Over the past few years of being immersed in the Internet of Things (IoT), I have found that customers have very specific problems they are trying to solve; e.g. gaining energy efficiency, early fault detection or remote diagnosis and maintenance of equipment. Decisions are driven by the need to reduce Operational Expenditure (OPEX) and save on Capital Expenditure (CAPEX).
With all the data generated from IoT devices, having strong analytics and visualization capabilities can help in making accurate decisions and taking timely action thus achieving these critical business objectives. Of course, while this sounds appealing, it’s not quite that simple. In order to achieve meaningful value through reductions in OPEX and/or CAPEX, we need to effectively address data collection, analytics, visualization and control. Absent those essential elements, we are not able to harness the power of the IoT.
What follows is an overview of what these critical elements entail and steps to implementing a successful IoT solution the leverages them fully.
The IoT Data Journey –
From Data Collection & Analytics to Visualization & Control
Data is fluid and tends to be misunderstood in its raw form. The real challenge of the IoT is that you have too many faucets running at the same time with different kinds of fluid. At the collection point, dealing with data complexity and variation is extremely critical. Without addressing that complexity early, it’s impossible to achieve the end business result you’re after.
Let’s consider, for example, a typical commercial building and the data journey in that environment. You are likely to come across different sub-systems from different manufacturers; e.g. HVAC, elevators, security, power. The first step is to try and normalize data from all these sub-systems through a common data model and then focus on the data that is relevant to the problem you are trying to solve.
In effective IoT platforms, after normalization, the data is fed into an analytics engine that adds intelligence to how data should be interpreted. The analytical engine is built out of rules based on specific domain expertise and feeds into a dashboard that visualizes the information necessary to take action. However, visualization in absence of action is not of much help. Therefore, remediation is an important piece of the overall solution.
Typically, in IoT use cases, alarms would indicate that an action needs to be taken. But somebody needs to press a button somewhere to make that action happen. The best IoT platforms are designed to close that loop. They allow you not only to take manual actions but help to automatically (or semi-automatically) remediate from when an alarm is generated in as close to real-time as possible.
Barriers to Wide Adoption of IoT Analytics & Visualization
Although the value of analytics/visualization is huge in IoT, there are several barriers that you need to understand and overcome while developing your solution.
Data acquisition is expensive
There is a huge amount of data that can be collected and a lot of it is irrelevant. There are a large number of disparate and proprietary devices in a building. Getting data out of these systems is cumbersome and sometimes will require several different tools. This can become expensive. Even if you are able to collect data, some industries are struggling with how to name and identify data in a common way so analytics applications can consume this data easily.
Domain expertise
To get the most from IoT, organizations must have team members with domain expertise that are dedicated to solving problems and delivering on specific IoT goals. ‘Energy Officer’ is a relatively new title in many companies, but having such a person ensures someone is focused on driving energy savings with your IoT solution.
Return On Investment (ROI) is not always instant
ROI, while real, is slow to materialize. I’ve seen this again and again when working with IoT customers. In buildings, some customers have only seen significant benefit when their IoT solution has extended to multiple sites. ROI depends on your business. And it is something you should be prepared to be patient about.
Too much going on in the IoT market
As IoT is gaining momentum there are startups and established companies entering the IoT arena with new platform, analytics and visualization technologies. While having more options for products and services can be good, it can also be confusing and can make it very difficult to select the right technology needed to build a strong IoT analytics and visualization solution.
Selecting and Developing a Robust IoT Analytics & Visualization Solution
Below are a few tips to consider as you design your IoT solution. There are probably several other considerations but for this post, I will outline those that I have seen implemented over the past few years:
Identify the Problem and Set Your Goals
It is extremely important to understand and identify what you really want to solve with your IoT solution; e.g. where and how much to target to save in your operations annually. This goal is unique to your business and a very critical start. This also means you will need to have domain expertise to help with the problem.
Ensure Smart Data Collection
This is a hard one and takes multiple iterations before getting it right. Try to identify the data you need and ensure that there is accuracy in the data collected. Additionally, the data needs to be reliable and high performing. Most of the time, data will need to be collected from multiple systems that are already installed.
Select the Right IoT Platform
If you know your goals and have an idea of what data you need, selecting the right foundational technology for data collection and management is very important. There are some key tenets in an IoT platform that you should be looking for:
Open Technology – so you can normalize data from legacy proprietary and new edge devices, build applications and integrate with 3rd party systems as and when you need without having to replace the platform or infrastructure. APIs play a critical role here – look for published open APIs for your developers.
Stable Technology – if you have the choice, besides evaluating pros and cons of existing vs new platforms in your labs, evaluate established “real” IoT operational case studies. See how long these systems have been running and how customers have benefited over multiple years. IoT systems should be designed for prolonged and sustained benefits.
Robust Eco-System – you might want to conquer the world by building all applications you need yourself but with Android and iOS, we all know the power of an application ecosystem. You want to be able to have choice. Select a platform that has a developer community around the technology.
Scalable – Although scalability depends on your business needs, I recommend selecting a platform that can scale from the edge to the cloud. Learning, managing and developing applications on multiple platforms is hard and cost-prohibitive. If your business serves a large and complex IoT infrastructure, you should plan for the millions of devices that are going to get connected to the web over the next several years.
Prepare for Real-time and Historical Analytics
Depending on your business, you might need real time data for mission critical decisions OR just historical data for you to run periodic reports. Traditional methods of analytics are not suitable for harnessing the IoT’s enormous power. Using real-time analytics at the edge (device level) in conjunction with historical trends analysis is very important. In a recent video below, I talked about what makes data explosion such a great opportunity for IoT.
Actionable Visualization
Flexibility and integration with analytics is very important in an IoT data visualization solution. There are choices that range from well established/legacy enterprise class Business Intelligence (BI) visualization tools that are able to deal with complex data and new cloud based tools for complex and simple visualization of unstructured data. I like visualization capabilities that are self-servicing so I don’t have to wait forever for someone to create a report. Also consider what your mobile users will need – simplicity is a big driver there. Visualization is all about how data is presented in a manner such that appropriate action can be taken in time.
Once selected, installed and operational, you will need to continuously evaluate your analytics & visualization solution and make changes as required.
Conclusion
Do what’s best for you. There is no set formula and every business is different. Identify the specific problem that you want to solve and build your solution around it.
Next Generation of Systems Design at Siemens