The business models of IoT are based on data analysis. Whether responding to emergencies or discerning patterns across a multitude of historical data, a variety of approaches to obtaining information are made possible by IoT companies. Despite the critical role that data plays in creating new values, businesses often do not have a strong strategy for managing data.
Problems related to data growth and IoT
The growth of data has been explosive in recent years: 63% of companies manage 50PB or more. The rapid pace of annual data growth, sometimes even between 40% and 50%, complicates the data management challenge. This growth creates a huge challenge in cost management and effective data management. In recent surveys, 51% of companies admit that their backup infrastructure (eg tape backup) fails to keep pace with data growth and finds data migration painful. The lack of an effective backup and growth management strategy can be very expensive. Downtime costs between $ 50,000 and $ 5 million per hour, which can result in incalculable reputation damage. More importantly, opportunity costs when data is unrecoverable or unavailable for analytic analysis can lead to significant competitive disadvantages.
How data flows in an IoT application
Before designing an effective data management strategy, it is helpful to understand how the data collected will be used. In a typical scenario (Figure 1), sensors and control devices at the edge of the network continuously collect data, and gateways transmit data to local or public clouds. The collected data must be quickly evaluated and classified in terms of frequency of access. For example, critical alarm data will be frequently accessed to ensure that patches are in place and actions at the site level are performed, while log / routine data collected for regulatory compliance must be maintained. archived and will only be accessible by exception.
Recently, with the emergence of machine learning algorithms and artificial intelligence, archived data, often referred to as "cold data" – has become invaluable for forming algorithms and designing new information. Stakeholders in data management need to balance the current and future utility of the data collected and determine which data will be stored "cold" and which will be readily available.
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Real-world use of IoT data
The type of data collected is also an important factor in determining a storage strategy. Here, the IoT application and the industry play an important role. Table 1 presents some data collection scenarios in four business areas and the type of information that can be exploited by the data.
Solutions you should use for your data storage needs
Paradigms of data storage in the form of blobs for unstructured and binary data, data lakes for large data analysis, files to share, tables for NoSQL data without schema, etc. are all important to consider as decisions about the data strategy are made.
Data Management Solutions
If the application primarily generates binary and unstructured data, it is essential to find the most efficient way to back up, archive, and back up data. Similarly, if the analyzes correlate structured and unstructured data, it is necessary to have the correct approach for lacustrine data.
Another key element in determining a data management strategy is the data acquisition rate and, therefore, the growth rate of the data collected. Very frequent additions of small data (eg textual data) can be as problematic as sporadic additions of large data. In addition, HD image data sources or graphics data can be particularly expensive to store and archive permanently. The exchange of business needs with the frequency of data collection, the amount of historical data needed and the cost of storing data is critical to achieving a sustainable data management strategy.
Data security solutions
With the nature of the data and the use of the determined data, data security becomes a priority. Because of the value and compliance of data with respect for data, it is essential to ensure data security. Whether you choose a DIY option or a commercial option, securing data is paramount, as unauthorized access to this data can have costly consequences, as evidenced by several highly publicized violations at FedEx, Target, and so on. Similarly, do not lose data in case of failure or failure. is also crucial. Regular testing of the backup and recovery process is of course the best practice.
On-site storage or public cloud
The decision to host data on premise or in a public cloud is perhaps the most important consideration in the cloud age. While there are benefits to both true complexity accounting, premium control, total cost of ownership and all inherent legacy switching costs can provide an objective assessment for making decisions. On-site infrastructure gives organizations more control, but requires strong skills, resources, and processes to succeed. More importantly, the range of features required to run a modern enterprise data center can be substantial. Facilities management, electrical and electrical infrastructure, IT purchases, management staff and 24/7 support for users can be expensive. Nevertheless, infrastructure control, security, or proprietary considerations may tip the balance in favor of an on-site infrastructure.
Off-site public clouds or co-located Infrastructure-as-a-Service (IaaS) models consider compute storage as a service that organizations can obtain on demand. This creates great flexibility, focuses IT resources on added value, and simplifies infrastructure management. Robust regimes for backup, on-demand, and on-demand scalability provide a growing enterprise with a scalable data management infrastructure.
Hybrid environments combining on-premise infrastructure and public cloud are becoming increasingly popular as they help overcome stakeholder concerns related to control and ownership issues, while providing on-demand flexibility and scalability. Stakeholders also appreciate the security and backup options offered by cloud providers.
For an effective decision, a thorough exercise in total cost of ownership is invaluable. By highlighting the various aspects of infrastructure management, the opportunity costs of scale and flexibility are important for the evaluation of options.
An effective IoT Cloud data storage strategy requires consideration of the following
1. How will the collected data be used and how often will it be consulted?
2. Type, nature, volume and speed of collected data
3. How data will be secured, backed up and recovered
4. The most efficient storage strategy: on-premise, cloud or hybrid infrastructure
5. The most effective ways to upgrade storage, as needs will inevitably change
6. The total cost of ownership (TCO)
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