Data Management is the development and execution of architectures, policies, practices, and procedures to manage the information lifecycle needs of an enterprise effectively.”
It is merely the ‘management of information.’ Organizations use Data management to make business decisions and to understand customer behavior, trends, and opportunities for creating extraordinary customer experiences. Data Management is a pivotal part of any organization as it:
- Acquires
- Validates
- Stores
And secures, the data to ensure the reliability and accessibility for users. It includes everything from data planning, managing, and documentation to storage.
An organization needs to have an effective aptness of data for better decision making. It is a crucial process that leads them to the success and reproducibility of an organization.
Managing data as a resource is an essential function of data management. Accurate and relevant data is a source of valuable information.
Data Lifecycle Management
The different activities involving Data Management is formulated into a concept called Data Life Cycle. It follows the steps from data generation to its execution.
It integrates the collection of data, analyzing, submission, preservation, discovery, and publication.
DLM approach is a comprehensive strategy for managing the organization's data to achieve their goals better.
It is important for an organization to follow this approach as it manages the flow of an information system by automating the processes which make it easier for the organization to segment the data according to specific policies.
Effective data management helps in limiting potential mistakes. The probability of errors like copy-paste, and linking of documents, will be reduced. Effective data management ensures the protection of the most valuable assets of the company.
DLM automates the data migration from one tier to another according to specified criteria and removes data when it becomes obsolete, giving higher productivity to an organization.
Data is a driving force for all the business decisions today, and this is why the process should be such that the data is accessible on demand. Data management helps in aggregating and correlating data while maximizing productivity.
Through data governance, the data is secured establishing the process of managing the data effectively. It follows specified guidelines and recommends any improvements in data security.
Data management conforms to the rules and regulations of maintaining the data. It is the adherence to the standards or laws which are overlooked before keeping data. Data compliance offers the highest value opportunity and increases efficiency.
Data management practices are not limited to tech agile sectors, but it also provides a considerable advantage to the finance industry as well.
There is regulatory compliance that is maintained through the centralized system of data management in any organization.
Financial institutions are combining their business strategies of data management with machine learning to cover numerous business aspects.
Though the automatization of risk management processes is still in the budding stage, it is still offering a huge impact on financial institutions by identifying the creditworthiness of potential customers.
It identifies and prioritizes customer data, commercial lending, and insurance results and monitors risk from various vectors of customer/organization information.
There is a vast amount of data that is voluminous and is diversified in nature. Financial data is processed manually.
Data management combines machine learning tools to extract real intelligence from data which contributes to smarter governance and better business solutions.
Data Management tools combine massive amounts of data from different resources to identify the possible outcomes of a business decision. It also gives out predictive analysis with a better understanding of the customers.
A large amount of data is stored and managed from social media, news trends, and other data sources. This data provides an insight into things such as customer’s lifetime value, stock market, etc.
In the education sector, environmental monitoring of development is a complicated task. Data management in the education system provides accountability and transparency.
Education systems are looking for an optimum resource that can contribute to the management’s growth as well as the teachers & students.
Education Management Information System (EMIS) is a system that gathers and stores data and generates various reports after analyzing it.
This system provides accurate data that helps in decision making. Several IT-based initiatives are implemented to such as mobile applications and GIS systems that follow more involvement of the parents where they can actively participate in the day to day processes.
EMIS keeps track of the day-to-day progress of students i.e., exam records, library details, and personal data. Personal data may include demographic data & class status.
This type of maintenance provides crucial insights that will help to analyze and monitor the improvements of the students.
EMIS reduces the workload on the teachers by providing quick access to each data of the students. Also, it helps to track the development of the teachers by monitoring the training, induction, and qualifications.
The EMIS system helps in tracking the resource distribution and checks on any unnecessary expenditure.
It also checks and controls which faculty has access to what kind of data. It analyzes the infrastructure development and provides the details all schools location, type of school, number of classrooms, toilets, library, science labs, etc.
Data management in healthcare creates a holistic view of patient-related data and offers personalized treatment.
The healthcare facility can communicate in a better way, which will result in faster delivery of the process. Healthcare data management is governed by law, namely HIPAA.
- Unifying the organization's goals with that of the physician's activity.
- Improves health outcomes by analyzing current health trends and offering a predictive analysis.
- The healthcare facility can make informed decisions related to the health of the patients based on the data insights.
- A healthcare CRM collects and stores data of the patients and generates an automatic report of each patient thereby reducing the burden on the physicians.
Many companies find it difficult to integrate themselves with a data-driven culture so much that it ends up as merely archived and forgotten. This can occur in the organization because of:
- Lack of resources
- Non-availability of right tools and environment
- Unskilled employees who are uninformed on how to use data and information
For example, in any healthcare facility, the administrators need to be tenacious about maintaining any data. Making data management a priority requires involvement from all the players of the organization.
Conclusion:
Businesses face compliance Sarbanes-Oxley Act, which regulates any organization on how to deal with particular types of data. For a data-driven culture to exist, there should be a grapevine hierarchy in any organization.
It must be a part of the mentality of any leadership and every employee must understand the importance of data in their role. When any organization has data-driven culture from top-down, success speaks for itself.
For any organization to become data-driven one needs to have quality assurance of the data and needs to enable broad data governance of the data collected.
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