In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.

At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.

Here are some companies and their stories.

Tata Steel: Mining data and maximizing its power

Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage. 

To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.

They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.

“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.

Helping L&T Financial Services reach customers in rural communities

In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.

L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.

“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.

Creating conversational connections for Digitate’s customers

Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.

“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.

Indian enterprises going G Suite

The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.

A hybrid and multi-cloud future in India

IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions  — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.

Responding to multiple, simultaneous disruptive forces has become a daily routine for most demand planners. To effectively forecast demand, they need to be able to predict the unpredictable while accounting for diverse and sometimes competing factors, including:

• Labor and materials shortages
• Global health crises
• Shifting cross-border restrictions
• Unprecedented weather impacts
• A deepening focus on sustainability
• Rising inflation

Innovators are looking to improve demand forecast accuracy by incorporating advanced capabilities for AI and data analytics, which also speed up demand planning. According to a McKinsey survey of dozens of supply chain executives, 90% expect to overhaul planning IT within the next five years, and 80% expect to or already use AI and machine learning in planning.

Google Cloud and SAP have partnered to help customers navigate these challenges and supply chain disruptions starting with the upstream demand planning process, focusing on improving forecast accuracy and speed through integrated, engineered solutions. The partnership is enabling demand planners who use SAP IBP for Supply Chain in conjunction with Google Cloud services to access a growing repository of third-party contextual data for their forecasting, as well use an AI-driven methodology that streamlines workflows and improves forecast accuracy. Let’s take a closer look at these capabilities.

Unify data from SAP software with unique Google data signals

When it comes to demand forecasting and planning, the more high-quality and relevant contextual data you use, the better, because it helps you understand the influencing factors of your product sales to sense trends and react to disruptions or capitalize on market opportunities more timely and accurately.

The expanded Google Cloud and SAP partnership helps customers who use SAP® Integrated Business Planning for Supply Chain (SAP IBP for Supply Chain) bring public and commercial data sets that Google Cloud offers into their own instances of SAP IBP and include them in their demand planning models in SAP IBP. So, in addition to sales history, promotions, stakeholder inputs and customer data that are typically in SAP IBP, a demand planner can incorporate their advertising performance, online search, consumer trends, community health data, and many more data signals from Google Cloud when working through demand scenarios.

More data enables more robust and accurate planning, so Google continues to build an ecosystem of data providers and grow the number of available data sets on Google Cloud. Some current providers include the U.S. Census Bureau, the National Oceanic and Atmospheric Administration, and Google Earth, and partnerships are underway with Crux, Climate Engine, Craft, and Dun & Bradstreet to help companies identify and mitigate risk and build resilient supply chains.

Augmenting demand planning with additional external causal factor data is a starting point to drive more accurate forecasting. For example, knowing what regional events may be happening, or the weather patterns that may impact sales of your products, allows you to react faster to these changes by making sure adequate supply is being provided. The result is a more accurate overall plan that reduces resource waste and out-of-stock events. Planners can respond with more accurate and granular daily predictions about sales, pricing, sourcing, production, inventory, logistics, marketing, advertising, and more based on the expanded data.

Get more accurate forecasts with Google AI inside

Extending the already expansive algorithm selection available in SAP IBP, the release of version 2205 allows SAP IBP customers to access Google Cloud’s supply chain forecasting engine, which is built on Vertex AI — Google Cloud’s AI-as-a-platform offering — from within SAP IBP as part of their forecasting process.

The benefit of using an AI-driven engine for demand forecasting is that it meaningfully improves forecast accuracy. Most demand forecasting today is done through a manually set, rules-based model versus an AI-driven model that is smarter and gets better at predicting demand as it works.

Take the fastest path from data to value with streamlined workflows

Vertex AI can include relevant contextual data sets for demand planning, and the results can be shown in SAP IBP for planners to incorporate when building their workflows.

In addition to more accurate forecasts, planners can work faster and more efficiently as they build potential scenarios, meaning they can do more simulations than they do now so that a wider range of disruptions can be modeled. Customers of SAP IBP don’t have to do any of the heavy lifting. They just have to share their data from SAP IBP with Google, then access the process workflow capabilities to set up automated workflows that use the combined data. Google makes the data available so that planners can use it as they’re setting up their workflows in Vertex AI.

