Prologis at a glance
- HQ: San Francisco, CA
- Industry: Logistics Real Estate
- Ticker: PLD (NYSE)
- Continents: 4
- Countries: 19
- Use Cases: Data Discovery, Sel-service analytics
- Challenge: Need for a centralized platform that could enable anyone in the organization to explore data and start thinking about how it could get their business questions answered
- Solution: data.world is powered by a knowledge graph, making it easy to establish relationships between data elements, business elements, and decision elements.
Luke Slotwinsky is the VP of Data and Analytics for Prologis, the world’s largest logistics real estate company operating across four continents and 19 countries. He says the future is changing very quickly, and historical views of company data were not going to be enough to set them up for the next five years of success and visibility.
So Luke and his team embarked on a large digital transformation effort to take their historical data warehouse and move it into the cloud.
“Having data that was well organized, really clearly explained and available for self-service were the key drivers that we started making our decisions on both from an architecture perspective, but as well as a product perspective. We wanted to model our data to represent who we are in what we do. So for example, we’re an industrial real estate company. Our top data domains are our customer, building, unit lease financial metrics.”
Prologis wanted a centralized platform that could enable anyone in the organization to explore the data and start thinking about how their business questions could be answered with this dataset. Luke wanted a tool that not only did a data catalog function, but established a data collaboration platform, where if he had a business question, he might not know exactly where to look or what data would be needed but he would have a platform where the meanings of the data elements were listed, and he could start asking my questions in that platform.
One of the primary focuses that Prologis has going forward is to really increase the use of AI/ML to drive the business forward.
“We’re focusing right back into the data catalog, whether you’re doing traditional analytics, exploring data sets for descriptive type of use cases, or you’re starting to look at predictive or prescriptive use cases, we want to bring the knowledge into one platform that can start to bring together these different types of advanced analytics use cases into one platform that’s easy to understand and consolidated.”
Luke says that working with the data.world team has been an absolutely amazing experience.