Challenges with Big Data Analytics in IoT

Ritesh Sutaria
IoT Data Analytics
Illustration: © IoT For All

A thriving IoT environment demands standardization that consists of interoperability, adaptability, dependability, and effectiveness of the operations globally. Speedy development in IoT increases data growth. Vast amounts of networking sensors are ceaselessly collecting and transferring data (environmental data, geographical data, astronomical data, logistic data, etc.) for storage and processing purpose in the cloud.

The primary equipment acquiring data in IoT are mobile devices, transportation facilities, public facilities, and home appliances. The flow of an enormous amount of data surpasses the capacities of IT architectures and infrastructure of existing enterprises. Due to the real-time analysis character, it significantly affects computing capacity.

The generation of Big Data by IoT has ruptured the existing data processing capacity of IoT and recommends to adopt the data analytics to strengthen solutions. The success of IoT depends upon the influential association of big data analytics.

Big data is widely used for a broad set of heterogeneous data present in the structured, unstructured, and semi-structured forms. As per Statista, big data revenue generates from service spending, representing 39 percent of the overall market as of 2019. The data volume created by IoT connections reached 13.6 zettabytes in 2019, and it would extend to 79 zettabytes by 2025.

Big Data and IoT

Big data and IoT are two unique concepts dependent on each other for constituting ultimate success. Both aim to convert data into actionable insights.

For instance, shipping companies attach IoT sensors to vehicles to monitor speed, upcoming stops, engine status, ambiance, and other related issues in the shipping industry. Companies use the data collected to make prompt decisions and predict future maintenance requirements. Companies also store collected data to get a clear view of the company’s performance over time. The combination of real-time IoT insights and long-term big data analytics helps save extra expenditure, enhances efficiency and effective use, and manages available resources.

Using Big Data

Big data aids IoT with easy functioning but poses its own challenges. Data is generated by connected devices and helps make better and profit-oriented decisions when utilizing IoT and analytics.

Data processing includes the following steps:

  1. IoT connected devices generate a massive amount of heterogeneous data that is stored in a big data system on a large scale. The data is dependent on the ‘Four “V”s of Big Data’: Volume, Variety, Veracity and Velocity.
  2. A big data system is a shared and distributed system, which implies that a large number of data records in big data files present in the storage system.
  3. Use an advanced analytic tool to analyze the data collected.
  4. Examine and produce a conclusion of the analyzed data for reliable and timely decision-making.

Challenges with Big Data Analytics

The key challenges associated with Big Data and IoT include the following:

Data Storage and Management

The data generated from connected devices is increasing at a high rate, but most big data systems’ storage capacity is confined. It becomes a significant challenge to store and manages a large amount of data. Hence, it has become imperative to build frameworks or mechanisms that can collect, save, and handle data.

Data Visualization

The data generated from connected devices are heterogeneous data or structured, unstructured, and semi-structured in varying formats. It isn’t easy to visualize the data immediately. Hence, there is a need to prepare data for better visualization and understanding to obtain accurate decision-making in real-time while improving the industry’s efficiency.

Confidentiality and Privacy

Every IoT-enabled devices generating enormous data requires full data privacy and protection. The data collected and stored should stay confidential and have complete privacy as it contains users’ personal information.

Integrity

Smart devices are experts in sensing, communicating, information sharing, and carrying analysis for various applications. The device assures users of no data leakage and piracy. Data assembly methods must use some scale and condition of integrity strongly with standard systems and commands.

Power Captivity

Internet-enabled devices require an unending power supply for the continuous and stable functioning of IoT operations. Connected devices are lacking in terms of memory, processing power, and energy –– so they must have light-weighted mechanisms.

Device Security

Analytics face device security challenges as big data is vulnerable to attacks. Data processing faces challenges due to short computational, networking, and storage at the IoT device-end.

Various Big Data tools provide valuable and real-time data to globally connected devices. Big data and IoT analyze data accurately and efficiently through suitable techniques and mechanisms. Data analytics may vary with the types of data drawn from heterogeneous sources.

Author
Ritesh Sutaria
Ritesh Sutaria - Director, Prompt Softech
Over 18 years of experience in Product Engineering and Leading an Organization. Demonstrable organizational skills in putting together customer-focused teams of high calibre achievers who have delivered remarkable performances.
Over 18 years of experience in Product Engineering and Leading an Organization. Demonstrable organizational skills in putting together customer-focused teams of high calibre achievers who have delivered remarkable performances.