6 IoT Challenges That Keep IoT Engineers and Data Scientists up at Night
And, just like other tech revolutions to come before the IoT, the fast-paced growth and often hype of a new technology being followed by various struggles and IoT is no exception. In this IoT application development case review, we’ll explore some challenges that keep IoT engineers, data scientists, and businesses (OEMs) awake at night as they embark on product development and the digitalization of work and services.
Pressure on IoT Decision Makers
A significant number of companies believe that IoT solutions will have a high level of impact on their businesses in the long run. Thus, having the right people available to make data-driven decisions is becoming paramount. And, given the expertise needed to set up, manage, and extract value from vast amounts of data collected in the field requires expertise from data scientists or trained engineers who can be responsible for critical IoT decision-making processes, plans, execution, and achieving the established goals or PoCs.
So, what are the biggest challenges delaying or stopping data scientists, engineers, and businesses from adopting and/or developing IoT Applications?
- Predictive Analytics
Thanks to the IoT, predictive analytics has become a value-adding capability for companies. The combination of the industrial Internet of Things and predictive analytics can be revolutionary in the way we understand the optimization of processes right now. It can also bring significant advances in efficiency and cost savings.
However, to achieve this, it’s necessary to fully grasp how predictive analytics works and how to apply it in the exact situation. Thus, careful preparation is a must; requiring a clear objective followed by thorough research and planning. And, this might become a challenge due to the lack of experts capable of taking full advantage of predictive analytics as the required hardware must be deployed or manipulated to record data over time. Then the appropriate software of Machine Learning and AI must be applied to train the system to recognize incidence to be able to model the probability of failure to realize value from the raw data collected.
- Poor Data Quality
Precise data preparation is the key to obtaining high-quality, efficient data. However, when data scientists start analyzing the information, they usually have to go through loads of messy data, sensor errors, or missed readings, no matter the size or type of the company.
The application of data integration tools is crucial in data management. These tools help to automate information entry and avoid errors that may arise by entering it manually. For example, spelling or typographical mistakes. For this reason, IoT platforms, like Ubidots, provide essential app technological components so engineer and data scientist do not need to reinvent the wheel when it comes to sensor data collection and data management. Using production tested integrations and an IoT optimized time-series data storage server users can effortlessly send data to the platform where it can be organized with time reality and apply analytics as needed.
It’s crucial to communicate to the stakeholders how vital the appropriate preparation is. The entire preparation process may take a lot of the engineer and data scientists’ time and effort.
It seems that the rapid growth of the IoT is not going to slow down in the coming years, meaning security options might end up being outpaced. Thus, the implementation of IoT solutions in business is both exciting and slightly dangerous with the ever-present existence of hacking or hijacking of hardware or software systems.
IoT network security is more challenging than traditional network security due to its more extensive range of device capabilities, communication protocols, and standards. Therefore, to ensure it is an enormously difficult task.
However, such measures as simple as using a reliable VPN can help to overcome security threats. One of the most effective ways to use a VPN is by installing it on your router so that all the devices connected to it are protected by an additional level of security beyond simple encryption.
- The Data-Scope Is Too Big
There’s one paradoxical challenge – big data may be too large to analyze and even harmful in pursuing the set goals. How come? In predictive analytics, it’s necessary to understand which information is related to your objective and which isn’t. With too much information, data scientists and engineers may end up trapped or drowning in data. A combination of high variance fields and inability to generalize well can set them back from developing high-quality predictive models. It may lead to even worse consequences, for example, the misinterpretation of data and decision-making based on false interpretations, anomalies, or errors.
- Data Accessibility
The integrity of data is a challenge to data scientists and App engineers. Who gets access to the data? Who owns it? How will they access the data? These questions are a real headache for specialists tasked with development. The frequency of data sharing needs to be strictly managed as the nature of data varies creating the never-ending challenge of user-management security.
- IoT Skills Gap
TEKsystems survey revealed that 45% of businesses struggle to find IoT and its security professionals. Immarsat interviewed 500 senior IT professionals from major companies and found out that 46% of respondents lacked experience in analytics and data science. This knowledge gap creates barriers for businesses willing to incorporate the IoT and AI into their decision-making process.
As the IoT is still in an early stage of development and adoption, it will take some time to find a way to overcome the IoT challenges keeping engineers and data scientists up at night. But, with the assistance of IoT Application Development Platforms, IoT Enablement and Digitization can be a little less scary to the engineers, data scientists, and businesses looking to and beginning IoT Solution development and adoption.