How can you prepare for the future of R&D data analysis?

R&D data analysis is the process of collecting, organizing, exploring, and interpreting data to support research and development activities. It can help you generate insights, identify opportunities, solve problems, and optimize outcomes. However, as the volume, variety, and velocity of data increase, so do the challenges and opportunities for R&D data analysis. How can you prepare for the future of R&D data analysis? Here are some tips to help you stay ahead of the curve.

1 Learn new skills

R&D data analysis requires a combination of technical, analytical, and domain-specific skills. You need to be proficient in using various tools and methods to handle different types of data, such as structured, unstructured, or streaming data. You also need to be able to apply appropriate statistical, machine learning, or artificial intelligence techniques to extract meaningful information from data. Moreover, you need to have a solid understanding of the research and development context and objectives, as well as the ethical and legal implications of data analysis. To keep up with the evolving demands and trends of R&D data analysis, you should invest in continuous learning and development of your skills. You can do this by taking online courses, attending workshops, reading books and articles, or joining communities of practice.

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Phil Scinto

Senior Fellow at The Lubrizol Corporation
Supersaturated designs are a great first step to screen numerous features at relatively low cost. As a bonus, couple the analysis with prior knowledge gleaned from experts in the field. Following more focused research, it is important to test sensitivity, repeatability and reproducibility. Too much published research falls apart because this last step is neglected.
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2 Adopt agile practices

R&D data analysis is not a linear or static process. It is an iterative and dynamic process that involves constant experimentation, feedback, and improvement. To cope with the uncertainty and complexity of R&D data analysis, you should adopt agile practices that allow you to be flexible, responsive, and collaborative. For example, you can use Scrum, Kanban, or Lean methodologies to organize your data analysis projects into manageable sprints, tasks, and deliverables. You can also use tools like Jira, Trello, or Asana to track your progress and communicate with your team and stakeholders. Furthermore, you can use agile principles like MVP (minimum viable product), fail fast, and test-driven development to validate your assumptions and hypotheses quickly and efficiently.

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3 Embrace automation

R&D data analysis can be time-consuming and labor-intensive, especially when dealing with large and complex data sets. To save time and resources, you should embrace automation as much as possible. Automation can help you perform repetitive and tedious tasks, such as data cleaning, preprocessing, or visualization, faster and more accurately. It can also help you automate more advanced tasks, such as data mining, feature engineering, or model selection, using tools like AutoML or AutoAI. By automating some aspects of your data analysis workflow, you can free up more time and energy for creative and critical thinking, as well as focus on the business value and impact of your data analysis results.

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Eric Line

Senior Traffic Safety Analyst, Motorcyclist Safety Representative at Michigan Dept. of Transportation (retired). US-DOT Motorcyclist Advisory Council member. SMARTER Board of Director.
I few other tools are Power BI, SAS, and SPSS. Learning to write scripts or syntax to can help automate your process as well.
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4 Explore new sources

R&D data analysis is not limited to the data that you already have or collect. It can also benefit from exploring new sources of data that can enrich your understanding of the research and development problem or opportunity. For example, you can use external data sources, such as open data, web data, social media data, or sensor data, to complement your internal data sources. You can also use alternative data sources, such as text, images, audio, or video, to capture more dimensions and perspectives of your data. By exploring new sources of data, you can discover new patterns, trends, or insights that can enhance your R&D data analysis outcomes.

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5 Share your findings

R&D data analysis is not an isolated or one-way process. It is a collaborative and interactive process that involves sharing your findings and insights with others. Sharing your data analysis results can help you get feedback, validation, or improvement suggestions from your team, stakeholders, or peers. It can also help you communicate the value and impact of your data analysis to your target audience, such as customers, investors, or policymakers. To share your findings effectively, you should use clear and concise language, as well as compelling and engaging visuals, such as charts, graphs, dashboards, or stories. You should also use tools like GitHub, Google Colab, or Tableau to publish and distribute your data analysis outputs.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Terry Clapp

Independent consultant: technical business development, from lab to product at Ex: Cambridge University
R&D data analysis will remain a skilled task. Machine learning and so-called AI are only ever capable of inference, understanding and the task of hypothesis building is required to plan the next round of research to build proof of strengthening theoretical framework.
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How can you prepare for the future of R&D data analysis?

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1 Learn new skills

2 Adopt agile practices

3 Embrace automation

4 Explore new sources

5 Share your findings

6 Here’s what else to consider

Research and Development (R&D)

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