Data Visualisation & Dashboards

Data visualisations, what can you do with it?

Make the value of data visible to the business. Data Scientists and Data Analysts enthusiastically get to work to get value from the available data, but how do you make the insights from data clear to the business? In such a way that the business can make decisions based on it? Which chart do you use for what? And what tooling fits with that?

All relevant questions when it comes to making the value of your data and/or prediction model clear.

The art of effective dashboards

Having the most important information that is needed to achieve the goals available at a glance, that is what a dashboard should offer. This gives you the possibility to actively and quickly adjust at every level in the organisation.

Features

  • Bring all your data sources together
  • Understand your data at a glance
  • Active and quick adjustments

The dashboard is supposed to be simple. The dashboard must be able to display problem areas, not analyse them. Turning data-driven insights into actions.

Our solutions

Visualisations with model plot[r/py]

With R and Python, it is possible to make very good prediction models. Of course, properly communicating these models to your colleagues in the business is another challenge. Below is a great example of converting these languages into visualisations and letting the power of your prediction model speak.

Visualisaties-modelplot-r-py-Broad-Horizon
Visualisaties-omgevingsdata-Broad-Horizon

Visualising environmental data to determine potential audience reach

An example of how you can use polygon plotting in R to provide insight into, for example, public data on a map of a certain area. By providing insight into the data on the map, you quickly get a picture of existing opportunities.