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What do you understand by Data Management? Why is Data Management important and how does it affect the over all quality of data generated? Which guidelines are followed by Data Management professionals?

Data Management: Meaning and Importance

There are a number of trends in technology which are continuously changing the ways of storing, processing and using data in present business scenario. Some of these trends are named as cloud computing, shared-service infrastructure, Internet of Things and Big Data. These techniques have made the management of data very easy. Although before using these techniques for managing data, an enterprise needs to know all the features, advantages and importance of such techniques1. This report describes the concept of data management by defining its meaning, importance and need for using the tools for data management. Further, it describes the effect of data management on the quality of gathered data and also describes the guidelines that are required to be followed by professionals engaged in data management activities. At the end of the report, a conclusion has been drawn to provide an overview of the study.

Data management is related to the practice of maintaining and organizing data processes to meet constant information and lifecycle needs. The need for data management begins with the electronics period of data processing, but the methods of data management have their roots in statistics, accounting, logical planning and other restraints. It can be defined as the process of acquiring, storing, analyzing, validating, and processing important and required data for the end users2. At present, most of the organizations are using Big Data to make important business decisions and acquire in-depth knowledge of customer behavior, market trends, and opportunities that may result in creating uncommon customer experiences.

The modern companies are turning to data management platform to make effective use of data gathered, analyzed and stored by them. Data management system is less time consuming and simplified process of obtaining results from the collected data3. The main advantages of data management system are improvement in data sharing, enforcement of integrity, minimization of data inconsistency, improvement in data security and easy backup and recovery of data in the situation of loss or theft.

Major level data management solutions and platforms enable the enterprises to control Big Data from main data resources in real time for enabling engagement with customers in an efficient manner and for increasing CLV (customer lifetime value). Data Management software is important as the enterprises are consuming and creating data at extraordinary rates. Top data management platforms enable organizations and enterprises to examine their customer from a 360-degree angle to gain critical, deep and brief knowledge of customer behavior which is helpful in giving a competitive edge to brands4.

Impact of Data Management on Data Quality

The importance of data management can be summarized as follows:

Good and effective data management can make an organization more productive. It also makes easy to find relevant information for the employees of an organization. Along with this, it also enables them to validate and conclude results in an easy manner. Data management provides a structure that should be used while storing the data and sharing it with other users or employees5.

Data management allows an organization to avoid unwanted and unnecessary duplication of data. It stores and make data easily referable and also ensures that employees may not have to conduct analyses on the topic which has already been analysed6.

An organization is required to make decisions immediately to take the advantage of the opportunities. This is possible with the help of data management system as a good data management system allows the employees to access information of the market conditions and changes. As a result, data management and sharing system enables a company to make decisions in a very quick manner.

If data is not managed properly then it is possible that the information may fall into the hands of competitors or wrong persons. For example, Sony, a well-known brand of electronics was targeted by computer attacks which led to the loss of information of 77 million play station users7.

An effective data management ensures that information and stored data will not be lost and can be accessed easily at the time of requirement. Data management plan put things and information at the right place and also has the facility of back up and retrievable of important information from the secondary source in case the primary source become inaccessible due to any reason.

Data management largely affects the generated data in terms of quality improvement, storage, analysis and interpretation. The data collected by an organization includes both types of data that is primary and secondary. An effective data management plan identifies the type of data and categorizes the same to save the time of the user. It also identifies the relevant data to the project and automatically eliminates irrelevant and time-consuming information8. When data is collected by the employees, it is usually in raw form which may take a lot of time and efforts. It can also be saved by applying data management plan and techniques. Data processes are carried out by data management tools like big data, cloud computing, internet of things which are based on the rules and regulations of data management. It enables the company to work on data that is resulted after applying all the rules and it also increases the accuracy of results9.

Guidelines for Data Management Professionals

Usually, data collected from various sources and it is almost difficult to identify that which source is relevant and which is not. By analyzing the quality of data, data management tools help the employees to find out the same. With clean and reliable data, an organization can provide professional level services to the customers and can also create a positive image by delivering the order on or before time. The duplicity of data can also be identified by the tools and techniques which is automatically removed at the time of providing final results. Data management tools also have the abilities of cross-checking of data against a trusted source for improvement or validation10.

As data management is an important aspect on the basis of which, an organization takes final and important decisions related to operational, financial, marketing areas. There are some guidelines that are to be followed by the professionals engaged in data management process. These guidelines can be summarized as follows:

The professionals are required to consider that DBMS selected by them should be user-friendly so that all staff members can use it without facing any issue. The selected DBMA should also be appropriate for the purpose of analyzing collected data in the desired manner11.

