Data management is becoming the backbone of good online marketing strategy and research. Data should be clean, quality, and reliable to give strong insight to research, behavioral patterns, and creating marketing campaigns. There are a few steps that steps that should be followed in order to ensure good data management. These steps are:
Outline the research goals- Starting small and outlining goals will help to keep only the pertinent data for the research goal.
Prioritize data protection and security- This is important so that the data is not breeched by others.
Focus on data quality- This is important to make sure the data being collected is clean and reliable.
Reduce duplicate data- This step is put in place to handle any redundancies.
Ensure the data is readily accessible to the team- The data being collecting should not be easily accessible to those who do not have permission to view it but should also be easily accessible to those who need to access it.
Create a data recovery strategy- This step is to assure if any accidents happen, the data obtained can be easily recovered.
Use quality data management software- This step is essential to creating a quality data management process. The wrong data management software could cause multiple issues to the data being collected and managed.
Soffer, Ari. July 10, 2019. 7 Best Practices for Effective Data Management. Leadspace. 7 Best Practices for Effective Data Management in 2019 – Leadspace.
Data management involves collecting, storing, organizing, and keeping an organization’s data. Data is the key component of research. Therefore, research goals guide data to be stored after it has been collected. Sorting raw research data for storage is the initial step of data management. The process separates data based on relevance to the research.
Data protection protects data from being corrupted, lost, or compromised. According to (Gharaibeh et al.,2017), data can be used to leverage personal safety. Malicious hackers may try to access databases and gain information for crime or blackmail. Also, databases are prone to virus attacks from unsafe programs installed on the database computer. As a result, there are various ways to protect data from corruption, compromise, or loss. First, a backup is a necessity in case of data loss. Second, encryption of data protects it from compromise. Finally, anti-male ware software protects data from corruption while firewalls and cyber security systems prevent hackers from accessing data.
Quality data is data that fits the research context. Several features describe quality data; accuracy, completeness, consistency, and timeliness. Quality data should not have missing values and must be up to date. Data managers are responsible for ensuring data quality. Data quality can be managed by current methods such as data cleansing using a technology referred to as visual analytics (Liu et al.,2018).
Redundancy compromises data quality and integrity. Data redundancy is eliminated by a process called database normalization. Here, databases are structured in line with a series of normal forms in a relational model. On the other hand, data access is granted through a user’s interface. There are two methods of data access, random access, and sequential access. The former allows researchers to access data within constant time while sequential access is faster to access.
Restoring previous system backup in a server with the base operating system is the best data recovery strategy. It allows individuals to update backup files in the servers at fixed intervals. Data recovery is made by accessing backup servers. Finally, data management software turns diverse data into a constant resource such as a database. They may also have analytical and data processing features—for example, SolveXia.
Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017). Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys & Tutorials, 19(4), 2456-2501.
Liu, S., Andrienko, G., Wu, Y., Cao, N., Jiang, L., Shi, C., … & Hong, S. (2018). Steering data quality with visual analytics: The complexity challenge. Visual Informatics, 2(4), 191-197.