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Keys To A Successful IoT Strategy

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Keys To A Successful IoT Strategy

Think about the architecture to collect and ingest data in real-time at the edge. The key to a successful IoT initiative is a sound business case and data management plan.

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To gather insights on the evolution of IoT to this point in 2017, we spoke to 19 executives who are familiar with the current state of the Internet of Things. We asked them, "What are the keys to a successful IoT strategy?" Here's what they told us:

  • Don’t try to tackle the entire beast at one time. Think about a broad-reaching strategy. Divide the workload up into smaller chunks to begin seeing value.
  • You need the architecture for big data to be able to collect data in real-time at the edge. This requires a hybrid cloud and on-premises solution. Crawl, walk, run – know what data you’re collecting today and be flexible enough to handle what you will need tomorrow.
  • Consider the complexity of the IoT ecosystem. Backends, devices, mobile apps for configuration, hardware design. Getting it right with different toolchains and stacks that must come together. How to service after the fact. Get automated so you can quickly patch security issues. You have to worry about security. Frameworks, platforms, operating systems, and applications architectures all need to address security. Every device needs to ship with a unique password rather than a standard password that you expect the user to reset.
  • Understand what you want to accomplish with IoT – have a strategy. What do you want IoT to do for you? Your answer will impact your technical strategy; however, it’s always better to start with AWS so you don’t have to build an infrastructure up front. Start with pre-built, pre-defined services. Grow your IoT business to a point where you can confidently identify that building internally is a better long-term investment.
  • While IoT is very cool, let’s start with what didn’t work with RFID. IoT is sensors with more data and reporting. What’s the business goal? How can we use the data “outside the box” to solve business problems? We can now know the gender and size of the shopper as well as a traffic conversion report for the mall and the store. Based on this information, what actions can we change to improve the customer experience as well as revenue? Feed the data into a platform with prescriptive analytics to see trends and correlations that will improve performance in ways members of your team haven’t been able to imagine. Use prescriptive analytics to take action that drives revenue. If you use all the IoT data in a new paradigm, how will you change your business?
  • Drive business value – articulate the business challenge you are trying to solve. Look at the solution architecture that will help solve the problem. Weave customer experience and business optimization into the discussion.
  • Identify how fast to go to meet your goals without getting ahead of the market. Verizon is building out its CAT-M wireless network just for IoT with a newer spectrum that requires lower power. Batteries for IoT devices will never need to be changed. We’re seen early adoption in manufacturing, industry, transportation, and logistics solving first order of magnitude problems. We will start to see newer technologies that use lower power and lower bandwidth and higher power and higher bandwidth when needed. Unfortunately, it’s taking three to six months to get a product certified for CAT-M so some companies are looking for WAN-options. Hub and spoke can work for defined farming locations but not shipping containers around the world.
  • All heuristics and data from the sensors are being fed back into a corporate, scalable repository. Most likely an object storage platform versus a SAN or NAN file system.
  • We work with IoT on both the producer and enterprise side. We seeing more growth from the producer side right now. Device manufacturers go through and evolution of connecting to the cloud, worrying about security, getting telemetry, and moving to everything as a service strategy to provide value-added services based on data which is the root of business transformation. Broader solutions are tied to service delivery. As you move up the maturity curve to security, connectivity, and more business focus all become more difficult to scale.
  • Leverage connected devices as an enabler of the customer relationship with the business. More devices = more applications = more challenges. Keep everything secure and respectful.
  • Looking at devices and data and determining how to harness the data, get insights and get the insights back out to the edge. Have a converged data strategy whereby you’re ingesting continuous streams of data, collecting the data across devices and geographies, and integrating the data to perform real-time analysis that provide real-time insights. Ability to leverage a stream of real-time data with historical data to do real-time streaming analytics. The database is where you operationalize the insights.
  • Focus on making incremental improvements. Start small, scale, and see value. See if you need to collect different data. There may be an opportunity to use predictive analytics to detect when something will fail. Learn what’s real versus hype. Prove the value of what you’re doing to get buy-in.
  • IoT service is evolving. Adoption is increasing. It takes time for standards to build out. The integrations and APIs will remain consistent. Quality assurance of API integration so developers and testers can test the integration between IoT devices. Integrations need to perform and scale well with security being the primary concern.
  • Energy management and cost of ownership. Know how to optimize energy efficiency.
  • IoT and smart home device system developers need to look at the big picture and understand that consumers want services—not just a bunch of connected devices—that make their lives safer, smarter, more comfortable and more convenient, like smart security, smart environmental control, smart energy and smart lifestyle monitoring. They do not want complicated installations and setups; they want services that learn how they and their families live their lives and then automatically configure the house to the needs and schedules of the people living inside. Smart home systems need to actually become smart. The connected home devices need to connect to the cloud to learn how the household functions and to evolve its services to efficiently meet the needs of the residents, ensuring their comfort and security. In some ways, the smart home will mature into a smart home butler that anticipates needs and makes lives more comfortable.
  • Define the use case regardless of the vertical. Be precise in your definition. Be prepared to address the challenges: new and disparate technologies with few standards. Plagued by security issues. Lack of technical and personnel resources.
  • Tying IoT projects to CX and customer value. Analytics around IoT should always create value for customers, like facilitating new pricing schemes based on usage, promoting driver safety by tracking driving habits, or determining best predictive maintenance windows based on devices usage patterns. Keep it simple. You don’t have to look at per-second streaming data to optimize your operations. For example, we have seen applications where device idle times are measured on an hourly basis. For expensive devices, fleet, orcapital assets, this drives huge amounts of savings. Not being tied down to the original rudimentary IoT data. For many companies, IoT data is often very basic. For example, it provides a time stamp, latitude and longitude, current state of a device, temperature, or an IP address. With the right data management platform, you can always infer more context from data. For example, one of our customers, Pulse Mining Systems, is using our analytics platform to transform time stamps into day, time, and AM versus PM shifts, to show the productivity of mining devices during different parts of the day, and by different shift crews. Look for cause and effect signals. In IoT, the device itself does not have the intelligence, but its surrounding ecosystem might. Merging IoT data with other sources creates intelligence and helps a company figure out whether the IoT data is telling you something that you didn’t know otherwise. We have seen examples where a coffee brewing manufacturer tests whether brewing coffee increases tweeting, answering questions such as: “Are people tweeting more as they are brewing their coffee?” This is tying in a machine’s usage data with social media data to examine a cause and effect syndrome. Then, the company can decide to leverage this discovery for advertising or building customer loyalty programs.

