IoT market is growing and it will disrupt industries like manufacturing, logistics, and others. For example, the transportation of billions of units of COVID-19 vaccines will require IoT sensors to measure the temperatures of vaccines over the entire supply chain. This is critical because vaccines are exposed to heat which could become ineffective.
There were just over 17 billion internet-connected devices in use worldwide in 2018, and this number is estimated to exceed 55 billion by 2025. However, implementing IoT is difficult due to challenges like the complexity of components in the IoT ecosystem. According to studies, the average time-to-market for an IoT project is around 18-24 months, and 75% of IoT initiatives fail. Therefore businesses are recommended to follow common steps and the best practices of IoT implementation.
IoT Implementation Steps
Even if your business expectations on IoT projects are high and you can afford the technical tools and human resources, focusing on proofs of concept (PoCs), sandboxes, human resource alignment, pilots, and narrowing use cases for IoT deployments are advisable. This will enable you to learn from your mistakes and be encouraged by success.
1. Identify IoT objectives & IoT use cases suitable for your business
As with other digital transformation initiatives, IoT implementation starts with the identification of project objectives. It would be best if you determined what your organization wants to achieve through IoT technology. Each organization has different needs; some may aim to decrease operational costs while others target customer experience.
It may be hard to identify use cases that suit your business needs. IoT consulting companies help businesses understand IoT technology. Or you can check out our list of IoT applications based on company objectives, and create a roadmap to achieve your business goals.
2. Select necessary IoT components suitable for your use case
Hardware and software selection is a critical decision during implementation. IoT projects involve various tools, and businesses need to be careful about these systems’ connectivity and interoperability. Required components in IoT implementation include
- sensors to collect data such as weight, volume, temperature, humidity, pressure, etc.
- edge gateways to serve as a network entry point for devices and sensors talking to cloud services
- communication protocols for machine to machine (M2M) communication like SigFox Zigbee, 6LoWPAN, etc.
- IoT platforms to transmit information from a variety of hardware to the cloud and manage devices
- cloud data management and analytics software to transform generated data into insight.
3. Implementation & Prototyping
IoT requires a team that contains a mix of experts across IT and operations to work together. It would be best if you started implementation by building an IoT team that meets the requirements of selected use cases. Skills you may need during the IoT journey are listed below. However, this depends on the exact project. Many companies rely on turnkey IoT solutions and only need to oversee solution implementation which requires significantly fewer resources.
- Industrial & embedded systems design
- Electrical & mechanical knowledge
- Back-end & front-end development
- General technical expertise
A team with these skills can build IoT devices and implement the network; however, you need to enhance your team with data talent to make collected data useful. Skills your IoT team may rely on after implementation are listed below:
- Information systems expert to handle data storage
- Data scientist to analyze the data gathered
- Statistician to assist in data analysis and quality control.
4. If necessary: Integrate IoT system with other advanced technologies
After sensors start collecting and storing data, businesses can introduce new technologies such as analytics, machine learning, and edge computing to IoT infrastructure.
For instance, cognitive IoT is the use of machine learning in combination with data generated by connected IoT devices and the actions those devices can perform. The growth of unstructured data collected from IoT devices exceeds that of structured data. Cognitive IoT technologies aim to understand and learn using both structured and unstructured data for training and continuous improvement.
5. Apply necessary security measurements
Data security and privacy are the businesses’ concerns. IoT security breaches are common and businesses need to inform their data security officer about IoT projects to ensure that data governance best practices are integrated into the project. If necessary, GDPR compliance should be considered. In addition, IoT security solutions can be integrated to minimize security breaches. Endpoint security, communication protocols, access control, encryption, and fraud management are some measures you can take to enhance data security and privacy.
Challenges during IoT implementation
1. Compatibility & Longevity
IoT infrastructure involves various tools, sensors, and devices, and each vendor is competing to become the standard. A successful implementation requires the integration of IoT components with existing systems. Some compatibility challenges are non-unified cloud services, lack of standardized M2M protocols, and diversities in firmware and operating systems among IoT devices.
For example, as a transport mechanism between devices and hubs, there are ZigBee, Z-Wave, Wi-Fi, Bluetooth, and Bluetooth Low Energy (BTLE) protocols. This variety causes difficulties in implementation and requires the deployment of extra hardware and software when connecting devices.
Learn more about IoT communication protocols.
2. Security issues
Though IoT projects provide different business opportunities, adding new devices to your network increases the risk of cyberattacks. According to studies, 57% of IoT devices are vulnerable to cybersecurity attacks. This makes cybersecurity the biggest barrier to IoT implementation.
3. Data storage issues
Once you deploy IoT systems, your database grows exponentially. To capture IoT data and perform analytics, organizations need high-capacity and high-speed storage along with advanced memory processing technologies.
4. Power management of IoT devices
Though there are IoT devices that work via AC power, industrial IoT (IIoT) involves devices that are located in extreme conditions, and they use their battery as their only power source. Companies should track when the battery of an IoT device needs to be recharged or replaced. Finding devices that conserve or produce power when not in use enables businesses to design a sustainable IoT system. Especially when a device is placed in a difficult place to access, battery replacements can be overwhelming.
5. Unstructured data processing that requires data cleaning
IoT sensors collect unstructured data that is difficult to use for analysis. Collected data may contain anomalies if the sensors’ environment or systems are not stable. It is important to identify such data quality issues to improve decision making.
Learn more about data cleaning.
6. Analytics challenges
IoT analytics are applications that help analyze data obtained by IoT sensors to make better and data-driven decisions. IoT analytics has specific challenges but common analytics challenges also apply for IoT implementation.
Interested in learning more about IoT implementation and other key technological advances that are changing the way we do business? Check out our research for a wide range of IoT/AI-related topics or contact us:
if you believe your business would benefit from adopting an IoT platform, feel free to check our lists of:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
To stay up-to-date on B2B tech & accelerate your enterprise:Follow on
Next to Read
Your email address will not be published. All fields are required.