The ELK solution that we offer here at is the perfect tool for storing and analysing large data sets, regardless of whether you’re a business-owner trying to understand market data, an app developer who wants to analyse their software’s performance or an academic who needs to analyse data to test a theory.

Elasticsearch, Logstash and Kibana are faster, more secure and have more features than their predecessors, while we ensure that your data makes it into the ELK stack successfully and is managed securely. However, even the most powerful data analysis solution is only as good as the data analyst using it. That’s why we’d like to use today’s blog to give you some tips that will make analysing data simpler and more effective.

1. Start with a solid hypothesis

It’s easier to see the significance in your data if you know what you are looking for. If you start off each session of data analysis with a theory that you would like to test, you can interpret the results of your analysis with reference to that theory, which makes it easier to see meaning in them. For example, if you run a business, you might start a data analysis session by hypothesising that there is an optimal price for a specific product that encourages maximum uptake by consumers. If you know that you are looking for an optimal price, you will be better equipped to spot it if there is one. Kibana 4.2’s handy visualisation features make it easier to spot significant correlations when looking at two or more types of data (‘price of product’ and ‘customer uptake’ in our example).

2. Don’t try to rationalise your data

While it’s always a good idea to look at your data with a solid hypothesis in mind, you should never try to rationalise your data to fit your hypothesis. Keep an open mind regarding what your data may tell you and don’t try to explain away anomalies or results that don’t fit your theory. This will prevent you reaching false conclusions that could have a detrimental effect on your business or project. It may also enable you to see significant patterns in your data that you did not expect to find.

3. Only use the data you need

For each session of data analysis, focus on looking at the data you need to test your hypothesis: feeding too much data into an analysis can produce very complex results that are hard to interpret. Work out what information you need beforehand, and then utilise it. There’s no harm in playing around with larger, more complex data sets when you’re not trying to test a specific hypothesis (in fact, doing so may show you something useful), but this should always be viewed as a side activity. When performing data analysis, your focus should always be on looking for specific answers using only the data you need.

Here at, we’re proud of our secure, flexible and efficient ELK solution. However, we also understand that successful data analysis is the result of the analyst, not just their tools. We hope the tips we’ve provided in today’s blog help you develop into a more focused, successful data analyst.