Data management is the process of collecting, using, and storing data most efficiently and less expensively. It usually helps to optimize the usage of data within the bounding policies and regulations of an organizationto ensure that decisions made can maximize profits while minimizing cost. In the current world, data management is becoming more important since organizations are increasingly relying on intangible assets to create value. It revolves around the implementation of procedures as well as policies that help to put an organization in a position to control its data regardless of where it comes from.
It is majorly concerned with end to end path cycle of data from its creation to the retirement. For effective management of data, an organization must come up with reliable strategies and methods to assess data, storage as well as its integration to make it useful in analysis within the organization. In an organization, data mainly comes from transactional as well as operational systems, social media, and smart devices, among other sources (AM Huberman, 2017). However, the quality of data majorly depends on how it is used other than its quality, format, or its source. With data management, any complications related to law or any breach in security are minimized.
Moreover, data management provides quality and more accurate data when and where it is needed with zerolevels of ambiguity or miscommunication.Datamanagement majorly covers a wide range of aspects such as creation, updating it across various data drivers, storing it across multiple drivers, availability of data and risk management, data safety and security as well as the destruction of data concerning the compliance laws and regulatory requirements(AM Huberman, 2017). One of the most common strategies in the management of data is through the use of master data. With this file, a property associated with an assetis provided to do way with ambiguity or competitive data policies that may give the organization a hard time in the control and management of its data.
Importance of data management.
Data is one of the most important assets of an organization, and it is the foundation with which organization decisions are made. Once data is complete, accurate, and organized properly,it contributes a lot to its growth and development. Currently, the data connected to an organization is usually on an unpredictable scale, which makes it hard to process (RB Bhise, 2015). This raises the need to implement and establish strong data management systems within an organization.
Through proper management of data, the productivity of an organization is usually improved. In big organizations, data can be accessed easily, which leaves it more organized and productive. For instance, it saves the organization the time used when looking for information that promotes efficient working. Also, workers can communicate and pass information to others(AM Huberman, 2017). With data management, past correspondence of data can easily be accessed, which helps to prevent miscommunication caused by lost messages before they are delivered. Employees are, therefore, able to provide the best of their services, which leads to a boost in productivity.
Data management within an organization helps to smoothen its day to day operations. It can create a seamless operating system, which is a dream for many organizations. It is one of the determining factors in the prosperity of any organization. For instance, taking too much time to respond to a customer may affect the loyalty trends around them, which is risky in terms of business success. A proper management system ensures that responding to the customer is timely, and competition can be managed(RB Bhise, 2015).
Appropriate management of data helps to reduce risks associated with company information. For instance, personal data such as credit card information, photos, personal addresses, or phone numbers should be protected from unauthorized people. It is essential to protect this data with the best data management and security system since any data which is not properly managed may fall in the hands of the wrong people. The theft of data willalso harm the growth and development of the company. People usually want to leave personal information, a company or organization that they trust in terms of privacy and security. This makes it essential for the proper management of data.
Also, with the right data management systems, and organizationscan incur less cost, especially in the occurrence of an unexpected event. It also helps to prevent time and money that could be used to duplicate information that already exists. Therefore with a good data management system in place, less is spent on events that should not have happened(RB Bhise, 2015). The chances of losing company information are usually low with the right management of data. It ensures that data is backed up in case of any failure of the system or any sudden glitch. Any lost information can easily be retrieved, thus limiting the repercussions of the same.
Data management is a foundation for better and proper decision making within an organization. When data is stored in the right place and where it can easily be retrieved, the decisions so made are forth viableand promising. Organizations usually have different ways of processing information, but a centralized data management system creates a framework to plan, delegate, and organize. It helps in strategic planning, litigation, documentation as well as auditing within an organization. Also, a good system will ensure proper feedback, which results in positive updates, thus benefiting the organization in the long run.
Implications of data management on the quality of data.
Quality data can only be obtained from a proper data management system. For instance, proper management of data improves its accuracy since it is not tampered with. Inaccurate data creates problems within an organization and can lead to poor decisions. Any decisions made with poorly managed data will automatically result in inaccurate data leading to poor conclusions. Also, a good data management system ensures that data is complete, and there are no omissions or gaps. With incomplete data, there is usually a problem when coming up with accurate ideas or quality data (DP Ballou, 2018).
Data management also has implications for its validity and relevancy. Properly managed data should be useful in initiatives as well as campaigns that it is used in. even if the information has other qualities of data, it must be useful. In terms of validity, how data is collected and managed affects its validity and, thus, its quality. Data management should ensure the validity of data through the right formats, the correct type as well as the correct range. Once data fails to meet these conditions, organizing and analyzing would be problematic.
Timeliness and consistency is an aspect of data which hugely relies on its management. The sooner data is recorded after the occurrence of an event, the better the quality. Typically, data usually becomes less useful and informative as time goes by(DP Ballou, 2018). The use of outdated data may result in less accuracy in its analysis, thus affecting its quality. In terms of consistency, data should be the same when comparing different types of data sets. Proper management ensures that data remains consistent, thus improving its quality.
Guidelines followed by data management professionals.
Data management professionals must describe the type of data being managed. This may involve field work, general lab reports, as well as surveys. They should state the type of metadatato improve understanding and its reuse. They should state whether data can be reused for other studies. The formats should also be described to ease use(MD Wilkinson, 2016). This may either involve raw or already processed data. Many organizations usually recommend machine-readable formats.
Data management professionals must ensure that data preservation and storage are properly done. This involves data backups, cloud storage, migration methods, as well as access protocols and procedures. Where and how data should be stored after a project should be clearly described. They should also outline the tools, strategies, and plans on how to avoid degradation or loss of data.
In data sharing and public access to data, any restrictions or licenses should clearly be described in data management. The procedures to access and share data should be outlined in any data management system within an organization. This involves copyright, confidentiality, disclaimers, among other data management restrictions. However, limiting the data distribution is normally discouraged. They should also provide a contingency plan in case data is breached. They should also provide how the information would be monitored during and after a project(MD Wilkinson, 2016). This involves progress in the sharing of data such as software, database, or publications.
In the current day, organizations need to implement data management strategies that provide the most effective and efficient way to manage data across diversified but unifying data tiers. Data management systems are usually established on data management platforms involving a database, warehouses, data analysis, as well as large data management systems. In simple terms, data management is how answers and ideas can be extracted from raw data. It is, however, not all about Information Technology but also revolves around planning, statistics as well as accounting.
AM Huberman, 2017, Data management methods and analysis…
DP Ballou, 2018, Implications of data qualityin data management systems.
RB Bhise, 2015, Importance of data mining in the higher education system, Journal Of data management and analysis.
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