Skillsoft Blog

I Got The Data Results- Now What Do I Do!?

August 10, 2020 | by Alessandra Abelli

Data. You might love it, hate it, or find it utterly confusing or even downright terrifying. It can be intimidating and difficult to synthesize and analyze, yet it is an extremely powerful tool to support one’s arguments and recommendations in countless professions — from business to law to medicine. I believe that becoming comfortable with synthesizing data and using it to your advantage when making recommendations and/or arguments is an attainable and invaluable skill. If you struggle with statistics and data analysis, you’re certainly not alone. In a study conducted by The University of Regensburg in Germany, only 4% of participants could solve reasoning problems in probability format, and only 24% of participants could solve reasoning problems in natural frequency format.

As a psychology and marketing student, statistics and data analysis has been an essential part of my coursework. I can empathize with those who find such material to be difficult, as statistical equations and programs such as R were meticulous and not always easy to learn and are not always easy to use. This summer, I have been given the opportunity to enhance my critical thinking and analytical skills working at Skillsoft and C Space. I have been involved in projects involving Salesforce and KPIs and fielded customer research surveys with subsequent reporting and analysis utilizing tools such as Excel. These projects have required me to build reports and/or recommendations from a vast array of numbers and metrics. Don’t get me wrong — looking at an endless set of numbers is intimidating at first. However, you — yes, you — can make data and statistics your friend and not your foe by implementing certain techniques and strategies and following certain guidelines when analyzing research.

Below, I listed some tips regarding how to effectively go about analyzing and reporting quantitative data that I have found to be helpful and address several common pitfalls in the process.

Step 1: As soon as you first look over your results, clean up your data.

This helps to narrow down what is most likely already a large and intimidating amount of information. You’ll first want to get rid of any outliers, or any data that just doesn’t make sense (i.e. are there negative values when that isn’t possible?). You’ll also want to get rid of any invalid responses. If you fielded a survey, for example, was the participant clearly not paying attention? Did they skip questions?

Step 2: Take what — and only what — you need.

Are you looking to analyze one specific aspect of the results? If you’re in Excel, I would highly recommend making a separate worksheet with only what is relevant to what you are investigating. Look at your data with your specific question(s) in mind.

Step 3: Analyze, analyze, analyze!

I like to run several data analysis methods. Descriptive statistics, such as mean, median, mode, frequency, and range, can be used to summarize data sets. Inferential statistics, such as correlation, regression, and analyses of variance, are more complex and can be used to show relationships between multiple variables, including identifying what differences are statistically significant, and can be used to make predictions.

Step 4: Keep an eye out for data that goes against your hypothesis.

Humans are naturally inclined to seek out information that only confirms their biases, known as confirmation bias. To combat this, make sure you look out for data that contradicts your hypotheses too. This is not only fair to yourself as a means of creating a good report, it’s a component of research ethics.

Step 5: Make useful visual tools.

Visual tools such as bar graphs, plots, and pie charts can help simplify whatever you are looking at and can be helpful in drawing conclusions. If you don’t know how to make these in Excel, I would highly suggest utilizing online learning tools to your advantage. For example, Percipio offers courses in Excel. However, make sure that such visuals aren’t doing more harm than good. For example, certain ranges on axes can be misleading and can make results seem either bigger or smaller than they truly are.

Step 6: For your report, focus on concision and clarity.

While the nature and format of your report will differ depend on what your task is, your writing should always be concise and you should only include the relevant metrics/tables. You made an aesthetically pleasing bar graph? Awesome! But if that bar graph isn’t relevant to anything in the report, all it will do is confuse the reader. While proofreading, I always ensure that each is adequately explained and linked to my key findings and recommendations. You should also aim for clarity both in the research method and in your recommendations.

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