These insights came from:

  • Scott Hanson, Founder, and CTO, Ambiq Micro
  • Adam Wray, CEO and Peter Coppola, SVP, Product Marketing, Basho
  • Farnaz Erfan, Senior Director, Product Marketing, Birst
  • Shahin Pirooz, CTO, Data Endure
  • Anders Wallgren, CTO, Electric Cloud
  • Eric Free, SVP Strategic Growth, Flexera
  • Brad Bush, Partner, Fortium Partners
  • Marisa Sires Wang, Vice President of Product, Gigya
  • Tony Paine, Kepware Platform President at PTC, Kepware
  • Eric Mizell, Vice President Global Engineering, Kinetica
  • Crystal Valentine, PhD, V.P. Technology Strategy and Jack Norris, S.V.P., Database Strategy and Applications, MapR
  • Pratibha Salwan, V.P. Digital Services Americas, NIIT Technologies
  • Guy Yehaiv, CEO, Profitect
  • Cees Links, general manager Wireless Connectivity, Qorvo
  • Paul Turner, CMO, Scality
  • Harsh Upreti, Product Marketing Manager, API, SmartBear
  • Rajeev Kozhikkuttuthodi, Vice President of Product Management, TIBCO

What do you consider to be the keys to a successful IoT strategy?

This content is brought to you in partnership with AnswerHub. Engage with your developer community in an environment like Stack Overflow, to drive engagement and reduce support costs

Topics:
iot analytics ,iot data ,iot ,industrial internet

Opinions expressed by DZone contributors are their own.

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