Big Data and Insightful Analytics

Particle41 partnered with Arrivalist to help them create organizational intelligence tools through the addition of targeted software tools and data management.


We created a analytics dashboard that allowed advertisers to track conversions based on arrivals. Tourism Boards and Hoteliers were able to monitor the successfulness of their campaigns and determine when and where to spend their marketing dollars. This was accomplished by combining the latest cloud hosting and open-source software in order to create a bespoke solution that added to revenues through better servicing of existing clientele.

Most customers don’t RSVP. Your online marketing plan has been inviting people to visit you for years. Isn’t it about time you found out which messages-and which media outlets—are influencing people to show up? Arrivalist’s patent-pending technology anonymously measures change in the locations of network-enabled devices after a sequence of media exposures to tell marketers which messages influence customers to join their party.

Understanding the Data

It takes a lot of understanding to get data in the right shape so that you can use visualization as part of data analysis. For example, if the data comes from social media content, you need to know who the user is in a general sense – such as a customer using a particular set of products – and understand what it is you’re trying to visualize out of the data. Without some sort of context, visualization tools are likely to be of less value to the user.

Addressing Data Quality

Even if you can find and analyze data quickly and put it in the proper context for the audience that will be consuming the information, the value of data for decision-making purposes will be jeopardized if the data is not accurate or timely. This is a challenge with any data analysis, but when considering the volumes of information involved in big data projects, it becomes even more pronounced. Again, data visualization will only prove to be a valuable tool if the data quality is assured. To address this issue, companies need to have a data governance or information management process in place to ensure the data is clean. It’s always best to have a proactive method to address data quality issues so problems won’t arise later.

Displaying Meaningful Results

Plotting points on a graph for analysis becomes difficult when dealing with extremely large amounts of information or a variety of categories of information. For example, imagine you have 10 billion rows of retail SKU data that you’re trying to compare. The user trying to view 10 billion plots on the screen will have a hard time seeing so many data points.