Users of the Google Supply Chain twin and SAP IBP can combine the rich planning data from IBP with additional SAP data and other Google data sources to provide better supply chain visibility. The Google Supply Chain twin is a real-time digital representation of your supply chain based on sales history, open customer orders, past and future promotions, pricing and competitor insights, consumer history signals, external data signals and Google data.

Leverage Google data signals with SAP IBP for more accurate forecasts

It’s not difficult to access these new capabilities, and the benefits are more accurate near-term forecasts and more return on your investments in SAP IBP and Google Cloud. If you happen to be at the Gartner Supply Chain Symposium from June 6-8th in Orlando, Florida, stop by our booth to say hello. Or, get started now

Setting up your new cloud infrastructure is scary. Extra scary when you realize that someone (is it gonna be you?) gets to have phenomenal cosmic power over the whole thing. 

Yes, I’m talking about the admin account, and today we’ll dig into why they are important, dangerous and different.

When the team at pistach.io got their nuts-as-a-service business growing fruitfully, they knew they needed to think carefully about admin accounts. These people would have tremendous control over their use of Google Cloud, and they could potentially cause very big problems if any were compromised. Definitely resources that require a protective shell.

Early in pistach.io’s development, an employee named Walter Nutt wanted to play a prank by changing his co-worker’s profile photo from an almond to a peanut (pretty devious, since a peanut is actually a legume). He didn’t have access to his friend’s computer, but he did have access to the company’s Cloud Storage bucket. While searching for the profile photo in question, Wally inadvertently deleted the entire contents of the bucket! 

As he searched for ways to restore its contents, Wally modified access to two other pistach.io buckets.  The company was ground to a halt for a week while teams worked to crack through the permissions issues.

Time to rethink permissions a bit, so this couldn’t spoil their buttery smooth operations in the future.

Following the resource manager guide, the team made a super admin email address that wasn’t tied to a particular individual or Workspace account, and secured it with strong multi-factor authentication. This would be their backup in case an admin account were to be compromised, so they could recover and repair.

The team already uses Google Workspace, so they have an organization set up already. That creation process established initial super administrators, allowing them to create and modify all other resources inside the organization. As they looked toward using Google Cloud, the super administrators could:

1. Give the admin role to people, for Cloud
2. Act as a point of contact for account recovery
3. Modify or delete the organization if needed

Making admin users for the organization allows other people to then flesh out the resources and policies for pistach.io, before they go nuts and give everyone all the permissions. While that would speed things up, it would make it easy for an attacker to crack through the security shell because any account compromise could give ousize access. Yikes!

Instead, the IT leads specified certain people to act as organization admins, and then gave them permissions to:

1. Define Identity and Access Management policies
2. Structure the Resource Hierarchy
3. Delegate control of specific Cloud elements to others on the team

Once those organization admins were set up, they could give management and oversight of Compute, Storage, Networking and other resource types to the relevant leads, making sure each person had just the right amount of permission for the role they needed to perform. The organization admins don’t have permissions themselves to make these resources. They just delegate. 

Now each person can accomplish the job they’re responsible for, but doesn’t have overly permissive access. Delegating like this keeps the entire organization safer, and limits the blast radius if someone does manage to break in.

You can go through these steps yourself with this tutorial.

By default the creation of an organization resource for the domain gives everyone the ability to create projects and billing accounts. Once they set up their Organization Admin at pistach.io they decided to remove some of these wide permissions and, in a nutshell, bring everything down to a much finer control. So people could get permissions for a folder or a project, but not the entire organization!

Remember to take care of your admin roles, as they have the power, and responsibility, to cause serious harm if not used safely. Be safe with your Identity and Access Management. And keep your data yours! 

Next time we join you we’ll take a crack at creating and provisioning an app to run inside the policies and resource management frameworks created today.

