Electronic Data Collection Consequences

¶ … research database be part of the protocol development team. Perhaps the most eminent of these reasons pertains to the need to have the data collection process aligned with the efforts of the protocol development team. The sort of congruence that needs to exist between database collection and protocol development is essential to ensuring that the data that is captured for analysis directly relates to the research study as a whole.

In this sense, the research database design process contributes to the development of a research study's protocol in that it provides a mechanism for determining what sort of data is possible to capture (and able to be analyzed) to support the purpose of the study. Again, it is critical to have these two elements in a research study aligned because the former directly influences the latter.

If a research database is not able to capture data that is used to facilitate the overall purpose and hypotheses of a study, then those designing the study's protocol need to address that protocol to find evidence that is empirical. For the most part, the research database is responsible for determining what sort of evidence is empirical and accessible to researchers for analysis. Electronic data capture is one of the trends in current technological advances that is gaining a significant amount of social prominence.

So called big data and analytics designed specifically for the variety, volume and velocity of Big Data can significantly impact research studies that need to comb through vast amounts of what is termed unstructured data (Harper, 2014). Disadvantages associated with electronic data capture include the fact that in several instances, the machinery and hardware that is required for electronic data is costly.

Additionally, depending on the degree of sophistication associated with the particular method for electronic data capture that is used, it may be difficult to find individuals that can successfully manipulate such hardware and software to gain the requisite data. Data scientists, for instance, might need to manipulate such mechanisms for data capture and analytics, although there are also self-service options which researchers might employ as well (Harper, 2014). Advantages include the ability to analyze time sensitive data expediently. Additional advantages include the deployment of real time and predictive analytics options.

Moreover, some of the more sophisticated statistical calculations process is able to be animated with certain electronic data capture options. There is an inherent difficulty associated with attempts to keep electronic data strictly confidential. It is possible to do so, although success in this regard might necessitate any number of information technology security and governance mechanisms Moreover, these security measures actually allude to the source of difficulty with electronic data, most of which is accessible via the internet.

The wide open opportunities for hackers and malware to infiltrate data stored online (and even in the Cloud, perhaps) are what make this sort of data vulnerable and difficult to keep confidential in certain instances. Still, it is possible to keep data confidential depending on where it.