About the Client 

The Client is a globally recognized commercial real estate service company that aims to optimize the rental potential of their properties. They offer comprehensive support in property acquisition, construction, and investment across a wide range of real estate categories, encompassing residential, commercial, hotel, and industrial properties. Additionally, they specialize in managing approximately 400 million square meters of space within their facilities. Their services cater to a diverse range of customers.

Complication 

The existing solution faced limitations for effectively managing bookings and forecasting demand across all corporate locations for their tenants. With long-term rental contracts in place, the Client's objective is to accurately predict the demand for office space and its supply. 

Before Datumo embarked on the project, the Client's teams relied on Excel, which occasionally posed challenges for smooth collaboration across multiple teams worldwide. At times, working with such a wide range of files led to disorganization and difficulties in effective data management. Additionally, different countries often didn't use the same format, resulting in challenges in accurately tracking changes. Furthermore, data editing and upserting presented issues as the history of modifications was not easily accessible or transparent. As a result, planning capabilities were limited.  To address these challenges, the Client partnered with Datumo who built a robust, unified system based on BigQuery.

The Value We Delivered

• Centralized Data Management: Datumo's platform unifies and centralizes data, making further analysis and collaboration across multiple teams more efficient, as well as easier for the Client.
• Enhanced Planning Capabilities: The Client can now generate weekly reports instead of monthly ones, leading to improved forecast accuracy.
• Cost Reduction: The Client is able to minimize unoccupied office space and ensure that it is rented at an efficient price, resulting in cost savings.
• Automated Demand Planning Process: The implementation of Datumo's solution automates the demand planning process, freeing up valuable time that was previously dedicated to manual activities. This allows analysts to focus on more creative and strategic challenges.
• Education for Client's Teams: Datumo provides consulting services and shares best practices, empowering the Client's teams with valuable knowledge and expertise within technology and business organizations.

Innovative solutions and advanced technologies

The legacy system used Google Spreadsheets for manual planning of supply and demand. It was difficult to maintain data quality. Report generation was restricted to a monthly basis. The proposed solution overcomes this limitation through planned automation resulting in weekly reporting which improves planning capabilities. Our goal was to automate the process and keep the data in one central location. The data is populated into Google Spreadsheets and then transferred into BigQuery tables using Plex technology. Next, it is orchestrated with Cloud Composer. Stored Procedures (routines) are run on top of the data to keep the quality and mapping in line with master data. When the ETL process ends, partitioned BigQuery tables are visualized with Data Studio for further analytics and reporting purposes. BigQuery is also an important input to machine learning algorithms which run as Docker containers inside the Google Kubernetes Engine.

In addition to our core services, we offered advice and shared best practices in a variety of areas. One of our focus areas was introducing DevOps practices, emphasizing the usage of Terraform for resource management. We also provided guidance on implementing GitOps practices and streamlining releases. Our team assisted in orchestrating Cloud Composer and creating Directed Acyclic Graphs (DAGs) to automate complex workflows. Additionally, we ran quality checks on their data and promptly notified relevant teams in case of anomalies. Furthermore, we provided guidance on partitioning data in BigQuery to optimize storage and query performance.

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