Technologies like Artificial Intelligence and Machine Learning will enable wider audience to get access to space analytics and insights. Assisted processes through software created by Machine Learning will enable users to run sophisticated models on their data. That is what I see happening in the next few years. It’s not about replacing, it’s about assisting new types of users to get access to the type of analysis that wasn’t accessible before. Automated Machine Learning is what we are going to be seeing in the coming years. It will expand the customer/ user base of spatial analytics by making it more accessible.
Geospatially aware
We have been using technology for many years. In 2020, we will see the full explosion of Machine Learning and Artificial Intelligence for driving business processes. And that’s a real game changer for us and for our customers in terms of the type of solutions that we can and will be providing. It will also lead to better data and better analysis, and will democratize access. AI, in particular, will enable wider adoption of spatial analytics by simplifying the way organizations make those analysis.
The CARTO platform is made of different components. It is targeted towards three different types of users. The first type is data scientists, who are not easily available, especially the spatial data scientists. What CARTO does for such users is to increase performance and productivity by making the day-to-day analysis simpler and more accurate. That means our solutions work on Jupiter notebooks with Python —things data scientists are comfortable with.
Very often, these models need to be distributed to businesses, for which we have CARTO Builder that can help in the presentation of that analysis in the easiest possible way. Finally, in most organizations, once you have found a good analysis or a good model, you have to take it to production. This means putting in insights and applications, or an assistant that enables a wider audience within the organization. That is done by those who develop custom applications.
CARTO simplifies and reduces the time that it takes for that analysis to be put into custom applications that solve a specific business problem. Our vision for our customers and organization is to make them geospatially aware. That is always the key. There are a lot of organizations that have no traditionally geospatial analytics to solve the internal challenges. So, we have to help them to understand the power or geography and spatial analytics.
Also Read: There’s a bit of geo in everything
Primary users
Data scientists or analytics teams are our primary users. These teams are responsible for forming customer analysis within the organization. So, they will leverage CARTO to improve the performance and simplify the spatial analysis that they do for many different operations inside the organization. We also work directly with business users, where we know spatial analytics can help them. In those cases, they might not have the capabilities to do the analysis themselves and might not even have teams. We work with such business users to solve with a solution their end-to-end problems.
Top tech trends
On top of the list is Machine Learning, which has changed the way we solved spatial analytics. Second probably will be data warehousing and cloud computing capabilities for storage and computing separations. The new data warehouses are altering the business models. So, we don’t pay for the storage of the data, we only pay for analysis. And that fundamentally changes the business models behind a lot of spatial data infrastructure. Then there is the rise of analytics platforms or visualization platforms on the web that will have a big impact.
Earning trust
It is not possible to position yourselves on the wrong side of data privacy. We needed regulations in this industry on everything around data privacy. That started with GDPR and it’s a good thing. I am actually quite positive about the changes it is bringing to our industry, which will be respectful about how we treat privacy and ensure we do it in an ethical and legal manner, giving control to the consumer.
Data privacy should not be considered a big issue. Privacy in spatial data only concerns very few use cases within our industry, apart from targeted advertising. The latter is the only part that is heavily affected. 95% of the data that we have is not affected by privacy. That is something we have to make clear — it sounds like our whole industry is affected by privacy.
We make decisions to not work with personal information or data, which has privacy issues. When we work with data providers, we make sure the data is identified when it hits our servers. We don’t really touch it and that’s the level of trust that we bring to our customers. But we have to take responsibility for the things we can do with the data.
Also Read: Geospatial data for an interconnected world
