Editor’s note: We invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. This original blog post was published by OnlineSales.ai. Please enjoy this updated entry from our partner.

Tata’s online healthcare entity, Tata 1mg, aims to revolutionize how a consumer finds the right healthcare professional for their needs. The platform offers e-pharmacy services, diagnostics, and health content and has more than 200,000 active brands in its rapidly expanding list of suppliers, sellers and manufacturers. Several of these brands would need regular involvement from the marketplace team in order to set up and run ad campaigns. As one of the largest platforms, Tata 1mg approached OnlineSales.ai to address their growing needs for an efficient Retail Media Monetisation suite to help scale up their existing monetisation efforts. 

Tata 1mg needed more data-driven insights and customizability for their monetisation efforts

Tata 1mg saw several challenges that they needed to address.

1. Manual effort: Tata 1mg marketplace team’s monetization process was largely manual and required heavy time and resource investment to activate advertisers, which subsequently ate into their overall ad revenues.
2. Non-scalable framework: Given the fast-growing number of advertisers on their marketplace – well into the hundreds of thousands – it became increasingly clear that their existing monetization framework was not scalable.
3. Inadequate reporting & analytics: The existing framework facilitated only simple reporting, and brands were not able to draw the deep insights they needed to make data-led decisions and refine their advertising ROIs.
4. Steep learning curve: Several brands lacked dedicated advertising experts who could bring the experience and skills needed to plan successful ad campaigns and optimize budgets. The involved learning curve led to increased advertiser-churn.
5. Lack of personalized offerings: The manual nature of Tata 1mg’s monetization framework left little room for customizability, and account managers could offer very little in terms of tailored ad-buying experiences to different types of advertisers.

All of these issues led to regular revenue leakage. Tata 1mg realized the need for a central platform that addressed the above-mentioned issues and would also be able to cater to new ad channels and the requirements thereof. Developing such a solution in house was evaluated, but was found to be expensive and would require a lot of time to build.

OnlineSales.ai’s AI/ML-powered solution on Google Cloud offered the automation and flexibility to support modern omnichannel advertising needs

Therefore, Tata 1mg looked to OnlineSales.ai Monetize, an AI/ML-powered retail media monetisation suite on Google Cloud, to help address their needs for a cloud-based monetisation platform that offered the flexibility to support omnichannel advertising models, had automation built in to reduce manual tasks, provided detailed intelligence and analytics, and delivered quick, reliable scalability.

1. White labeled self-serve platform: OnlineSales.ai’s retail media platform was implemented within a short 4 weeks. After thorough testing and implementation, the platform was successfully launched, and enabled sellers to run ads on a fully self-serve platform. This required zero involvement from the Tata team.
2. Unified omni channel buying: The team at OnlineSales.ai worked closely with the platform’s core teams to activate and mobilize various advertising products through a self-serve platform, while simultaneously addressing any unique requirements they had.
3. Omnichannel analytics with AI-led suggestions: Sellers are now also able to generate detailed reports that provide them critical insights into campaigns, and help them make better use of their budgets – enhancing their experience and interest in advertising on the platform.
4. Personalized buying experiences: The tailored UX offered by OnlineSales.ai’s Monetize helped Tata engage brands of all levels in technical aptitude to use the platform easily. This also allowed admins to configure what types of inventories were available to different brands or advertiser segments.

OnlineSales.ai makes use of Google Cloud Dataflow to facilitate computations on real-time streaming. Microservices are deployed on the Google Kubernetes Engine (GKE) platform due to the solution’s well-known ability to handle scale.

In addition, Dataproc is used by the company to run batch processing apps while Cloud Load Balancing helps manage traffic across different regions; all at scale. Onlinesales.ai also uses Memorystore as a database for their ad servers in order to have very low latency, and deploys BigQuery to run analytical applications and facilitate powerful reporting. The bulk of their office applications are deployed on different VMs on Compute Engine, and the company also makes use of Cloud Storage for data storage and transfers between applications.

Learn more about OnlineSales.ai available on the Google Cloud Marketplace.

