AI and the IoT have snowballed in attention over the past little while – garnering traction as innovative drivers for automation and revenue. However, many businesses remain hesitant to adopt AI-driven strategies given the many myths that still permeate the industry.
When it comes to AI and IoT analytics, a sleuth of concepts may come to mind, from device connectivity to data ownership – but do you know which truth can be derived from their basis? Every myth is built upon a foundational notion, but the accuracy of that foundation may be flawed. In this post, we’ll examine some popular IoT myths, and differentiate between facts and misconceptions.
Let’s debunk five of the most prevalent myths about IoT analytics.
Myth 1: The IoT is just about connecting things
The IoT describes a network of connected things, where smart devices are paired with capabilities that allow them to communicate and interact with each other – but it does not limit itself to that. Sending and receiving data are the IoT building blocks for increasing revenue, reducing costs, and improving aftermarket services. However, to be able to do so efficiently, simply exchanging data isn’t enough.
Traditionally, manufacturers could only track pre-production – manufacturing, distribution, shipping, and purchasing. This left them entirely reliant on manually collected customer feedback for views on the rest of the product lifecycle. System errors, failures, or product issues occurring post-deployment would only be made known after the fact – which does not make for the most efficient feedback loop. This lack of clarity can result in issue resolution delays, unnecessary truck rolls, avoidable system failures, loss of business, and ultimately customer churn.
It is only with advanced analytics for IoT technologies that companies have been able to achieve better visibility over post-production. AI and IoT-enabled solutions do not stop at connecting smart objects – they’re much more elaborate than that. By aggregating and analyzing sensor data from their connected products, stakeholders can better understand product usage and performance throughout the entire product lifecycle management (PLM) process. From pre-production all the way to live deployment, connected objects will arm organizations with more in-depth understanding of their entire operations. Collected data points will further help companies guide autonomous decision-making and task performance based on pre-established conditions, leading to better products down the line.
Data from IoT analytics also enables actionable lifecycle insights throughout the company – from Product Management to Sales and Marketing to C-level. End-to-end visibility provides consistency and alignment as the organization builds a better understanding of the customer’s engagement and interactions, optimizes their aftermarket service programs, and focuses their future product roadmap. The IoT is hence not about connecting things, it is all about analyzing data from these connected things.
Myth 2: Analytics platforms are all the same
Data-driven decision-making is a crucial factor when determining the competitiveness and relevance of today’s companies. From web analytics to mobile applications and social engines, analytics are an all-encompassing component of most corporate product and business strategy. With a common goal of providing actionable insights to the end user, it becomes easy to group analytics solutions under one big umbrella.
However, not all analytic tools are built the same, nor do they have the same functions. A breakdown of each branch, including IoT analytics, will provide a clear idea of how they are being used, what they track, and which KPI they analyze – making sure you use the right tool for your business needs.
Myth 3: It’s quick and easy to build an IoT analytics solution
A common misconception, or rather wishful thinking, is that building an IoT analytics solution is relatively easy. Therefore, a lot of companies will look into in-house resources in order to jumpstart their own data solution. However, that is not the most ideal path. Object manufacturers and service providers tend to lack experience with real IoT projects, despite having most necessary software and hardware skills. IoT projects require specs that are vastly different than most traditional software projects.
Underestimating the expertise needed as well as the required time to properly deploy AI technologies can be detrimental to the company – often causing more setbacks than progress. With limited exposure to technology unique to the IoT, companies will come to realize that they require specific skill sets in order to design, develop, and deploy IoT and sensor data. Some essential roles include the following key players:
- Developers (e.g. Java, Scala, Python, NodeJS)
- Big Data/NoSQL database specialists (e.g. Elastic, Cassandra, Hadoop)
- Data processing and messaging specialists (e.g. Spark, Kafka, ZooKepper)
- DevOps (e.g. Ansible, Docker, Mesos)
- Data Scientists (machine learning, predictive analytics, data mining, statistics)
When you consider the additional recruitment time to find these in-demand specialists to build software in-house (it may take up to 18 months), an estimated time-to-insight would be between 1.5. to 2 years – and that’s being optimistic! It becomes apparent that outsourcing to ready-to-use IoT data analytics is the optimal solution for data science capabilities. This option allows connected object manufacturers to operate as quickly as possible, and maintain a healthy feedback loop between their data and relevant insights.
Myth 4: The IT department owns the IoT strategy
Being data-reliant and technology-driven, the IoT strategy must solely belong under the IT department, right? Not quite.
For IoT projects to be successful, different departments within a company need to work together to adopt and embrace a data-enabled mindset. Disseminating the same data points across the organization ensures that there is a more holistic approach to decision-making.
Having each team involved in the implementation of IoT projects will break-up silos, and empower cross-departmental AI collaboration within a large enterprise. Only then would an IoT strategy be truly impactful as consistent information flows between teams. In turn, this feedback loop helps create a 360 degree view on a company’s products and services.
Myth 5: All data is good data
All news is good news, is how the saying goes. But be wary of applying the same logic to data collection. While Big Data is all about more data, analytics is about offering data that leads to actionable business insights.
Given this difference, it becomes vital to select the right plan or strategy for data compilation. Incomplete or out-of-context data can drive increases in costs and complexities. More importantly, this can produce flawed insights, and lead to decisions that may be more harmful than beneficial.
You could only do that by differentiating between various types of data – and zoning in on the information that is relevant to the problem you are trying to solve. So, before diving into building an IoT data strategy, product manufacturers must answer these questions to determine which information they need to gather:
- What endpoints will provide the data?
- What data points should be collected?
- Which analyses will generate strategic insights?
- What additional services do I need to offer?
AI and IoT analytics are two increasingly popular terms, but grasping what each concept entails is still murky territory. Concerns around the industry are not completely unfounded, but it’s important to decipher between facts and misconceptions and bridge the knowledge gap.
IoT is much more than just connecting things – it helps gather data, not just to build a bank of information, but to shed light on product lifecycles and draw conclusive insights across the organization. IoT analytics platforms differ from other tools, and outsourcing your analytics solution could be the most efficient option for your company. Learning more about IoT analytics can help companies embrace a business model that shows a lot of potential for future adoptions, despite still being in infancy stage.
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