When Doordash did an IPO in Dec. 2020, the food delivery company was valued at more than 70 Billion USD, more than the combined market capitalization of Domino’s Pizza and Chipotle.
Investors believed that Doordash could innovate much faster because it had data driven decision making. Let’s peel the layers to see why a traditional enterprise is hamstrung competing with a young startup.
Limited access to Insights
Every organization would like more decisions that are based on data. If a store manager had access to a predictive forecasting model output every day, she would be much more efficient in inventory planning, staffing etc. Furthermore, she could automate the restocking of cola (a low-impact decision) and focus on higher impact decisions such as upselling.
In practice, the required data and analytics is not accessible to a large majority of people in the way, shape and form that can be useful. A very few people have access to analytics.
Manual processes
What stops these enterprises from making data driven insights accessible to all of its employees? The reason is that the process of building insights is very manual. A great majority of analysis are diagnostic dashboards built by running batch jobs on data. The predictive analytics teams serve higher level strategic decisions for which the impact is in quarters or even years, not days and weeks.
Data silos
Why are processes slow and manual? Because the data exists in silos. I do not mean just the physical location. I mean siloed use cases. In the pre-cloud era, organizations built different systems for different use cases. For e.g. there is one system for online transaction processing (OLTP) and another for analytics warehouse (OLAP). Similarly, there are different systems for Streaming Vs. Batch analytics, for fast and cheap vs. secure and accessible data. This creates silos – across products, across clouds and across teams.
The cloud breaks down these data silos.
For example, in Google cloud, you can automate the process to ensure that all changes to the transaction systems (change data capture or CDC) are updated in the central warehouse, BigQuery. You can also seamlessly query from BigQuery, any data sitting in any other database in the cloud. The data silos between the systems are broken down.
The innovation leaders are going one level further. They are setting up data exchanges (called Analytics hub on google cloud) where subdivisions publish their data and other sub companies and vendors subscribe to it. This gives them the opportunity to include their vendor ecosystem to drive innovation.
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