The Research Ops Toolkit: Optimising Research with an Efficient Tool Ecosystem
Research operations (research ops) is a relatively new concept in the research industry that focuses on streamlining research processes, optimizing workflows, and maximizing the efficiency and quality of research efforts. To achieve these goals, research ops teams use a wide variety of tools and practices that support data management, participant recruitment, collaboration, documentation, and communication.
In this article we explore the composition of an effective tool ecosystem for research ops teams; what research ops managers consider when choosing the right tools for their team and workflow, and how Indeemo fits into this ecosystem.
What is Research Operations?
Research operations is a set of practices and principles that aim to enhance research productivity, efficiency, and quality by applying operational techniques to research activities. It involves a range of activities, including creating and managing research project plans and timelines, developing and implementing research protocols and methodologies, ensuring ethical and legal compliance, managing data collection, analysis, and reporting processes, and facilitating collaboration and communication among research team members and stakeholders. The ultimate goal of research operations is to maximise the value and impact of research projects while minimising costs, errors, and time-consuming tasks.
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Common Tools Used for Research Ops
Research operations teams use a variety of tools to streamline and optimise research processes. These tools can be classified into different categories based on their functionalities. Here are some of the essential tools used in research operations.
| Category | How it Helps Research Ops |
|---|---|
| Project Management | Helps to create and manage project plans, assign tasks, track progress, and collaborate with other team members. |
| Communication | Enables effective communication through instant messaging, video conferencing, and file sharing. Facilitates collaboration among team members. |
| Data Collection and Analysis | Facilitates the collection, storage, and analysis of research data. Helps to manage data quality and reduce errors. |
| Participant Recruitment | Facilitates the recruitment and management of research participants, such as online survey respondents, clinical trial participants. |
| Research Repository and Documentation | Helps to store, organize, and share research data, protocols, and results. Facilitates collaboration and knowledge sharing among team members and stakeholders. |
| Workflow Automation | Automates repetitive and time-consuming tasks in research workflows, such as data cleaning or data entry. Helps to increase efficiency and productivity. |
| Collaboration and Sharing | Facilitates collaboration and sharing of research data and documents among team members, stakeholders, and external partners. Helps to streamline communication and knowledge sharing. |
Procuring a Tool for Research Ops: What to Consider
When evaluating tools for research operations, research ops teams should consider several factors to ensure that they choose tools that meet their needs. These factors include functionality, ease of use, compatibility, security and privacy, scalability, cost, and support and maintenance.
Functionality
The first consideration when evaluating tools for research ops is their functionality. This includes evaluating whether the tool has the features and capabilities needed to support the specific research workflows and processes of the team. It's important to look for tools that can automate repetitive tasks, streamline collaboration, and improve data management and analysis.
Ease of Use
Another important factor to consider when evaluating tools is their ease of use. A tool that is difficult to learn or use can slow down workflows and reduce productivity. It's important to look for tools that are intuitive, user-friendly, and require minimal training.
Compatibility
It's important to ensure that the tools being evaluated are compatible with the existing technology infrastructure of the research team. This includes ensuring that the tools integrate well with existing software, platforms, and systems, and that they can be used across different devices and operating systems.
Cost
Cost is another important factor to consider when evaluating tools. Some tools may be free or low-cost, while others may require a significant investment. It's important to consider the value that a tool provides in relation to its cost, and to ensure that it fits within the team's budget.
Security & Privacy
Research data is often sensitive and confidential, and it's important to ensure that the tools being evaluated have appropriate security and privacy measures in place to protect data from unauthorised access or breaches. It's important to evaluate the tool's security protocols and compliance with relevant data protection regulations.
Customer Support
Finally, it's important to consider the level of customer support that a tool provides. This includes evaluating the tool's documentation and user guides, as well as the availability of customer support and training resources. It's important to choose tools that provide adequate support to ensure that any issues or problems can be quickly resolved.
The Crucial Role of Research Ops in Data Ownership
One of the primary concerns in research operations is data security and ownership. By owning the data and ensuring its complete portability, research professionals can control where the data is stored, export it, or put it in their own data warehouse.
Research operations professionals play a critical role in ensuring data ownership. Unlike other tools that may restrict data access, a well-designed tool ecosystem should provide for easy data transfer and complete ownership. This ensures that data remains accessible and in control of its rightful owners. When looking to procure a tool for their team, a research ops manager should look for a tool that allows them to be the data owner. Ultimately, you want a tool that is the data processor and not the data controller.
