Data in-motion, Data at-rest, & Data in-use

Data in-motion is the real-time streaming of data from a broad spectrum of technologies, which also encompasses the data transmission between systems (Katal, Wazid, & Goudar, 2013; Kishore & Sharma, 2016; Ovum, 2016; Ramachandran & Chang, 2016).  Data that is stored on a database system or cloud system is considered as data-at-rest and data that is being processed and analyzed is considered as data-in-use (Ramachandran & Chang, 2016).  The analysis of real-time streaming data in a timely fashion is also known as stream reasoning and implementing solutions for stream reasoning revolve around high throughput systems and storage space with low latency (Della Valle et al., 2016). Cisco (2017), stated that data in motion’s value decreases with time, unlike data-at-rest.

Data-in-motion focuses on the velocity and variety portion of the Gartner’s 3Vs of Big Data definition (Della Valle, Dell’Aglio, Margara, 2016). This is becoming an important issue in data analytics due to the emergence of the Internet of Things (IoT), which could be deployed in the cloud and can constitute as the variety portion of data-in-motion (Ovum, 2016).  Della Valle et al. (2016), stated that knowledge had been represented in various ways and the analysis of this data would allow for understanding implicit information hidden in these different forms of explicit knowledge.


Figure 1 is adapted from Della Valle et al. (2016), which is a conceptual model for real-time streaming that can provide a scalable solution for large volumes of data or a large variety of data sources. In this diagram (Figure 1), a wrapper hides the individuality of the data source by transforming it to look like one data source, while the mapping ties all the data together (Della Valle et al., 2016).

Kishore and Sharma (2016) and Ramachandran and Chang (2016), describes in their conceptual model the definition of data-in-motion as data-in-transit from two systems of data-at-rest.  Kishore and Sharma (2016) stated that data is most vulnerable while it is in motion. Given the vulnerabilities of data-in-motion, Kishore and Sharma (2016), discussed that protecting data-in-motion could be done either through encryption or Virtual Private Network (VPN) connections through the entire process. Ramachandran and Chang (2016), stated that encryption is the only security technique for data-in-motion.  However, security is not addressed in Della Valle et al. (2016) system, and this is just one reason of many on why Kishore and Sharma (2016) suggested that security for data-in-motion as an area for future research.

Cisco (2017), illustrates the need for further knowledge and development of data-in-motion research because retailers have the most to benefit from it. For retail environments, data collection and processing is key for thriving and increasing their profit margins because retailers are trying to build brand recognition, brand affinity, and a relationship with their customers.  All of this is done to enhance the customer experience, for example using the data coming from the web camera to create a virtual mirror where the customer can try on accessories and see how this accessory fits their personal style, is creating a customer experience from data-in-motion (Cisco, 2017).  This virtual mirror must use facial recognition technology similar to the use of Snapchat filters.  Other ways retailers could use data-in-motion data is by collecting phone data location and demographic data to create a real-time promotion for nearby travelers or in-store customers (Cisco, 2017).  Finally, Cisco (2017), also discussed how data in motion could help in providing proactive and cost-effective health care, enhancing manufacturing supply chain, provide scalable and secure energy production, etc.


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