Solving for Retail — Google’s innovations in Data & Artificial Intelligence

Kartik Trasi
4 min readMay 30, 2021
Solving for retail using data and artificial intelligence (Photo by Allan So from Pexels)

The pandemic has resulted in a radical shift in consumer behavior. According to Forbes [1], several trends have emerged: growth in online shopping, increased demand for essentials and desire to support local businesses. Retailers across the globe have not only had to keep up with the imminent demand but also have to adapt to, as McKinsey calls it - the “next normal” [2]. Whether it is redefining the online shopping experience or reimagining the stores for the future, it is more important than ever to leverage your most important asset — data. Although consumers have come to expect personalized experiences from their trusted brands, they expect their data to be handled responsibly and securely. This presents a unique set of challenges and opportunities for the retail industry.

In this article, I wanted to share some of the innovations that were announced at Google I/O 2021 that will empower retailers to unlock the true potential data and a provide a unified platform to leverage artificial intelligence (AI) to build meaningful consumer experiences.

Unlocking the value of data across the enterprise

Consumer journey has become increasingly complex from product discovery, brand research and to final purchase; each touch point being a potential source of valuable insights for the business. However, the data is only valuable if the data is easily discoverable and accessible for analytics for various parts of the business. Data often starts with a couple of use cases in mind, and ends up becoming useful to a growing number of people and tools within the business. For example: the data that can be used to optimize the online journey by the ecommerce team can also be used for personalization by the marketing teams and to determine future product offerings by the product research teams within the business. Often, organizations are forced to make multiple copies of the data leading to lack of control, governance and monitoring.

To address this, Google launched Dataplex — an intelligent data fabric that enables organizations to centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts while allowing the business the flexibility to choose the best analytics tools — open source or cloud native whilst enabling consistent security policies, governance, and data classification across data services.

Building a secure data ecosystem

The surge in demand during the pandemic has posed significant challenges for supply chains and operations for retailers globally which has resulted in new partnerships with vendors and suppliers. Nurturing long term partnerships and building trust requires transparency across organizations. As consumers choose to support local business, retailers need to increasingly work with local vendors to stock their product in stores and online. Demand and stock levels could serve valuable insights to local vendors to ensure product is available to consumers in a timely manner. Accuracy of inventory data serves as a key differentiator for the retailers and can help optimize availability and cost. Till date, sharing data beyond the enterprise securely and at scale was challenging.

To solve for this challenge, Google announced Analytics Hub which allows businesses to publish datasets securely across silos in a secure manner. Analytics Hub is a fully-managed service built on BigQuery (Google’s serverless, highly scalable, cost-effective data warehouse designed for business agility) that allows businesses to efficiently and securely create data sharing ecosystems with governance in mind. Retailers can leverage Analytics Hub to access and share valuable datasets and analytics assets like machine learning models, data quality recipes and visualization dashboards across any organizational boundary.

Reimagining the future of consumer experience

With focus on safety and shift towards ecommerce, retailers need to redefine the online experience to make it relevant and meaningful; and reimagine the in store experience making it more immersive and safe for shoppers. This requires retailers to leverage machine learning and artificial intelligence to build differentiated experiences. Today, businesses have teams of data analysts / scientists with widely varied skill sets and levels of expertise. The variety of tools for machine learning makes it challenging for teams to manually piece together machine learning point solutions which increases the effort required for model development and experimentation, resulting in very few models making it into production.

To address this, Google launched Vertex AI — a managed machine learning platform that allows businesses to accelerate the deployment and maintenance of artificial intelligence models. Vertex AI enables data scientists and ML engineers across all levels of expertise the ability to implement Machine Learning Operations (MLOps) to efficiently build and manage ML projects throughout the entire development lifecycle. A unified platform for building, training, and deploying machine learning models allows data science teams to prototype, experiment, deploy models, interpret models, and monitor them in production at scale within a single environment.

Hopefully, by now you are excited as I am around Google’s innovation and advancements in machine learning and artificial intelligence. You can read more about these announcements (and more) at the official Google I/O 2021 blog.


  1. Changes In Consumer Behavior Brought On By The Pandemic (Forbes, 2021)
  2. The Next Normal — The future of shopping: Technology everywhere | The Next Normal (McKinsey, 2021)
  3. COVID-19 Maintaining customer loyalty and trust during times of uncertainty (Deloitte, 2020)
  4. Top 10 AI use cases in retail (Google Cloud, 2020)
  5. Google Cloud launches from Google I/O (Google, 2021)

(Disclaimer: Opinions presented here are my own and not the views of my employer)



Kartik Trasi

Googler. Love solving real world problems using data and artificial intelligence. (Disclaimer: Opinions presented here are my own)