Avoid These 4 Mistakes While Handling IoT Bigdata

The IoT and Bigdata combination is revolutionizing the industry. Once the Internet of Things was considered a nerdy concept and is now ruling the Digital World. The number of devices using IoT may grow up to 50 billion by the end of the year 2020 which means 6 IoT devices per person on Earth.

With the Digital age gaining momentum and businesses in automobiles, computers, and real estate sectors steadily coming on terms with IoT, there is a pool of data generation. Business processes are made efficient through connected devices.

Unless you handle the pitfalls effectively while handling IoT data, you can not succeed without help from .Net Development Company. The common mistakes which can not be overlooked when developing a big data strategy are:

Collect the relevant Data:

With the exponential growth of IoT, as much as 6.6 stacks of fully loaded 128 GB iPads will be generated. If the data extraction is not handled properly, it may lead to memory size swelling to petabytes.

Precaution:

Design a roadmap to improve the effectiveness of data insights. Make use of edge computing tool for intelligent data pre-processing. It helps in filtering out potential valuable data without affecting the solution’s costs.

Not concentrating on Unstructured Data:

Interpretation and processing of IoT data with the traditional analytical tool are really difficult.

Precaution:

For effective handling of Big Data, implement machine learning algorithms and cognitive computing. Adopt tools like Data mining, pattern recognition, natural language processing, computer vision and more to get actionable insights and saves time.

Streaming all IoT data to Cloud:

 Uploading all the client’s data that is sensitive and valuable to the cloud is like exposing the data to the risks of breaching. Also, the normalization, aggregation and analyzing the data on the cloud requires a consistent internet connection.

Precaution:

Try to decentralize and implement edge analytics. By doing this, the raw data is first normalized and preprocessed and then sent to cloud platforms for final analysis and insight extractions. By doing this, you secure the data and get independent from Internet connection quality.

Retrospective analysis of IoT Big data:

You can not act slowly while dealing with real-time data insights. The more you wait, the less you extract. The retrospective analysis provides an effective way to get insights from the heap of data.

Precaution:

To make the business decision making proactive and gain a huge competitive edge, you need to readjust the retrospective data processing. It may require powerful ETL, real-time analytics framework and huge computational resources.

Elastic Stack,     SAP Hana, Apache Services and others can be useful tools for proactive analysis.

Clear out IoT Big Data Strategy:

Select carefully which data is valuable to you. Define a roadmap of strategies to address the business goals. Take advantage of the top-notch dot Net Development company to customize those solution based on your business needs. The process to extract meaningful data insights at a single place is really difficult and requires proper planning.