The home lending journey entails processing an immense number of documents daily from hundreds of thousands of borrowers. Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs. Scaling a business that sorts through millions of documents daily, while increasing efficacy and accuracy, is no small feat. When it comes to applying for a mortgage loan, consumers expect a digital experience that’s as good as the in-person one. Roostify simplifies the home lending journey for lenders and their customers.

No time to spare: Overcoming document processing challenges with AI

Roostify provides enterprise cloud applications for mortgage and home lenders. In order to empower its customers to deliver a better, more personalized lending experience, they needed to automate and scale their in-house document parsing functionality.

As a key component of its document intelligence service, Roostify is leveraging Google Cloud’s Lending DocAI machine learning platform to automate processing documents required during a home loan application process, such as tax returns or bank statements with multi-language support. This partnership delivers data capture at scale, enabling Roostify customers to automatically identify document types from the uploaded file and to extract relevant entities such as wages, tax liabilities, names, and ID numbers for further processing, and make things move faster in the cumbersome lending process. 

Roostify’s solutions leverage Google Cloud’s Lending DocAI, which is built on the recently announced Document AI platform, a unified console for document processing. Customers can easily create and customize all the specialized parsers (e.g., mortgage lending documents and tax returns parsers) on the platform without the need to perform additional data mapping or training. All Google Cloud’s specialized parsers are fine-tuned to achieve industry-leading accuracy, helping customers and partners confidently unlock insights from documents with machine learning. Learn more about the solution from the GA launch blog and the overview video.

Integrating Lending DocAI’s intelligent document processing capabilities into the Roostify platform means more innovation for their customers and tangible results: faster loan processing times, fewer document intake errors, and lower origination costs. Additional support in Google Lending DAI for other languages and more documents like global Know Your Customer (KYC) documents or payroll reports is in the near future.

Full integration of AI solutions

Working together with Roostify’s platform team, we were able to help them solve their document processing challenge through integration of various GCP products such as Lending DocAI (LDAI), Data Loss Prevention (DLP) for redacting sensitive data, BigQuery for data warehousing and analytics, and Firestore for API status. To make it very safe and secure, all data was encrypted end-to-end at Rest and in Transit. LDAI won’t require any training data to process. It is an easy plug and play API.

Here is a sneak peek in the high level deployment architecture for LDAI in Roostify environment:

Here are the steps for processing data:

1. Receives document processing request from the client.
2. API Function directs requests to the pre-processing service. For Async requests a processing ID is generated and returned to the caller.
3. Pre-processing service sends the request for further processing (Long/short PDF conversion), calling other microservices and receives back the responses. Any error in the response received is then sent to the response processing service. 
4. If the response is synchronous, the pre-processing service directs it to the LDAI Invoker service. 
   1. If the response is asynchronous, the pre-processing service feeds it into the Cloud Pub/Sub service.
5. Cloud Pub/Sub service feeds the response back to the LDAI Invoker service.
6. LDAI Invoker service routes the request to the Google LDAI API for classification if there are multiple pages in the document.
7. Document will be split based on LDAI response and then saved in a GCS bucket for temporary storage.
8. LDAI entity interface for single page processing and then LDAI Invoker sends LDAI results to LDAI Response Processing
9. If a request is a synchronous request the LDAI Response Processor sends results to the API Function so that it can complete the synchronous call and respond to the rConnect caller.
   1. If the request is an asynchronous request the LDAI Response Processor will respond to the caller’s webhook and complete the transaction.
10. Finally, Data stored in the GCP bucket will be deleted.

All the responses that come from the LDAI API can optionally feed into BigQuery via the Response Processor, after parsing it through Data Loss Prevention (DLP) API to redact the PII/sensitive information.  Throughout the processing of both asynchronous and synchronous requests all transactions are logged using Cloud Logging.  For asynchronous transactions, the state is maintained throughout the process using Cloud Firestore.

Roostify currently uses this technology to power two different solutions: Roostify Document Intelligence and Roostify Beyond™. Roostify Document Intelligence is a real-time document capture, classification, and data extraction solution built for home lenders. It ingests documents uploaded by borrowers and loan officers, identifies the relevant documents, and extracts and classifies key information. Roostify Document Intelligence is available as a standalone API service to any home lender with any digital lending infrastructure already in place. 

