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What Are The Keys To A Successful IoT Strategy?

DZone's Guide to

What Are The Keys To A Successful IoT Strategy?

Understand how you are going to get value from all of the data you collect and how this will improve the customer experience.

· IoT Zone ·
Free Resource

To gather insights on the current and future state of IoT, we talked to 23 executives involved with IoT. We asked them, "What are the keys to a successful IoT strategy?"

Value

  • Understand that collecting all of the device data is the easy part. How you use and derive value from the data is the most important step.
    1. What is the product or service being created? What is the benefit being provided? What is the value you are adding?
    2. What is the business model? Subscription models are more successful if you are adding convenience or value. Your strategy needs to be clearly articulated.
    3. Operational efficiency. Evaluate and operationalize better to save money. You need to articulate the value to the customer. What’s the ROI? You need a new stream of revenue to boost the bottom line. Everyone collects a lot of data. Not all of the data is useful. Analyze the data remotely and decide what is sent back to the data repository. Filter to capture the most meaningful information to save money and see insights more easily.
  • I believe the core components of IoT — connectivity, sensor data, and robotics — will ultimately lead to a requirement for almost all ‘dumb’ devices in order to become intelligent. In other words, the IoT needs smart machines. Hence the need for AI. AI will have a profound impact on every aspect of our personal and working lives — an impact that will be magnified and multiplied by its combination with the IoT. To get there, companies need to discern what data customers want (and will be willing to pay for) to decide on which sensors or communication protocols, for example, best suit their product and their customer. IoT is prompting companies to form a new mindset around servicing clients. So, businesses must move rapidly to identify how they’ll derive value from combining AI and IoT.

Business Need

  • What is the business need? What is the end user use case? Identify the appropriate technology to solve the business need. Define how success will be measured.
  • How are you gathering data from the edge to make real-time, informed business decisions?
  • Understand the complexity of deploying a successful project. How to develop the business model to provide new services to the customer. Ensure this makes business sense.
  • Identify a real need. Perhaps it’s your mission like ours is to reinvent the wasteful beverage supply industry. Use devices to ensure you are providing a great customer experience as well as gathering insights from customers. More sensors in the machine provide more information so you are able to troubleshoot problems (i.e. we are able to know if there is a water pressure problem or a problem with one of the parts of our dispenser before dispatching a service tech).
  • Get all of your data in one place. Operationalize the data and use it in a real application to solve a business need or improve the customer experience.
  • Operationalize the entire network with business analytics. Increase production at lower cost with automation.

Customer Experience

  • What’s good for the consumer? What’s good for the business?
  • When it comes to CX:
    1. Establish a digital touchpoint with the customer themselves.
    2. Consider a bridging solution for self-service apps rather than direct connection by scanning a QR code or serial number for quicker resolution.
    3. Create a digital touchpoint into the client environment. Close loop on the backend to know how to improve CX, UX, parts, and service. Integrate frontend and backend. Use AR where additional support is required.
  • Clients often start with the technology. We try to get them to focus on the business value, engagement, personas, and capabilities. Solve pain and improve gains.

Other

  • Having a data fabric platform that enables applications to be holistic and inclusive of all data, real-time and legacy, seamlessly and securely.
  • Make it as easy as possible for the end user and minimize work needed by developers. Start with a kernel of usefulness. Start small and scale.
  • Every IoT application is a time series project. The collection, storage, analysis of data all with a time stamp. Business insights to solve problems. A different topology of use cases from smart meter environments to green wall technology monitoring.
  • The lessons we've learned include:
    1. Ensure the device is instrumented so the features can be monetized.
    2. Ensure the software on the device, especially Open Source, has a bill of materials with security vulnerabilities identified.
    3. Manage the initial distribution, installation, and updates once the device is in the field.
  • Be successful integrating devices with the software stack underneath and the apps created on top.
  • The keys to a success IoT strategy involve prioritizing data security, privacy, and protection. The first wave of IoT lacked necessary security features, so we want to ensure that our IoT sensors are among the most secure technologies on the market. Another tenant of success is to build a platform that aggregates data from multiple sensors into one unified platform/experience that leverages the latest machine learning and AI technologies. Having video as the primary sensor sets these platforms ahead. Be able to explore hypotheses with AI/ML to see trends humans are unable to see.
  • Adopt an iterative process whereby you are continually improving your device based on the data you are receiving. Be able to update your software and sensors remotely.
  • Success requires developers, manufacturers, security vendors, VARs, MSSPs and regulators across the entire device ecosystem to act in concert and strike a balance between ease of use and security. Market forces, as well as who responds best to the never-ending stream of new threats, will shape the eventual winners and losers in the IoT and connected devices market.
    Today, the vast majority of IoT devices are being deployed by the average home user. These users are not experts in networking and will gravitate toward devices that are simple and easy to install. To that end, device manufacturers need to ensure the ease of deployment does not significantly expand the attack surface of the average home network. In the past, we have seen devices which automatically open ports on a user’s home router/firewall, allowing direct access to IoT devices from the internet.
    While this makes it easy for users to remotely access things like video camera feeds, it also broadens the attack surface for the home network (making it a much easier target for malicious actors). Manufacturers have made improvements with the design and implementation of accessing devices remotely, and need to continue to include security in architectural design decisions.
    At the same time, network security vendors, VARs and MSSPs are recognizing that as the complexity and scale of attacks continue to increase, they can no longer play a “reactive” game trying to use signature-based approaches to prevent attacks. Rather, they need to harness much of the same technology and techniques of hackers, such as advanced AI-based systems to proactively identify anomalous behavior and predict future threats.
  • So many landscapes to choose from. Go with the platform that will solve your needs.

Here’s who we spoke to:

Topics:
iot ,cx ,use case ,iot platform

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