Seven steps to enterprise IoT success

As 2019 kicks into gear, one of the most exciting developments in enterprise technology will be the growth of the Internet Of Things (IoT). With an estimated 50 billion of connected devices on the internet by 2020, the future of this technology is exciting.

According to Boston Consulting Group (BGC), B2B spending on IoT technologies, apps and solutions will reach US$267 billion by 2020.

At least 50 percent of that spending will be driven by discrete manufacturing, transportation and logistics, and utilities.

IoT is not a single technology but a collection of technologies, processes and devices that promise to drive major changes in most organizations. IoT is primarily about using data insights and automation to drive business decisions

As Sean Bryson of Cisco explains, IoT systems consist of seven essential stages and technologies. In order to manage successful IoT projects, businesses need an understanding of the following components.

# 1 | Devices, sensors and device management

One must be aware of physical sensors and devices that are placed in equipment as well as centralized tools that maintain those sensors and devices. This includes the ability to update firmware, apply security settings and more.

#2 | Gateway and edge services

Sensors and devices must establish some sort of communication and initial decisions may be done by edge computing tools— a network architecture that enables data processing much closer to the devices and users engaging in these activities.

This is to support real-time business technologies and manage data processing. The data is then aggregated and sent into an IoT system.

#3 | Cold-path analysis

To obtain insights, machine learning (ML) tools will analyze a collection of data that has been stored for longer periods of time, or data of sufficient volume to identify potential patterns. This is known as cold-path data.

Hot-path data involves known patterns, where the appropriate ML and real-time ingestion processes can be applied. When the patterns have been identified, the ML algorithms can be published into edge devices and hot-path analytics to support real-time business decisions.

#4 | Hot-path analytics

This allows organizations to apply real-time business algorithms on the sensor data, enabling immediate responses. Complex event processing is often used to perform this analysis.

#5 | Data storage and advanced analytics

As data is kept for longer periods of time, additional analysis and insights can be developed. Data can be sent back into cold-path analytics for additional insights or be used for reporting.

#6 | Line-of-business system integration

IoT is mostly connected to line-of-business systems, enabling data insights to directly feed business applications and management systems.

#7 | Reporting and dashboards

IoT data will be supplied to reporting and dashboard tools to provide visual indicators of system health, actions happening or impacts on business.

Bryson explains that the most common IoT scenario is that of predictive maintenance, the ability to prevent an equipment failure through proactive management.

The most important starting point in an IoT project is the cold-path analytics stage. As the data is loaded into an ML solution, data scientists carefully use the data to identify data patterns.

Testing is an evolutionary process

The process is usually iterative, where an algorithm is developed and tested against the data. Testing can identify further changes to detect data anomalies.

Once data patterns are understood and algorithms created, the ML models can be connected to the hot-path analytics/complex event processing systems and edge computing systems. Real-time data insights and alerts will follow.

Ultimately, successful IoT projects start with an end goal. Organizations need to start with data and sensors available as initial starting points and then add additional sensors and devices to expand the data.

Never stop asking questions

Common questions in the planning and initial pilot phases include:

• What is the business goal of an IoT system and which metrics will identify success?
• Does the data exist to support the business goal?
• Are the data patterns already known and understood or is further analysis required?
• Is there a small or easy proof of concept that can demonstrate business value?

As long as IT practitioners are looking at a specific business problem to be solved, business goals can be broken into smaller pieces to demonstrate the value of the technology.

If these smaller goals can be tested, the business case for continued investment can then be tested. So, when the initial business case is proven, additional sensors and ‘things’ can be added to expand insights.

At the end of the day, IoT projects need to be focused on the business and drive innovation. Reducing costs, improving safety and driving profitability are the real benefits that will keep businesses ahead of the curve.