9 Best Practices for Data Management in Research

23 January 2019
9 Best Practices for Data Management in Research

Biomedical and clinical research require intensive efforts for data collection, analysis, and generation, all of which result in high-quality, statistically accurate findings. These findings are typically shared via established journals or conferences (which include distilled portions of the data collected) or are available as data products (which contain the raw data used to arrive at the findings, e.g., statistical data or patient information). As such, these findings and the data behind them are of high value to biomedical researchers, pharma companies, and healthcare practitioners as well as governmental, non-governmental, and private sponsors of research. As a result, in recent times, there has been an increased focus on finding ways to optimize this data through its effective management.1,2,3 As facilitators and distributors of clinical findings through key channels, publication managers play a critical role in shaping how this data is stored, accessed, and shared. This post will guide you through key data management practices that help publication managers handle data effectively.

Why you need to have a data management plan in place

An effective data management plan offers several benefits. First, it helps improve data accessibility, which forms an integral part of the recent global discussions around transparency and open science, because data sharing facilitates the replication of research methodology. Further, a good data management plan helps safeguard the integrity and confidentiality of information collected during clinical research, such as patient records. Following best practices in data management will also help publication managers ensure adherence to relevant regulatory guidelines surrounding the use and distribution of clinical data.

Best practices to follow for effective data management

Listed below are a few best practices publication managers should adopt when working with data:

1. Set up a process for 360° data management:

This includes systems and processes for data collection, reporting, presentation, storage, accessibility, archival, updation, and disposal.2 Also include detailed documentation of the processes you intend to follow, including how your data will be documented. The more detailed you get, the more effective your data management plan will be.

2. Communicate the requirements clearly:

As a funder, clearly communicate your data requirements to the research lead. Remember to include important details about the aspects such as data collection, storage, and retrieval as well as the conditions that are part of your data management policy.

3. Identify the data you need to collect and manage:

Identify all areas that involve data collection and work your way down. Consider the sources of information and types of files and classify them as data, for example, tables, graphs, software, algorithms, spreadsheets, text, audio and video files, images patient records, reference data sourced from other sources, models, patient records, and physical documents.

4. Ensure that relevant publication related guidelines are followed:

Specific principles guide publication writing, e.g., Good Publication Practice (GPP), recommendations of the International Council of Medical Journal Editors (ICMJE), Consolidated Standards On Reporting Trials (CONSORT), and the Committee on Publication Ethics (COPE) guidelines. Following the best practices outlined in these industry-accepted guidelines will help ensure effective data sharing and reporting.

5. Safeguard the confidentiality of sensitive information:

When making data available, mention how the confidentiality of sensitive information like patient data will be protected and how sensitive data will be stored and handled.

6. Use the right tools:

No data management plan is complete without the right tools. Today, several digital solutions, known as Clinical Data Management Systems (CDMS), help manage data, such as ORACLE CLINICAL, CLINTRIAL, MACRO, and RAVE. Identify the various functions for which you need tools and choose the one that meets your requirements.4, 5.

7. Think beyond data storage:

Data management is also about preserving data to prepare for issues such as data loss, URL changes, disk crashes, and file degradation. Include creating back-ups and data archives as part of your strategy.

8. Work with publishers who embrace open data and have data sharing systems in place:

Today, several major publishers, like Wiley, are recognizing the impact open access to information could have and are giving researchers and research funders the option to transparently share all study data. Working with publishers who support such initiatives is a good practice because they not only provide clear guidelines on data sharing but also provide access to information about data compliance tools and repositories.5

9. Perform regular data audits:

Given the rapid pace of research, existing data could soon become obsolete. Perform regular audits of your data to make sure that you have and provide only the most relevant information.

Following these best practices will help publication managers manage data effectively and ensure that the information being stored is reliable, easily accessible, and treated with confidentiality. Also, choosing journals or publishers who support data sharing will help publication managers ensure that they have access to relevant support, tools, and resources to ensure compliance with data sharing policies.

Note: This post is a sequel to our previous post on the importance and role of big data and data transparency in clinical research, publishing, and practice.


  1. Palgon, G., 2017, The pharmaceutical R&D process and the inherent data challenges, Liaison, https://www.liaison.com/blog/2017/04/07/pharmaceutical-rd-process-inherent-data-challenges/

  2. Michener, W. K., 2015, Ten simple rules for creating a good data management plan, PLoS Comput Biol. 11(10): e1004525. https://doi.org/10.1371/journal.pcbi.1004525

  3. George, R.; Truong, T.; Davidson, J., 2017, Establishing an effective data governance system, Pharmaceutical Technology, Volume 41, Issue 11, 42–45

  4. Krishnankutty, B.; Bellary, S.; Kumar, N. B. R.; Moodahadu, L. S., 2012, Data management in clinical research: An overview. Indian J Pharmacol. 44(2), 168–172.

  5. Wiley Author Resources, Wiley’s data sharing policies, https://authorservices.wiley.com/author-resources/Journal-Authors/open-access/data-sharing-citation/data-sharing-policy.html