IoT Analytics – Part 6

This is the sixth and final article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide a series of “best practices” and “lessons learned” for what companies are seeking from deploying IoT analytics. Previous parts to this special feature:

Part 1: Internet of Things – An Overview

Part 2: Marriage of IoT Analytics and the Cloud

Part 3: The Rise of IoT Analytics

Part 4: IoT Analytics Value Drivers and ROI for the Enterprise

Part 5: Challenges of Deploying IoT Analytics

IoT Analytics Best Practices

In this section we’ll provide a number of high-level checklist items that summarize “best practices” and “lessons learned” for what companies are seeking from this developing technology. The list can serve as a simple road map for enterprises to maximize the strategic value of IoT analytics.

Vendors in the IoT ecosystem have a largely unexplored opportunity in developing compelling solutions that explore how the ability to collect and analyze disparate data—in real time and across time—might transform the business. These developments will play out within and across enterprises, offering opportunities for sustained value creation, and even disruption for those who can imagine possibilities beyond the incremental.

• Deploy early – the value of IoT increases as more devices are connected, but launching such an effort requires long lead times. Don’t wait until your competitors have adopted IoT solutions, start early.
• Architecture first – build your IoT architecture before jumping into analytics. While complex, doing it ahead of time will solve a lot of future headaches.
• Emphasize growth – expand IoT’s potential by working with representatives from across the organization to find business functions that can be leveraged.
• Get close to customers – IoT helps reduce gaps between companies and customers. Proximity detectors, for example, interact with mobile devices to offer suggestions about products.
• Identify pain points – determine which top-priority problems you want to tackle and define the metrics that will measure the impact of your IoT strategy. It’s never too early to plan for useful IoT analytics.
• Align IoT with analytics – with this approach, you’re connecting machines to learn more about current consumer behavior, while using available tech to predict future behavior.
• Step up security – develop a security game plan that’s preventative and responsive to the complexities of threats in an era in which traditional perimeters no longer exist.
• Ensure transparency – to gain trust, make it clear to users which data is being collected and how it’s being shared.
• Invest wisely – be sure to perform due diligence to invest in “solutions” and not just “things,” likewise, invest in the things that really matter. Just because a thing can connect to the IoT doesn’t necessarily mean that is should be connected. Weigh the value of things you invest in – what technologies does it involve, what’s its expected life span, who makes it, etc.

Summary

Digital intelligence is the convergence of analytics with real-time data and processes to drive competitive advantage. Today, sensors are monitoring machines on the factory floor, assets in the field, and data center systems, tracking ships and inventory, helping businesses gain productivity and become ever more efficient. The Internet of Things is fundamentally shifting industry dynamics. Connected things and digital networks are enabling big data analytics to push data-driven decision making to new frontiers. Real-time and in-context analytics is how these new digital organizations can deliver game changing advantage.

This technology guide makes the case for adopting leading-edge IoT analytics solutions to gain strategic advantage – there is clear competitive risk of not deploying IoT when your competitor is in the process of doing so. If the path is clear that your enterprise is headed toward IoT, then waiting until your competitor actually does it is not going to work.

Even if your company believes it does not have any use cases that involve IoT, this notion may be proven wrong when the discussion turns to its physical buildings and facilities where it perform business. In fact, building and facility management is a huge use case for IoT as it involves the remote and autonomous monitoring and management of physical facilities, e.g. autonomously monitoring smoke alarms, occupancy sensors, electronic doors – including fire doors, temperature, etc. Many companies don’t realize that IoT actually can impact way they do business, where they do business, how they obey laws, regulations, and requirements (such as fire marshal limitations on the number of people in any given area), how they improve personnel security & safety, and how they improve their overall working environments.

In summary, this series provided a road map for climbing on the IoT analytics bandwagon including a general background in order to quickly get up to speed with IoT technology. Then we presented important value drivers for IoT and how an enterprise can see significant ROI. Next up, we identified a number of real challenges that enterprises may encounter when deploying IoT solutions. Finally, we laid out a series of IoT analytics best practices.

Contributed by Daniel D. Gutierrez, Managing Editor of insideBIGDATA. In addition to being a tech journalist, Daniel also is a practicing data scientist, author, educator and sits on a number of advisory boards for various start-up companies.

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