draw important correlations/information to client business.

Educate Employees

Once the IoT ecosystem is being implemented, educating employees about the change in their roles and the IoT shift. Also the hiring needs to be aligned with this technology shift and candidates who have a firm sense of technology need to be hired.

Challenges Faced on Transitioning to IoT

Though IT Cloud and IoT Cloud have a lot of similarities, there is a 30-40 percent difference that can really hamper your efforts of launching these sensor-based services. The IT cloud service mirrors an ecosystem where more resources are allocated on the cloud side whereas in the IoT cloud service, the distribution of devices on cloud and the thing side is equal.

Working with Stakeholders

The biggest challenge while setting up the IoT ecosystem is working with a number of stakeholders. For developing an end-to-end IoT application, it is needed to collaborate with various technology experts, device vendors, IoT platform provider, system integrators and the like. Thus, building relationships with all these stakeholders is necessary.

Managing Remote Devices

The IoT cloud ecosystem consists of a number of sensors, gateways, and devices which will be extremely large and spread over various geographical regions, some of which may lie in remote locations. The biggest challenge faced by companies will be to remotely manage all these fully automated devices.

Interoperability of Devices

The measurement of data from complex networks will require a lot of sensors and other devices talking to each other. With each device manufactured by different vendors, the interoperability of these devices is definitely a challenge.

Data integration

Once the IoT system is setup, the data streams from various sensors, mobile devices, social networks and other web resources will be continuously available. However, the biggest challenge is to keep the semantics of data as a part of the data stream itself instead of dealing with it within the application logic.

Handling Huge Data Volumes

The IoT cloud system deals with a lot of devices, and this means companies will have increased data volumes that need to be collected, stored and analyzed situation at hand. This means the existing Big Data system architecture and platform will prove to be inadequate. Also, measuring real-time data performance alongside application level data without any latency is also a challenge faced.

System Flexibility

Though IoT is still naïve, as it evolves into a mature system, more sensors and applications with increased capabilities will evolve. Thus, the existing ecosystem and applications need to be flexible enough to incorporate these changes.

Data Security

Most of the data collected from the various devices will be sensitive personal data that needs to be protected from unauthorized access. Also, the user needs to allow the devices to collect the data for specific and defined purposes. For sharing data with authorized users and applications, tools need to be developed which also adhere to privacy policies.

IoT Case Study: Dundee Precious Metals (DPM)

The Internet of Things buzzword has caught attention of not only the newly developed industry segments but also the old ones like mining and manufacturing. Dundee Precious metals have proved that IoT can really turn things around, if implemented properly.

Problem statement: Increase production of DPM mining operation by 30 percent without increasing manpower and number of operating vehicles.

Solution:

With a target to achieve 30% increase in production without increase in manpower, the DPM IT team set up IoT system to capture data from various activities. The IoT ecosystem enabled capturing real time data such as miner’s location, equipment updates, and number of buckets filled. This made sure that the miners and mine managers had this key information that helped in keeping track of daily tasks that would eventually help in achieving their goal.

Wireless networks were set up which ensured communication link between drivers, miners and mine managers wasn’t lost, above and below the ground. A location tracking application that got data from Radio Frequency Identification (RFID) tag placed on miner’s caps and vehicles, made sure workers were safe in their region of work.

Connecting different DPM locations (managers, metallurgists, geologists, and the management) with such technology helped in achieving better understanding and enhanced business decision-making.

Results:

  1. Production increased by 400 percent instead of original 30 percent goal.
  2. Miner safety improved as miner’s movements and location is continuously tracked.
  3. Asset utilization of vehicle has improved due to the continuously transmitting data identifying repair needs.

[Source: Cisco Blog]

With Internet of Things taking the center stage, businesses across the globe should develop the in-house capacity to innovate means to utilize this ecosystem to their advantage. Big data, analytics, cloud computing experts, will be among the top-notch positions in organizations implementing IoT. Having the right talent on-board will ensure your business makes the most of the IoT system

With the right talent in-house and clarity on what, why, and how to do while implementing the IoT ecosystem will ensure organizations invent their own Internet of Things.

Image Source: engagor.com

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Professional Certificate Program in Data Engineering

Cohort Starts: 2 Dec, 2024

7 months$ 3,850
Post Graduate Program in Data Analytics

Cohort Starts: 6 Dec, 2024

8 months$ 3,500
Post Graduate Program in Data Science

Cohort Starts: 9 Dec, 2024

11 months$ 3,800
Professional Certificate in Data Analytics and Generative AI

Cohort Starts: 10 Dec, 2024

22 weeks$ 4,000
Caltech Post Graduate Program in Data Science

Cohort Starts: 23 Dec, 2024

11 months$ 4,000
Data Scientist11 months$ 1,449
Data Analyst11 months$ 1,449

Get Free Certifications with free video courses

  • Introduction to Cloud Computing

    Cloud Computing & DevOps

    Introduction to Cloud Computing

    2 hours4.683K learners
  • Introduction to Big Data Tools for Beginners

    Data Science & Business Analytics

    Introduction to Big Data Tools for Beginners

    2 hours4.57.5K learners
prevNext

Learn from Industry Experts with free Masterclasses

  • GenAI in Data Analytics: How to Take Your Data Analytics Career to the Next Level

    Data Science & Business Analytics

    GenAI in Data Analytics: How to Take Your Data Analytics Career to the Next Level

    28th Nov, Thursday3:30 PM IST
  • Career Webinar: Secrets for a Successful Career in Big Data

    Big Data

    Career Webinar: Secrets for a Successful Career in Big Data

    21st Sep, Wednesday9:00 PM IST
  • DE vs DA vs DS: Which Career Path Is Your Best Fit?

    Data Science & Business Analytics

    DE vs DA vs DS: Which Career Path Is Your Best Fit?

    7th Nov, Thursday9:00 PM IST
prevNext