^{About OnlineSales.ai}

^{OnlineSales.ai, offers a fully white labeled retail media platform which helps retailers unlock additional revenue by activating advertising real estate on their platform. With its AI/ML-powered solution, OnlineSales.ai allows retailers to turn brands of all sizes into advertisers – at scale. Its self-serve platform simplifies the ad buying process, and enhances outcomes with smart automation, boosting ad retailers’ ad revenues by multiples.}

Cloud transformation and the adoption of modern DevOps technology presents both opportunities and challenges for IT compliance functions. With DevOps style application development, the feedback loop for developers and engineers is much tighter than with traditional application development pipelines, enabling speed and agility of application release cycles. While speedy CI/CD is a critical advantage of DevOps, it also shifts compliance left in the development timeline, and therefore puts pressure on the IT risk & compliance organization to modernize their approach to regulatory compliance as well. With the ongoing shift towards cloud technologies and DevOps, modernization of regulatory compliance is no longer optional for an IT compliance function

Compliance modernization is a broad mandate that spans the way the function is governed; the tools, technology, and analytics it uses; the number and nature of its connections to other parts of the business; verifiability and auditability of the controls’ evidence, the expectations assigned to it; and more.

Public cloud technology is becoming a core part of many industries today, and with this comes some potential risks such as cloud misconfigurations exposing intellectual property, loss of physical control of assets, skillset scarcity around cloud based security and compliance. 

Given the constantly changing risk landscape, it is critical that regulations more closely align to address these risks. As regulations and risks evolve, the aim of a modern compliance function is to help an organization stay compliant as it goes through a digital transformation. As organizations go through digital transformation, IT compliance also needs to transform — via upgrading the technology stack, modifying the business processes and most importantly re-skilling people to become cloud aware.

Today we are releasing the new paper by Google Cloud’s Office of the CISO. In the paper we reveal a new approach for modernizing your compliance approach using modern approaches and Google Cloud toolsets. Your team can leverage the paper to add value to enterprises, both by charting a course to the safe use of cloud technology and by reducing risk through the use of the public cloud.

Read the paper “Assuring Compliance in the Cloud.”

Also, review these related resources:

• “Risk Governance of Digital Transformation in the Cloud“ paper
• “Making Compliance Cloud-native” (episode 14) with Zeal Somani
• Google Cloud Compliance resource center
• Our compliance blueprints: PCI DSS on GKE and GCP FedRAMP Blueprint
• Google Cloud security best practices center

We’re excited to announce that Spring Cloud GCP version 4.0 is now generally available! In this post, we’ll be describing what the new major version has to offer, and demonstrating the process of using the migration guide on one of our reference architectures, Bank of Anthos.

What’s new?

With this release, Spring Cloud GCP officially supports Spring Boot 3.x. However, this migration involves a number of breaking changes as outlined in the migration guide. The full list of changes made is available on Github, but the one of the most significant differences is that Java 17 is now a minimum requirement.

Another notable feature of this release is the addition of starter artifacts – Spring Boot starters for Google Cloud – that provide dependencies and auto-configurations for 80+ Google Client libraries. Just as the name suggests, these starters can serve as helpful starting points when working with a new client library. For now, they’re in preview. 

These starters are not included in the BOM by default and need to be added as a dependency to your project before they can be used. For example, if you wanted to get started with Cloud Text-to-Speech, you would include the following:

<dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-texttospeech-spring-starter</artifactId>
    <version>4.0.0-preview</version>
  </dependency>

The upgrade process

We prepared a migration guide to help answer any questions involved with moving from 3.x to 4.x.  Let’s follow those instructions to migrate Bank of Anthos.

