Data Management and the Difficulties Associated With the Practice and How to Resolve Them

There are multiple difficulties associated with data management and different approaches on how to resolve the issues. Data in business keep increases at such a fast rate, and the data management programs are responsible for ensuring that the integrity of the data is maintained. A data warehouse comes in handy when handling the issue. A typical data warehouse objective is to store history; scalability means an increase in the size of the data and as history is stored more space is required which has proved to be a major issue in data management (Wu et al., 204). Within the data warehouse, it has been possible to manage this problem by defining a number of functions within your database; these include details such as; how long in terms of years is the company data is to be kept. This helps the warehouse keep the data for a specific duration of time before it's released as the data grows. This also helps in ensuring consistency of the data within the warehouse. Scalability becomes less of a problem with the integration of a data warehouse since as years go and the data increases the deleted data after the set duration, gives room for more new data which is more relevant for the company.

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Data warehouses can also be used when retrieving small portions of data that is relevant for specific decisions. This is possible through the storing of data in the level of importance where the most relevant data responsible for decision making is the most recent data within a database. A data warehouse solves this issue by enabling the data to be stored in the database in relation to how recent the data is (Wu et al., 204). This results to faster decision-making processes, which are carried out efficiently and smoothly. The most relevant data is set in categories which are easily accessible.

Online transactional databases have also helped resolves multiple data management challenges such as data security, quality, and integrity of the data. This has been achieved via real-time alerts which are achievable within the online transactional databases. The systems have continuous monitoring which provokes a function within the system when an operation within the database takes place. This triggers an alert whenever an event occurs; the event can probably be an email to the database management program or an alert in forms of beeps in some systems which in return ensure data security, and integrity of the data. The real-time alerts are scheduled timely either after 1, 5, 10, or 30 minutes Kogan et al., 2018). In case illegal operations are carried out on the dataset the monitor programs are able to signal the operation and enable the termination of such requests.

Data mining is the practice in data management where new information is generated by examining pre-existing databases. This has been one of the most important solutions to most of data management problems. For data scattered issue which is seen in most databases, data mining helps gather the relevant data from the scattered data and transforms it into productive data and stores it in a new database hence solving the problem (Larose, 2014). Data mining helps access multiple systems from different departments of the organizations such as the sales, customer shopping behaviors among other sectors, and analyzes the pre-existing patterns within the databases to come up with new data which can be utilized by the company in making informed decisions. The problem of managing the scattered data is resolved and transformed into a productive measure within a company or an organization.

Data warehouses are also used in resolving cases where data is stored in multiple formats. The data warehouse takes in different functions which help process this data despite the different formats. Functionalities such as whether the key for the data possesses the same data types, domains, and the length, functions help the data warehouse to take in data from different sources and manipulating the different formats (Jukic et al., 2016). Other functions such as new data relationships can be implemented within the data warehouse, and this also helps overcome the different data format challenges faced during data management. Data sharing is one of the primary goals of the data warehouse; there are multiple functions responsible for this, which includes; data cleansing, data dispute resolutions and Metadata access component help achieve the goal (Chen & Zhang, 2014). These functions help overcome the different data formats barrier within data management.

Online transactional databases call for the real-time requirement where data is accessed and manipulated from different parts of the world simultaneously. The legal rules in most countries within the world have less limitations on this as compared to the sharing of data. This helps resolve the issue on the equitable share of data from different parts of the world due to the constant monitoring of the data transactions and manipulations which offers room for more security analysis of the data and adherence to the legal terms of involved countries (Kogan et al., 2018).

Data management has multiple challenges, but different technological advancement in the field provides solutions to resolve the difficulties. A view of fields such as data mining, data warehouse, and online transactional databases among other functions help resolve the data management challenges.

References

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Jukic, N., Vrbsky, S., & Nestorov, S. (2016). Database systems: Introduction to databases and data warehouses. Prospect Press.

Kogan, A., Sudit, E. F., & Vasarhelyi, M. A. (2018). Continuous online auditing: A program of research. In Continuous Auditing: Theory and Application (pp. 125-148). Emerald Publishing Limited.

Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107