The Data Analysis Process: Explained
Updated: May 25
When confronted with bucket loads of data, it can feel confusing to know where to begin with processing all that data and understanding what it is that you can take away from it.
We’re here to help. This guide will help you to understand the basic steps you need to take to get from data to insight with ease. It’s not as hard as it sounds!
The process itself can be made pretty straightforward by following the steps below:
In a nutshell, you take your basic data, spruce it up a bit, and spot the patterns that appear. By basic data, we mean the spreadsheets or CSV files that you have input information into, like attendee records or mailing lists. The sprucing up bit can take some time, as it involves a number of steps, but we’ve explained each of them below to make your day a little bit simpler.
So, let’s start by downloading your data from LinkedIn and Facebook or Mailchimp and Eventbrite, if you haven’t already. Then, you can save your spreadsheet as a CSV file to make it a bit easier for your computer to read. That’s your input (the data you will carry through to the next steps).
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This is the first step to getting your data ready for analysis. This step is important because if it's missed out your analysis may end up muddled and not quite as you need it to be.
The easiest way to think about data analysis is that you are taking pieces of information, each slightly different from the other, and telling your computer that they actually mean the same thing.
The same goes for empty columns and rows, as well as extra spaces before and after words. You need consistency in your data so that your computer can summarise your data more efficiently, and it will stop you from having to needlessly scroll through your data to find what you need.
Start by using our tool to remove extra spaces, columns and rows. This can make your data much easier to navigate!
It’s much harder to understand your data when it is spread across multiple files. Following this step will ensure that all of your data is together, all in one place. You can do this by merging your separate spreadsheets in all together in one table.
This is important because it means that when you come to analyse your data, you will be able to check for patterns across the entire data set, rather than just separate parts. This is particularly handy if you want to cross reference performance reports, mailing lists, or attendee lists.
Cleaning your data and removing inconsistencies is one of the most important things you need to do. It’s the spring cleaning of your spreadsheets; an important step that helps to organise and structure your data in a way that avoids problems later on in the process.
Pop your document into our data cleaning tool to remove any pesky typos and errors, and group similar values together. This extra nudge toward standardisation will really help you out in the long run - with better analysis and more focused insights as a result.
For example, if your spreadsheet contains the job title Chief Executive Officer, it may be written as CEO, ceo, C.E.O., or even Chief Exec. Your computer won’t know, like we do, that these are all the exact same role. So, if you are tallying the number of CEOs on your mailing list, you need them to all appear in one place.
This is important because it means that when you come to analyse your data, you will be able to check for patterns across the entire data set, rather than just separate parts. This is particularly handy if you want to cross reference performance reports, mailing lists, or even just create a big master list of attendees from all your events.
The next step involves adding in extra information that will help to make your analysis a bit easier. This can be anything from the LinkedIn profiles of your event attendees to the seniority of your staff.
Adding extra information is always handy when it comes to analysis; you’ll have more ways to connect and compare your data than with just your initial data sets.
Some popular additions include: gender enrichment, company enrichment, and country enrichment. We can help you to decode country codes, add company biographies, and predict the gender of people across your spreadsheets within minutes!
Analysis involves summarising your data to give you a clear, easy-to-read overview of what is happening in each of your columns. This step brings together all of those little adjustments from above into a simplified and more accurate picture of your data.
Auto analysing your data is just asking your computer to calculate how many times a certain number or text appears in each column. By doing this step, you can find stand out information, connect important data points, and spot interesting patterns.
From there you can easily navigate your data to develop strategic insights for your workplace!
If you’re interested in using AI to work through your tasks at hyper-speed, check out our selection of low-code and no-code tools now:
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