As we continue to reduce the cost of research and development (R&D), the Global Research Institute outlines the most important steps to interpreting data. The results of this process will determine the overall success of your project, so it’s important to get this part right.
Equally important, however, is to do it as efficiently as possible, or else waste precious time wading through your data.
A survey by Australian machine intelligence company, Appen, found that almost three-quarters of data scientists spend at least one quarter of their time managing, cleaning, and/or labelling data.
This highlights the need to get the review process as streamlined as possible. After all, time is money and data scientist salaries aren’t cheap.
Once you’ve perfected your review process, make sure to contact us to learn more about claiming the R&D project on tax.
Basic Principles
Before we outline each individual step, it’s important to consider these overarching principles throughout the process. With these in mind, your review will be guided and thoughtful, saving you and your audience time by removing any unnecessary data or interpretations.
Connect everything back to your research goals at all stages of R&D because a misguided research paper will appear messy, lacking consideration, and overall unhelpful. Only take tangents that aid to your main point and ensure each piece of data is well explained and not included for the sake of it.
Consider your audience at each step or else risk losing their attention and potentially their investment in future projects. Whether or not you satisfy your audience will largely depend on how relevant your goals are to them – for example, increasing revenue from a product range – but each piece of discussion can also be used to relate back to their interests.
Develop a list of questions to interrogate the data and you will find each stage of the review process to be far more guided and consistent. So long as each point you make answers one of these questions, you can be sure of an effective review process.
Your questions will be specific to your project, but some broad examples might include: are the qualitative and quantitative data aligned? Is the new data highly significant, moderately so, or insignificant? Or, would we find similar data from a different research method?
With these principles under your belt, follow the below steps to ensure a smooth review process in your next R&D project.
Organise and Manipulate Data to Find Trends
You’ll have no hope drawing helpful insights from your research if it’s not organised into a suitable format. The first step is to determine what format your research will be best suited to.
Consider starting by separating findings into quantitative and qualitative data before diving into each of these sections. The former will likely fall into tables and graphs from which trends can be recognised. But there may be multiple ways to organise this data so consider trying all kinds of tables and graphs before seeing what can be learned from each.
Qualitative data can be sorted into themes and categories from which a narrative can be deciphered to align with your predetermined goals and audience.
Some of the negative connotations associated with “manipulation” don’t apply to data manipulation. It will be important to bend the data in a way that supports your argument while disproving alternative ideas. Of course, this can be disclosed and acknowledged in your discussion section(s), but your ultimate aim is to use the data to forge an argument which supports your hypothesis.
Explain All Data in the Text
To reiterate, make sure to avoid including data for the sake of it. If there is a table in your paper which you think is self-explanatory or is open to interpretation, avoid that mindset and explain the data yourself. However obvious a piece of data might seem to you, make sure to reference it and explain its relevance to the hypothesis and aim of the project.
Just because it’s obvious to you, doesn’t mean it will be to your audience who probably don’t specialise in data analysis.
If you don’t think a piece of data is worth explaining then it’s probably not worth including in your paper.
Equal Parts Highlighting Data and Interpreting the Meaning
Following the previous point, it’s important to dedicate equal amounts of time to highlighting/explaining the meaning of a datapoint, and to interpreting the meaning. The difference between the two cannot be misunderstood.
Highlighting and explaining the data involves fundamentally defining what a graph or table depicts. Interpreting it yourself then involves relating it back to your aim and audience, making it relevant to the paper as a whole and qualifying why it was important to include.
Conclude – Did You Achieve Your Goals?
Once all of your relevant data is exhausted and there are no more points to be made, wrap up your paper with a discussion of what’s been outlined and why it matters.
Recognise any shortcomings in your project for future study and highlight the importance of such projects to your company. If this isn’t accentuated, your audience may conclude that this project was the be-all and end-all of their research requirements, leaving people out of a job and the company stagnant.
Store All Commentary for Future Reference
If you do successfully convince your audience that further research is required, it will be important to have all of this handy information on file for later use. This includes anything you did or did not use in your final research paper. Just because some data was unused this time around doesn’t mean it won’t be relevant to your next project.
Contact the Global Research Institute
Once your project is wrapped up, there may be opportunities to claim some processes on tax. Get in touch with us to understand where your project can get some money back for its efforts, further incentivising future projects!
