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Top 5 IoT Data Management Challenges

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Data evolvement from micro to macro resulting from a variety of myriad devices can easily capture or create data. The procedure is really astonishing. Today there is more data than yesterday, there will be more tomorrow and much more data in the near future. Internet of Things is the major reason behind creating such massive volumes of data daily. This increase in data has once again prompted the industry to analyze their strategies of data management or data governance approach from different perspectives that include data gravity, scale, data security, integration, etc.

The techniques of data management that were used yesterday are no longer appropriate to deal with the large volume of data. Data nowadays have great diversity and interconnectivity that is characterized by the Internet of things. Therefore, scalable infrastructure and centralized management are required. IoT Data Management Challenges are faced by almost each and every enterprise who tries to implement IoT.

Challenges faced

Internet of Things is not able to aptly prepare businesses for the difficulties that may creep in while a business implements and deploy IoT solutions. A set of various ground-level challenges may be faced during implementation that includes data ingestion, data storage, data analytics, data processing, visualization, etc.

We have shared some of the challenges faced in the course of implementing or after implementing IoT. Keep reading to know in detail about some of the challenges that you may address along the way.

IoT Data Gravity: Value comes with Volume

When data volume grows, other applications or functions look for value in that data. Instead, the applications tend to increase the volume of data further. The data when combined with data from other downhole operations it increases in size and becomes more valuable. When the data is analyzed, operators predict and optimize the performance of drilling operations in the same locations.

Equipment manufacturers benefit from operational data, especially when failures are indicated. Therefore, we can conclude that larger volumes of data provide greater insights and benefits manufacturers, operators and maintenance personnel.

Scaling Infrastructure easily and globally

Volumes of data generated by IoT is the first step towards shocking IT organizations as they have to contend with this data. The increased volume of data has been a concern for many years now and the increase has now become manageable with the help of storage size and costs involved.

However, the sources, speed and location at which the data by IoT is generated require rethinking data cycle. The challenges for many enterprises is several numbers of cloud providers with whom businesses work to support the applications, operations and geographies in which they operate. Reduced costs lead to more complexity to manage a diverse infrastructure.

Data Security and Encryption Strategy

Though data in motion needs to be protected by the enterprises, they also need to take care of the data at rest. The threats have now expanded much more than just personal and financial data and enterprises now with significant operations carried physically such as city infrastructure, manufacturing, transportation, chemical and petroleum, etc. are also concerned with the security and intellectual data.

As far as IoT and widely distributed operations are concerned across the various cloud environments, the encryption key management is necessary with the help of the centralised approach. Encryption key management should be delivered efficiently as a service. This will provide an extra level of security.

Secure integration is required by IoT

Much value of IoT comes from interconnectivity and it can easily transfer bits into values. Great value is acquired by such data when it is shared with a legitimate party. A manufacturer of drilling equipment is benefitted significantly by analyzing the operational data that is being shared by its customers.

However, integrations that are secured among various connections and components comprising the IoT environment is a challenge. Real-time processing is the base requirement while generating operational data and it should be analyzed at the factory floor. You also need to efficiently collect, connect, manage and exchange data while data security moves to expand the IoT network.

Making it a winning proposition 

The business should work towards implementing an enterprise data strategy and EIM Consulting in a secure and effective manner so that they can come up with IoT solutions. IoT data management strategies also help businesses to reduce costs and efficient installation of solutions. IoT data management is tough and cannot be handled by manpower alone.

Conclusion

A business organization faces various issues and some issues such as security, scalability and data gravity can be more effectively addressed using current platforms of IoT data management. The platforms easily help businesses to connect and deploy, thus their IoT infrastructure will scale. There is no need for building an IoT infrastructure from scratch and they can easily work with IoT platforms that provide easy access to IoT devices. The approach is however helpful for businesses of different scales. IoT devices are of heterogeneous nature and there are various additional challenges for data management, privacy and protection linked to the big data generated.

 

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