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Before we can begin to understand data, we need to define it. Essentially, data is information in raw or unorganized form such as letters, numbers, symbols that refer to, or represent conditions, ideas, or objects.
Data is potentially limitless and it is important to recognize the challenges this poses. Here are some of the core challenges related to data.
Let’s think about some of the more common key misconceptions when thinking about your data.
Misconception 1: You need lots and lots of data to do analysis.
This comes back to statistics. A larger dataset means more meaningful results. Sometimes having a massive pool of datasets actually can be more complex and confusing, and the common phrase ‘analysis paralysis’ can prove true. So think about that and how you might not necessarily need to focus on massive datasets.
Historical data may prove to be less valuable upon further reflection. Relying on old data may not be the best predictor of the way things are going to happen in the future. Be wary of this when evaluating your data.
Correlation basically means that, when one thing happens, another thing also tends to happen. So the two things are correlated together. The problem with that is that it can be difficult to discern why the second thing happened. For example, if you make a change and customers respond, are they responding because of the change you made or because of something else in the customers’ psyche. When analyzing data, make sure you don’t make assumptions about causation.
This occurs when you try to look at data through too narrow a lens. Understanding context is essential to make sure you’ve got the wider, holistic picture around what the data is saying.
In fact, they are completely different. Be careful about that because you can experience different results if you just look at percentage against percentage points.
This can occur when you have large sets of data, and people try to understand what is the average of what that data is telling you. The problem with average or median is that you don’t truly understand what’s happening in the various data pools so you might miss out on vital insights or you might miss out on what the data is really telling you. However, if the standard deviation from the median is very, very narrow, then the average becomes more reliable.
These are just a few key misconceptions to watch out for to make sure that you don’t fall into data traps when analyzing your data. Standard deviation is a great way to overcome some of those core biases that you might come across when analyzing data.Back to Top
Jack Preston is a Data Scientist working within marketing analytics, with a particular focus on strategic customer loyalty. Jack has experience working in both small-scale startups and large corporates, including dunnhumby and Notonthehighstreet. He also holds an MSc in Business Analytics from UCL where he graduated with distinction.
ABOUT THIS DIGITAL MARKETING MODULE
This short course covers the principles of analytics and demonstrates techniques and useful tools that you can use to develop and refine your knowledge of data analytics.
You will learn:
Approximate learning time: 3 hours
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