Data analysis is incredibly useful for all kinds of businesses and also has academic and personal applications.

However, it’s easy to get data analysis wrong: there are many potential pitfalls for unwary analysts to fall into. Here at, we transfer your data into our ELK stack for you. What’s more, our stack uses Kibana visualisation software so that you can view and understand your data more easily.

When you use our hosted ELK solution for data analysis, you won’t have any software-related problems or comprehension issues.

Nonetheless, it’s still possible to fall into numerous traps when trying to actually interpret your data. That’s why we’re giving you a list of three common data analysis mistakes to avoid at all costs.

1. Assuming that every correlation is meaningful

When two sets of data seem to show an interesting correlation, it is tempting to assume that one is impacting the other. In fact, this is often the case, and the science of data analysis depends on spotting meaningful correlations. However, not all correlations are meaningful: some are simple coincidences and shouldn’t be allowed to influence your business strategy.

To demonstrate, let’s look at two hypothetical correlations. Firstly, imagine you own a retail business and one of the products you sell is sunglasses. You might look at your data and see that you sell more pair of sunglasses during the morning and evening than in the middle of the day. This may be a meaningful correlation, because the position of the sun in the sky could be prompting people to buy sunglasses to protect their eyes. Now imagine that you looked at your data and saw you sold more sets of sunglasses on Tuesdays than any other day during a particular month. This correlation has no rational explanation and is therefore likely to be coincidental. It is important to learn to spot the difference between meaningful and coincidental correlations.

2. Drawing conclusions from insufficient data

One of the easiest mistakes to make when carrying out data analysis is to draw conclusions based on very limited amounts of data. If you haven’t been gathering data for very long, you may be tempted to try and extract meaningful conclusions using the small amount of data you have at your disposal. However, this can lead to incorrect conclusions: always make sure you have a reasonable pool of data to work with before trying to interpret it.

3. Not updating conclusions

Once you have reached a conclusion based on the available data, it’s easy to assume that the conclusion will hold true indefinitely. However, this is not the case. For example, if you run a business, the behaviour of your market sector and your customers may change over time, invalidating old conclusions. It’s therefore important to regularly check your conclusions against current data to ensure they remain accurate.

Here at, our ELK solution uses Elasticsearch 2, Logstash 2 and Kibana 4.2 to make data analysis as easy and intuitive as possible. If you can avoid the mistakes we’ve listed here, you will find it easy to interpret your data meaningfully and reach significant, up to date conclusions.