What Is Data Management In Research? How Many Steps Are There?

What Is Data Management In Research? How Many Steps Are There?
Data management in research is a multistep process. It entails gathering, cleaning and storing of data to perform correct analysis and, thereby, generate useful findings. Data management in research refers to strategies that allow researchers to quickly discover, comprehend and apply their data at all project stages and in the future. Furthermore, proper data management can make data analysis, visualisation and reporting more efficient. It facilitates the researchers in publishing their research with ease and in less time. Researchers can set themselves up for success by formalising processes and avoiding frequent data handling errors. Researchers can free up more time for research by using basic data management techniques. This article will discuss data management in research and different steps to ensure effective data management.

What is Data Management?

Data management in research involves active data organisation and maintenance throughout the research process. In most of times, dissertation writing services in UK help students to in data management. After concluding the research, researchers usually choose an appropriate medium for data preservation. It is a continuous process that occurs throughout the data lifecycle. The majority of research endeavours create a large amount of data.

High-quality data enhances the overall value of the research study. As a result, effective data management is essential for high-quality research. Abiding by the best practices of data management in research can aid researchers in ensuring that data gathering and analysis methods are well-organised, intelligible and transparent.

What are the benefits of data management in research?

Researchers tend to lose data frequently. There is a long history of data not being available when researchers need it. Data loss is common in both small- and large-scale projects. It is because many researchers do not utilise data management practices. Even when academics have access to the data, researchers spend countless hours in identifying certain data files on a computer. In addition, without effective data management practices, it takes a lot of time in understanding what that data signifies. Many of these issues can be avoided and solved with data management safeguards and solutions.

Researchers benefit from a well-managed data since it requires less digging to uncover and understand the data. In addition, it takes less time in processing data and makes it available for collaboration, reuse and sharing. Data management in research ensures the continuation of a project in case of a failed hard drive or loss of a key partner who had access to the data. The primary aim of data management is to make research more efficient, allowing researchers to focus on scientific challenges rather than data administration. Proper administration of data can benefit scientific communities by increasing the speed of discovery. It helps in boosting the integrity of discoveries, thus allowing for better collaboration. Thus, effective data management in research can open up new possibilities for educational data use.

Fundamental Constituents of Data Management

Following are the cardinal elements of data management:
  • Collection of reliable data
  • Data organization
  • Storing the data in a database
  • Making amendments in the data if necessary
  • Verifying the authenticity of the data
  • Provision of data in an analysable format
  • Storing the data in a secure and reliable medium

Steps of Data Management

Data management in research constitutes of the following steps:


Making a plan is necessary for almost everything in life, and similarly, it is the foremost step in data management. You should make a plan for data management as soon as you finalise your research proposal. You should consider the following things while planning:
  • Ascertain how much data your research will generate in the light of proposed hypotheses and sampling size
  • Outline the data collection methods from the chosen sample size
  • Delineate how you will analyse the data
  • Identify the required tools to collect and analyse the data
  • How you will organise the data
  • Develop a filing method for organising your data
  • Database you will use to store the data and in which file formats
  • Identify how many people you will need to implement your data management plan
  • Identify the type of resources you will require to implement your plan effectively
  • Identify the processes through which you will make your data accessible to the larger scientific community
  • What your plans are for data preservation in the long run

Create Data

The second step in the data management process is to create data. In quantitative studies, this stage entails the following steps:
  • Decide what type of data do you want to collect.
  • How will you format the data?
  • Usage of standardised instruments, data collection procedures for maintaining consistency in data collection and checking error rates.
  • This stage requires checking the validity of the responses gathered through surveys or questionnaires by random re-interview process.
In qualitative studies, this stage includes the following steps:
  • Outlining the instruments through which the researcher will collect the data, such as focus group discussions and interviews.
  • Setting up all the recording equipment in a way that can capture the conversations on the topic.
  • Creating a safe environment for respondents for open discussion and maintenance of privacy.

Data Processing

The data processing stage involves converting raw data to a format that makes it convenient for the researcher to analyse it. Data processing for quantitative studies comprises the following stages:
  • Constructing an electronic database to handle all sorts of data. For example, to analyse multiple responses, numerical data or visual analogue scale data.
  • Establishment of comprehensible file and coding structures.
  • Production of a codebook for identification of data.
  • Determining which data will be included in the database and which will be removed.
  • Avoiding data entry errors by double entry.
  • Validating the consistency of the responses.
Data Processing for qualitative studies involves the following stages:
  • Transcribing the recorded materials, such as from the recordings of interviews and discussion.
  • Sharing the transcripts with the respondents for verification.

Data Storage

Data storage is necessary during the research process, but it is equally important in the long run. The data storage process entails the following steps:
  • Archiving the data in a secure repository.
  • Electronic data storage is recommended for storing the data.
  • Develop a backup to prevent the data loss.
  • Use strong passwords, level of access and data encryption methods to ensure the security of the data.
  • Data should be preserved for at least two years following the research.


The process of data management in research is cyclical. The data life cycle begins with creating data and ends with processing and storage. Data management is a collection of mixed activities that collectively build up the ability to access and use the data when needed. We hope that the guidelines mentioned above will help you effectively in managing the data for your research.


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