Sensitive business data and personal information of customers should be stored strongly and securely to protect it from theft and loss. Such theft and loss can be of two types including tangible risk like risk from fire and intangible risk like the risk of hacking, corrupting, viruses and human errors. The professionals, responsible for effective data management are always guided by their organizations to keeping the data secure form all such risks.

The professionals are guided to check that whether the modules available in DBMS meet the requirements of the business or not. This can be analyzed by considering following aspects:

  • Filter and Extraction of data
  • Proper Analysis
  • Modeling and Segmentation
  • Automation
  • Strategy Forecasting
  • Visualization of Results
  • ROI Management and Campaign Planning

Data Management professionals are strictly guided to check updates related to the stored data and present and future processes. There should be a frequency in accepting the updates of data and some important day to day updates should be automatically accepted by the system so that processes and results may fulfill the current business requirements along with all the trends and updates12.

The tools and techniques selected by the professionals should be cost effective to the company. The entire process of data management includes hiring technical experts, paying for the licensing of data, maintaining records and consumption of hard and soft cost for keeping the data secure. Therefore, data professionals should examine all these aspects because these are the main reasons for increasing the cost of using data management.

Conclusion

This report concludes that an effective data management system can lead to an organization in formulating productive strategies from customer perspective. The same idea can also be concluded by studying the importance of Data management system. The total systems and techniques affect the quality of collected data and therefore an organization should carefully analyze the features of a database management system and their compatibility level with the organizational objectives. The report also concludes that the professionals, responsible for managing DBMS are required to follow the guidelines so that data can be managed in an appropriate manner and can be used by the users at the time of requirement. Data management is not an easy function and is required to be performed by the qualified professionals. Also, all the organizations working in the present scenario should majorly consider the applicability of data management systems so the data can be saved from any type of misuse and results can contribute in increasing the profitability of the organization.

References

  1. Buys CM, Shaw PL. Data Management Practices Across an Institution: Survey and Report. Journal of Librarianship & Scholarly Communication. 2015 Apr 1;3(2);1-24.
  2. Cox AM, Pinfield S. Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science. 2014 Dec;46(4);299-316.
  3. Ali S, Miah SJ, Khan S. Analysis of interaction between business intelligence and SMEs: learn from each other. JISTEM-Journal of Information Systems and Technology Management. 2017 Aug;14(2);151-68.
  4. Alam JR, Sajid A, Talib R, Niaz M. A review on the role of big data in business. International Journal of Computer Science and Mobile Computing, IJCSMC. 2014 Apr;3(4);446-53.
  5. Wamba SF, Gunasekaran A, Akter S, Ren SJ, Dubey R, Childe SJ. Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research. 2017 Jan 1;70;356-65.
  6. Parks R, Thambusamy R. Understanding Business Analytics Success and Impact: A Qualitative Study. Information Systems Education Journal. 2017 Jun 1;15(6);43.
  7. kumar Das T, Mishra MR. A Study on Challenges and Opportunities in Master Data Management. International Journal of Database Management Systems. 2011 May: 3(2);129-139.
  8. Khalil T, Al-Refai M, Fayez AN, Qudah MS. TOWARDS EFFECTIVE CUSTOMER RELATIONSHIP MANAGEMENT IN OMAN: ROLE OF BIG DATA. International Journal of Research. 2017 Oct:5(10);92-100.
  9. Shorfuzzaman M. Leveraging cloud based big data analytics in knowledge management for enhanced decision making in organizations. International Journal of Distributed and Parallel Systems. 2017 Feb 15;8(1);1-13.
  10. Torabi F, El-Den J. The impact of Knowledge Management on Organizational Productivity: A Case Study on Koosar Bank of Iran. Procedia Computer Science. 2017 Dec 31:124;300-310.
  11. Khan RA, Khan FA, Khan MA. Impact of training and development on organizational performance. Global Journal of Management and Business Research. 2011 Jul 6;11(7);63-68.
  12. Listiani T, Kartini D. Application Performance Management Impact On Organizational Performance Local Company (Studies In West Java-Indonesia). International Journal of Scientific & Technology Research. 2015 Jan 1;4(2);191-8.
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My Assignment Help. Understanding Data Management: Definition, Importance, And Guidelines [Internet]. My Assignment Help. 2021 [cited 19 July 2024]. Available from: https://myassignmenthelp.com/free-samples/kit712-data-management-technology/the-effect-of-data-management.html.

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