We’ll start by cloning and building the application before the upgrade, according to the quickstart and development guide:

PROJECT_ID=<YOUR-PROJECT-ID>
gcloud services enable container --project ${PROJECT_ID}

git clone https://github.com/GoogleCloudPlatform/bank-of-anthos.git
cd bank-of-anthos/

gcloud services enable container.googleapis.com monitoring.googleapis.com \
  --project ${PROJECT_ID}

REGION=us-central1
gcloud container clusters create-auto bank-of-anthos \
  --project=${PROJECT_ID} --region=${REGION}

gcloud container clusters get-credentials bank-of-anthos \
  --project=${PROJECT_ID} --region=${REGION}

skaffold run --default-repo=gcr.io/${PROJECT_ID}/bank-of-anthos

At the end of this, you should see a “deployment stabilized” message:

Deployments stabilized in 9.657 seconds

With the environment set up for development, we’re ready to move on to the actual migration. The migration is already complete at time of writing, but here’s the full list of changes made:

• We began by checking the current Java, Spring Boot and Spring Cloud GCP versions.
  • Our Java version is okay, but the other two are out of date, and will need to be updated.
    We can also add the Spring Cloud GCP BOM to manage our versions going forward while we’re here.
• Then, in no particular order, we can make the following code changes:
  • Remove the explicit type declarations from our ResponseEntity instantiations.
  • Replace all javax.* imports with Jakarta.* counterparts, since Spring 3.0 makes use of Jakarta EE 9.0

With those changes in place, the app is upgraded and ready to re-deploy!

Live example

For a finished example of this migration, check out the Bank of Anthos repository on Github. It’s an excellent sample application that showcases a polyglot Java & Python app, served on Kubernetes and Google Cloud. All CI/CD and configurations are open source, so it may be instructive as you approach this migration.

Thanks for reading, and feel free to provide comments or feedback on Twitter, or in the issues section of the repository.

Enterprises need to move faster than ever to gain a competitive advantage in today’s customer-focused environment. Time-to-market for products and services has shortened dramatically, from years to days. IT teams must move fast, react fast, and enable business strategies via constant innovation. 

All of this digital transformation is about more than adopting the latest technologies. It is also about maximizing the use of existing data and services to improve efficiency and productivity, drive engagement and growth, and ultimately make the lives of customers, partners, and staff better. Connecting existing data and services and making them easily accessible via APIs promises a path forward, empowering enterprises to extend the value they already possess with new technologies, managed services,  ecosystems, and support.

In addition to the challenges of legacy data and systems, today’s organizations are overwhelmed by the variety of cloud applications to meet their business needs and deliver innovative services to their customers. 

Managing all of this data, connecting sources, integrating applications, and surfacing them as easy-to-use APIs for development is a crucial competency for any IT organization.

Design APIs with an Outside-in Approach

Many IT organizations have focused on solving this challenge with an “inside-out” approach: starting with the integration layer, building the flow, and then developing the APIs. But this approach is inefficient and fundamentally flawed because it looks at the problem from an “exposure” model; rather than designing for the business use cases of developers and other API consumers. Leaders in the organization end up seeing all of this data, connectivity, and integration as the “table stakes plumbing”, and thus do not seek inputs regarding business value from key business stakeholders.

The key issue is not exposure but rather how you leverage your data, services, and systems to drive impact across your digital value chain. Goals such as meeting your adoption or sales targets, reducing costs across lines of business, speeding up time to market, and reducing time spent supporting your customers may all be within reach. Easy-to-use APIs, rather than crudely exposed systems, are foundational enablers of this impact, and they are almost always designed from the outside-in, from the perspective of teams that are consuming the APIs to achieve a business goal.

Embrace API-first Integration

To accelerate the speed of development, enterprise IT teams need to take an API-first approach to integration, starting with the consumers’ use cases rather than the structure of the data in their systems. 

The notion of outside-in thinking should be familiar to product managers, who routinely have to demonstrate customer empathy and put themselves in their customers’ shoes. If your team has a product owner, be sure they are empowered to decide what functionality is needed from their data. 

Maximize your APIs with the right technology enablers

An API-first strategy treats the API not as middleware but as a software product that empowers developers, enables partnerships, and accelerates innovation—a big shift from integration-first operations in which APIs are typically exposed and then forgotten. 

Possessing APIs is only part of the equation. If a company is going to share valuable digital assets with outsiders, it needs API management tools to:

• apply security protections, such as authentication and authorization
• protect assets from malicious attacks
• monitor digital services to ensure availability and high performance
• measure and track usage of the assets 

With the right tools in place, APIs can unlock incredible business opportunities—which is a reason for every enterprise to aspire to be API-first!

Visit our website to learn more about API management with Google Cloud.