Data 101: Basics for Beginners
With the advent of new technologies such as generative AI, data has come to the forefront of discussions ranging from the boardroom to the classroom. Many unfamiliar with the concept of data can find this to be daunting. Terms like data, Python, analytics, forecasting, and large language models can appear like a blur of mercurial terms that are difficult to fathom. For many, data is a bit of a black box. The truth is, data is involved in nearly every facet of our everyday lives. It influences the advertising we see all around us, the prices you pay for your groceries, and the recommendations you receive browsing through your favourite streaming app.
So What is Data?
At its core, data is information. In fact, the Oxford Dictionary defines data as “facts or information, especially when examined and used to find out things or to make decisions.”
Effectively, data is the raw material that fuels our digital world. From your phone or smartwatch recording the number of steps you walked today to the tweets you scrolled through, data encapsulates details about our world and our interactions within it. A really important point to bear in mind is that data does not mean numbers alone. Words, dates, images, symbols, and sounds can all be treated as data when we use them to capture information about the world around us. Looking at things this way, you realise that nearly everything around us has a data dimension to it.
Types of Data
As you may have already gathered, data can come in all kinds of shapes, sizes, and formats. You may be asking yourself, “then how do I make sense of any of it?” Well, in order to do that, we have to consider how we categorise the types of data we have based on specific features and the way in which the information has been gathered or organised.
Based on its features, data is typically bucketed into two groups; quantitative and qualitative. Similarly, depending on how the information is organised, data can be labeled as structured or unstructured. Intuition might tell you that quantitative data is usually structured and qualitative data is probably unstructured, and for the most part, you would be right to think that.
Quantitative vs Qualitative
- Quantitative Data: This is typically found in number format, like your height, the temperature, or your speed while driving.
- Qualitative Data: This is typically descriptive and can include things like the genre of your favourite podcast, your feelings about a movie, or audio recordings of a conversation.
Structured vs Unstructured
- Structured Data: This is information that is neatly organised in tables or databases, like Excel spreadsheets.
- Unstructured Data: This is information that is ‘messy’ and not easily organised, like social media posts, or video content.
Why is Data Important?
You now have the basics of what data is. So naturally, your next question might be, “so what?” or “why should I care?” The truth is that data is all around us and has become a key pillar of the society we live in today. At its core, data helps us make better decisions about things. By looking at the data we have captured and learning from it, we can use information from the past to inform future decisions. Businesses use data to understand consumer behaviour, scientists use data to make discoveries, and governments use data to develop policies. Having said that, bear in mind that data does not make the decision. That is something left to people. Ultimately, data is used to inform the decision that an individual makes. This is often referred to as data-driven decision making.
How do I use Data?
Data can help you make better decisions, but a few steps are needed to get there. To make sure that you have the right information provided to you in the right way, you need to take a couple of steps. These can be broken down into collection, analysis, and visualisation:
- Collection: You can gather data through a range of methods. This can range from simple methods like surveys and observations to more complex ones like sensors or web scraping (i.e., capturing information from the internet automatically).
- Analysis: Once you have gathered your data, you need to analyse it. This usually involves a bit of cleaning (removing inaccurate information or duplications) and then you move on to organising the information you have collected and examining it to find patterns, trends, or insights.
- Visualisation: Arguably one of the most exciting things you can do with data is visualise it. Turning data into graphs, charts, or interactive dashboards makes it easier to understand and communicate what your data is telling you.
Data Privacy and Protection
With great data comes great responsibility. Depending on the information you have gathered, you need to consider how it is used and protected. As an example, you should not be gathering personal information belonging to someone without their consent, and many countries now have laws and regulations, like the GDPR, to make sure this happens. Where you are gathering personal information or data that you think might be sensitive to a business or individual, you also need to make sure that it’s properly protected, especially where data breaches are common.
Conclusion
Hopefully, you should now have a good understanding of the basics of data. There is plenty more to learn about data, analysis, visualisations, and all kinds of other topics that go around it. Remember to take your time and be patient as you continue to explore what data has to offer and how you can use it. If it feels like you are trying to learn a new language — that’s ok, because it is the language of the digital world. Ultimately, data is about telling stories and understanding the narratives hidden in the information we use to make better decisions.
Stay tuned for our next post where we’ll dive deeper into the world of data. Until then, keep decoding!
