Common Mistakes Made While Handling IoT Big Data And How To Avoid
IoT or the Internet Of Things is a term that was first coined by Kevin Ashton in 1999. IoT has turned into the most popular buzzwords. The Internet Of Things is like the thermostat that starts figuring out your routine in and out everyday and acts suitably. The IoT learns and tailors its intelligence according to what we require. In today’s digitally IoT-powered era, all equipment and appliances we clap our eyes on are IoT leveraged.
The IoT Analytics Research of 2018 statistics depict the number of connected devices to be 17.5 billion. The Cisco forecasts this amount to rise up to 50 billion devices by the end of 2020.
These statistics are a precursor of the digital trend-altering development as based on these figures, these devices generate terabytes of crucial data. But according to the Harvard Business Review, 99 percent of data (unstructured data) goes lost and is not accounted for.
To ensure the full value from captured data is made use of, it depends on the analytics platform used. There are three key factors to be considered while implementing Iot Big Data and while the data is extracted for businesses. They are as follows:
- Infrastructure Size.
- Company’s Forecast For Scalability.
- Company’s Performance.
On the basis of this, companies can decide on whether they require a single tenant physical server or hybrid cloud solutions. The former drives the company’s performance and the latter ensures the size of the infrastructure and also the company’s growth.
Once this has been decided on, a business can now implement IoT Big Data but understanding what to do and what not to is also equally important. Let us now have a walk through the major mistakes caused while handling IoT Big Data and how to avoid them:
- Excessive Data Collection Issue : Resolved By Edge Computing
The IoT generates massive amounts of data. “By 2020, more than 35% of all data could be considered useful data, thanks to the growth of data from the Internet of Things, but it will be up to businesses to put this data to use.”, according to EMC Research.
“Today, if a byte of data were a gallon of water, in only 10 seconds there would be enough data to fill an average house. In 2020, it will only take 2 seconds.” This is how EMC research study depicted the amount of generated data.
There is always a misconception that the more data extracted, the better it is for your business. So companies try extracting data (both structured and unstructured) with their numerous analytical tools but is still of no potential value for their business.
For this, the prime requisite is to have IoT big data strategy that helps you in choosing the best IoT sources for your business. This helps you in focussing only on relevant data. Another scenario is when you have a functional system in hand. This is where Edge Computing can help you build a bigger picture.
Edge Computing relieves you of the need for constantly transmitting data to the cloud and is an intelligent way of data pre-processing. It first identifies and extracts the best out of the data generated and only then pushes it upstream for further analysis.
This not only helps increasing the insights but also is a cost-effective solution.
- Trouble In Handling Unstructured Data : Resolved By Machine Learning Algorithms, Cognitive Computing
When there are torrents of data captured everyday, how will you be able to identify which data is important for a company’s success? The Cross-industry studies depict that it is less than 1 percent of data that is analyzed or used by them.
With so many data management functions and CDOs (Chief Data Officers) available, 70 percent of users have access to data that is of no primary use and most of the analysts spend 80 percent of their valuable time searching for data.
Unstructured IoT Big Data can effectively be handled by Machine learning algorithms in addition to technologies such as Cognitive computing, pattern recognition, computer vision and natural language processing. These technologies tend to normalize unstructured data, assemble these and remodel the data into actionable insights, either automatically or the other way round.
There are numerous frameworks and IoT big data platforms that help in data processing and deal with analyzing data into insights. These comprehensive systems that you should look forward to should be able to handle real-time and also in making them available through intuitive and informative dashboards as well.
- Cloud Streaming All The IoT Data : Resolved By Decentralizing IoT Data Analytics (Edge Analytics, Cloudlets, Fog Computing)
Users are at times doubtful if their personal information such as GPS, financial information, etc are subjected to breach. When directing numerous data streams into the cloud via IoT devices, organizations ought to restrict data sharing to limited and trustworthy members to avoid fraudulent activities. Also, the quality of internet connection matters when your IoT data analytics has been delegated to the cloud for real-time data analysis.
The solution is the need of an ecosystem that is distributed so that only required data is sent to the cloud. To avoid the risk of breaches, you can make sure the majority of data does not leave your smart device. For this, the raw data needs to be first normalized. Once pre-processing the data followed by the privacy compliance procedures is completed, this data is then sent to the cloud and also improves data integrity.
Encouraging a frequent sync can avoid the need to have your IoT devices to be free from keeping a connection with the cloud platform every single time and thus gain effective real-time analysis.
Related Reading : Role of Data Analytics in Internet of Things (IoT)
- Slow Analysis Of IoT Big Data : Resolved By Processing The IoT Big Data Real-Time
Obtaining Insights from the collected data is the key to driving maximized value. Getting a grip over immediate data-driven insights and being able to automatically trigger actions based on it requires introspective and immediate batch processing. This not only maximizes the value of collected data but also to make proactive business decisions using real-time analytics frameworks, and also large computational resources.
In a nutshell, optimizing your smartphone’s infrastructure, or remodelling your existing IoT big data system holds the secret to avoiding pitfalls while handling it. Moreover, building an effective IoT system is not only about accumulating all the data you possibly can but also making full use of it to enhance business value!
Author Bio:
Vinod has conceptualized and delivered niche mobility products that cater to various domains including logistics, media & non-profits. He leads, mentors & coaches a team of Project Coordinators & Analysts at Fingent a custom software development company.