The Importance of Data Security for Research Ops
A research tool used by a research ops team should have robust data security features that protect sensitive data from unauthorised access, theft, or loss. The tool should have strong access controls in place to limit access to sensitive data to only authorised personnel. The tool should be compliant with relevant data protection regulations, such as GDPR or HIPAA, depending on the type of data being processed or stored.
Overall, a research tool used by a research ops team should have robust data security features that protect sensitive data from unauthorised access, theft, or loss. It should also comply with relevant data protection regulations.
The Role of a Data Collection Tool for Research Ops
For researchers, the most commonly used tool at their disposal is the data collection and analysis tool. Whether it be quantitative research like surveys, or qualitative techniques for UX discovery and a deeper understanding of user behaviour, the tools adopted and onboarded will often determine the speed of fieldwork and validity of data.
For research ops, choosing the right platform for data collection, will typically need to meet a range of different criteria. In the table below, we outline the key areas a data collection tool should cover for a research team.
| Category | How it Helps Research Ops |
|---|---|
| Research Design and Data Type | The platform you choose should be appropriate for the research design and the type of data being collected. For example, if the research involves collecting numerical data, a survey tool with built-in statistical analysis features may be the best choice. Alternatively, if the research involves collecting audio or video data, like a video diary study, a tool that can handle multimedia files may be more appropriate. |
| Data Security and Privacy | For you research participants, data security and privacy is paramount. The platform should be secure and protect participant privacy. This is especially important when collecting sensitive data, such as health information or personal identification data. The tool should comply with relevant data protection laws and regulations, susch as GDPR. |
| Data Quality Control | The data collection tool should have built-in quality control features, such as validation rules or error checks, to help ensure that data is accurate and complete. The tool should also provide options for data cleaning and manipulation, such as data filtering or recoding. |
| Cost and Availability | The data collection tool should be affordable and accessible. Researchers should consider the cost of the tool, as well as any associated costs for training, maintenance, or support. Additionally, researchers should consider whether the tool is widely available and supported by the research community. |
| Knowledge Management, Transfer, and Collaboration | One of they benefits of a research ops team is the syngery it creates between various stakeholders. With this in mind, being able to share knowledge effectively and clearly across a number of stakeholder groups, is a critical component of both the dat acollection and the entire tool ecosystem. Equally, both researchers and designers for example, should have thge capability to collaborate effectively by leveraging on the accessibility and features of a tool. |
Effective Knowledge Management for Research Ops
Onboarding a tool for knowledge management is not enough to ensure effective knowledge transfer in research operations. It's essential to evaluate where knowledge currently exists in the business and how it's being referred to. Simply moving data to a new tool without considering its usability may not be effective in the long term.
Adopting a knowledge management tool or investing in a tool with strong knowledge management capabilities in research operations can be challenging, particularly when it comes to extracting specific data. Most tools have a built-in model for making sense of the data, which can limit the flexibility of the research process.
Flexible Data Repository and Insights
Many research tools operate on the premise of atomic insights or nuggets of information, which can restrict the information architecture and force researchers to work in a specific way. While this approach may work for some contexts, it doesn't suit all knowledge management needs. It's essential to interrogate the tools and the underlying mental models to ensure that they align with the research process.
Additionally, many tools have a similar information model as it's the easiest way to structure them on the backend. Some predefined information models can be restrictive for researchers during their analysis phase following fieldwork. An optimal tool should provide team members with the flexibility to interrogate and structure data in different formats.
In a nutshell, a research ops team should be able to dip in and out of a central repository of critical data needed to inform the wider business and stakeholders.
Let us support your next research project
If the above approach resonates with your research objectives, please get in touch and we can set up a call with one of our Strategists to discuss your own specific requirements.
Indeemo is available as a self service platform under annual licence. By combining our strategic advice with a self service SaaS platform, we give you the ability to benefit from our expertise while being completely autonomous and agile.
As the rate of change continues to increase, the need to stay both constantly connected with your users and customers AND be able to assign tasks / activities / questions at speed is paramount. Indeemo allows you to achieve both of these objectives in a way that is intuitive and personal enabling you to truly build a deeper connection with your target audience and their ever changing needs.
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