- Analytics


Data analytics is about extracting information from data and thus reducing it to a few, yet significant, parameters.

Using data analytics, you can gain insight into the hidden information contained in large amounts of data. As a result of this new knowledge, one may be able to improve one's decision-making process, plan a strategic approach, or set up monitoring which may alert one if something is not on track.

Analyzing data can be automated so that it is completely automatic. Therefore, the key figures will always be up-to-date and the monitoring will be "live".

Requirements for Data analytics

Data analysis requires that you have some data to analyze. In addition, there are a number of requirements for similar considerations in relation to the project, which must be met before beginning a data analysis.

  • Proper data quality
  • Objectives of the analysis
  • Scope with the analysis


The results of a data analysis will depend on how accurate or incomplete your data is. Of course, it will be best if the data is without errors and does not contain any "holes", but if this cannot be done, it will be necessary to test the data before you can determine if it is good enough to extract information from.


You should ideally know what you are looking for before you begin an analysis. Similar to building a house, you cannot simply tell the craftsmen, "Build me a house", as in many cases, you will be dissatisfied with the end result. You should specify where the house will be built, how many rooms it should have, etc. It is therefore a good idea to have defined what it is that you are interested in before you begin.


The use of the analysis also needs to be considered. Is the analysis isolated to the point that a report must be prepared, or is it ongoing? This is especially true in relation to infrastructure, since it is better to construct everything correctly from the start rather than to add fixes later.

Technical possibilities regarding data analytics

A data analysis can be used to find out how similar data points are to each other, or to analyze how much different data points depend on certain factors.

Depending on the goals you set for the data analysis, different techniques will be most appropriate for different calculations.

The process regarding data analytics

Q-analytics process when we do a data analysis is:

  • Initial meeting
  • Workshop
  • Data access
  • The analysis phase
  • Result

Initial meeting

As part of the introductory meeting, we will discuss the scope of the analysis, what goals you have, and how you envision your ideal solutions. In addition, we will suggest ways to turn your idea into a reality. 1-2 hours duration.


Workshops are used to determine what kind of data you have and how good the quality is. Additionally, we will discuss potential analytical methods that could be used, as well as decide what the final result should be. 2-4 hours duration

Data access

During this phase, access is granted to the data. It is possible that we receive the data and analyze it in our office. We can also perform the analysis locally with you if the data is extremely sensitive. The length of time this takes depends on the data we need to access and how we will access it.

The analysis phase

At this point, we analyze and set up your final product. It usually takes between 1-2 weeks, depending on the scope and the goals that have been set.


You will receive the final product, in which we will present the results and explain our reasoning. This takes approx. 1-2 hours, plus time for handover if a pipeline is to be built.