Roostify Beyond™ is a robust suite of AI-powered solutions that enables home lenders to create intelligent experiences from start to close. It combines powerful data, insightful analytics, and meaningful visualization to streamline the underwriting process. Roostify Beyond™ is currently available only to Roostify customers as part of an Early Adopter program and will be rolled out to the market later this year.

Through this partnership, Roostify has enabled its customers to adopt a data-first approach to their home lending processes, which will lead to improved user experiences and significantly reduced loan processing times.

Fast track end-to-end deployment with Google Cloud AI Services (AIS)

Google AIS (Professional Services Organization), in collaboration with our partner Quantiphi, helped Roostify deploy this system into production and fast-tracked the development multifold to generate the final business value.

The partnership between Google Cloud and Roostify is just one of the latest examples of how we’re providing AI-powered solutions to solve business problems.

Enterprises today have a very broad mix of networks — from SD-WANs, dedicated WANs such as MPLS, cloud interconnects, to VPNs. At the same time, they’re moving those WANs to the cloud to take advantage of faster turn-up, lower cost, and increased feature velocity. As workloads migrate to the cloud and multi-cloud environments, we believe that it’s critical to simplify enterprises’ networking model.

Each major cloud provider uses distinct abstraction models to configure networks or connections between your resources. Some use gateways, some use connections or links. Network Connectivity Center, launched last year, provides a simple management solution for your network connection, and is now Generally Available.

In this post, we outline the typical connectivity use cases for customers to help you select and set up the best connectivity option for your environment.

Understanding cloud network connectivity

Cloud networking refers to the ability to connect two resources together inside a cloud, across clouds and with on-premises data centers. A cloud provider needs to provide three main types of connectivity:

• Site-to-cloud – Between on-premises equipment and cloud resources
• Site-to-site – To connect on-premises resources together
• VPC-to-VPC – Connectivity between cloud resources
• Let’s take a look at each one.

Site-to-cloud connectivity

Site-to-cloud connectivity traditionally is done via a cloud interconnect or a cloud VPN. The automatic exchange of routes between on-premises and multiple VPCs can be done using a transit VPC.

A newer approach is to add cloud providers into an SD-WAN mesh using a router virtual appliance in Google Cloud. Network Connectivity Center brings the capacity to synchronize the appliance routes dynamically via BGP to Cloud Router and hence their VPCs. It enables connectivity between on-premises data centers and branch offices and their cloud workloads via SD-WAN-enabled connectivity. This capability is available globally across all 29+ Google Cloud regions. Several of our partners also support this capability in their router appliances.

Site-to-site connectivity

Site-to-site connectivity enables network connectivity directly between two or more hybrid connection points (VPN, Interconnect or SD-WAN). Network Connectivity Center simplifies this model by automating the routing announcements in this environment, such that all sites connected to a single global Network Connectivity Center hub are able to communicate freely in any-any fashion. You can see an example of this for a specific market vertical use case in a recent blog, Voice trading in the cloud — digital transformation of private wires.

VPC-to-VPC connectivity

You can create a full or partial mesh of VPC connections using multiple technologies, with VPC peering being the most common. VPC peering provides highly performant, low latency, private connectivity for customer networks connected via hybrid connectivity and Network Connectivity Center to multiple VPCs containing workloads, which can be segmented via granular firewall policies as needed. Alternatively, you can use a transit VPC model to connect multiple VPCs together in a hub and spoke topology.

With tight integration with third-party router appliances as mentioned earlier, you can also leverage their third-party supported solutions such as next-generation firewalls to connect your VPCs together to meet specific compliance and segmentation requirements. Network Connectivity Center allows you to synchronize the routing tables of these appliances with your VPC’s routing table, simplifying the process of setting up redundant configurations.

What’s next for cloud networking connectivity in Google Cloud?

As enterprises continue to migrate different types of workloads to public cloud providers, networking topologies are becoming more complex. In summary, we have solutions for all connectivity needs. We aim to keep our models and solutions understandable and simple. Over time, look for Network Connectivity Center to become Google Cloud’s single point of configuration for all your connectivity needs, with capabilities to handle the most complex network.