4 Mistakes To Avoid While Handling IoT Big Data

IOT Big Data
12Mar, 2019

The ubiquity of IoT and the power of Big Data is highly exploited to uncover a new way of finding customers.

Is “data” itself the secret to IoT? Partially True.

The combination is like an umbrella that covers applications with the realm of business and personal domain. With so many millions of devices using IoT, you could dive into gigabytes or even petabytes of data and derive meaningful interpretations.

The businesses now use the combination of IoT modules, and other technologies to interact with the world with the help of .Net Development Company.

Cisco forecasts that the number of devices using IoT can grow up to 50 billion by 2020 which further can be inferred as 6 IoT devices per each person on Earth. The IoT seems to gain momentum finally with dynamic players, automobiles and even real estate sectors coming on terms with it.

bigdatadiagram           Image: https://siatik.com/wp-content/uploads/2017/10/bigdatadiagram-1.jpg

Every event responses and machine behavior is now contributing to generating data be it calories burnt, room temperature we prefer, medicines usage, car usage and more.

If the unstructured and structured data is not handled properly, it may create ripples. The common roadblocks in gaining actionable insights from IoT-Big data are :

1. Collection of Excessive Data:

Mistake:

IoT data has helped in generating a myriad volume of data and is exponentially growing for IoT Solution. It is expected that by 2020 there would be around 6.6 stacks of fully loaded 128 GB iPads.

To be honest, companies extracting more data is better is just a hype. Unless the data is being handled properly, it is only going to swell with terabytes.

Action:

Plan a clear roadmap strategy of handling IoT big data. Make sure you have an effective way of working with Big Data and it should only focus on resources generating only relevant data. Instead of constantly sending data to the cloud in the cloud web application, try adopting Edge computing. It firsts sorts out the potentially valuable data and pushes it upstream for analysis.

2. Taking Unstructured Data for Granted:

Mistake:

99% of the IoT data is unstructured. Interpreting and making sense from this is a difficult task because of the traditional tools. To pull the curtain down, from 99% of the Data, imagine only 1 % brings value to the business.

Action:

The combination of machine learning algorithms and cognitive computing makes a perfect solution for data handling. Traditional tools were unable to normalize, process, aggregate, and transform the unstructured data. However, Data mining, Pattern recognition, natural language processing techs help to bring in sync the data.

Knowing that Data is an important asset, there are a number of easy-to-integrate powerful open source frameworks that possess data processing and analytics capabilities.

3. Uploading all IoT data to Cloud:

Mistake:

IoT is changing how we work, live and run the societies. The huge pool of data generated streams directly into the cloud for normalization, aggregation, and analysis. We mostly neglect or don’t give a second thought on the risks of breaching.

Our database has a lot of client’s personal and sensitive information, and we shouldn’t expose them constantly. As the cloud uploads depend on the quality of internet connections, a poor connection may give you a drawback.

Action:

You can decentralize IoT data by implementing edge analytics, god computing, and cloudlets. This helps in building a more distributed and effective computation ecosystem. The data generated is first normalized and pre-processed on-device and then the filtered data is sent to cloud platforms for further processing.

This approach solves both the concerns regarding data security and also internet connection dependency.

Thus, it can not affect the productivity of real-time analytics as the process doesn’t depend on the internet connection. In addition, the data will be pre-processed, anonymized for full-swing analysis and decreases the risk of breaching.

There are a number of tools to integrate with your endpoint and make work faster but they can not address all project challenges. You can explore the full potential of the edge computing if you have enough expertise or taking help from Dot Net Development company.

These products should be smoothly integrated into your device without compromising on the existing effectiveness.

4. Not analyzing IoT data Proactively:

Mistake:

A slow data processing approach may lose out on the ability to generate immediate data-driven insights. If you wait for more data processing, the less value you extract out of it. There is a need for continuous integration which helps to streamline the development, detection of problems reducing the chances of falling back and giving an effective way to get data insights.

Action:

SAP Hanna, Spark, Elastic stack or Apache services help in synchronizing the data resources. The real-time processing vitalizes and helps to take proactive business decisions.

The IoT data can only succeed if the points of its failure are addressed. Only focus on making the data highly secure while ensuring low-latency and real-time decision-making capability. The approach should help you address the business goals effectively.

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