For many enterprises, the venerable mainframe is home to decades’ worth of data about the company’s customers, processes and operations. And it goes without saying that the business would like access to that mainframe data — to report on it, to analyze it with big data analysis tools, or to use it as the basis of new machine learning and artificial intelligence initiatives.

At Google Cloud, we are eager to work with organizations to help them transform their mainframe assets for the cloud era. Of course, we can help them modernize their mainframe applications by migrating them to the cloud. At the same time, working with partners and customers, we’ve developed another, more lightweight approach that can help them start to leverage the cloud for their mainframe assets much more quickly than performing a full-fledged migration. We call this approach data-first digitization.   

In this rapidly evolving digital ecosystem, it’s imperative to understand the difference between ‘modernization’ and ‘digitization.’ With modernization you start with the current state and look forward, and rely on mainframe application migration approaches such as rehosting (emulation), refactoring (automated code transformation), reengineering — or simply replacing a custom application with a commercial package. With digitization, you start with the future state that you want to achieve, and work back to what is required to get there.

This data-first digitization approach includes a mainframe data-first integration framework comprising in-house and partner products and tools to migrate heterogeneous data sources from the mainframe to Google Cloud Storage. Once mainframe data has been copied to Cloud Storage, it can then be integrated and leveraged by Google Cloud tools such as BigQuery, AI and machine learning prodcuts  and Smart and Stream analytics platforms. The integration framework covers both bulk batch data transfers and real-time data replication (change data capture).

Data-first digitization is based on the tenet that ‘applications are transient, data is permanent.’ By bringing data first to Google Cloud instead of traditional ways of modernizing applications (for example, with Gartner’s 7 options to Modernize), this allows organizations to leapfrog to new business models, use cases and innovative ways to serve end customers. For example:

• Making decisions with smart and stream analytics platforms and AI/ML engines. These tools need data to make decisions. Google is a pioneer in extracting information and value from the raw structured and unstructured data, and this approach opens up mainframe data for use by BigQuery and AI/ML models. 
• Building new reporting applications. With access to mainframe data, you can use Google cloud products like Looker and Appsheet to build net-new reporting applications, expediting the process of retiring mainframe reporting applications, and accelerating your overall transformation.

In our experience, taking a data-first digitization approach to your mainframe offers a number of benefits:

1. Faster time-to-business: Because data-first modernization is built on existing products, the implementation cycle is much shorter.
2. Less capital investment: You spend your time integrating products, not developing applications.
3. Minimized risk: Data-first integrates with existing, proven and reliable Google Cloud products.
4. Faster overall mainframe transformation: When you shift your modernization center of gravity from the application to the data, you look at mainframe applications from a business perspective instead of just “keeping the lights on.” As a result, only the most business-critical applications are modernized and many support applications can be decommissioned, accelerating your transformation journey. 

Taking a data-first approach to digitization is still relatively new, but we’re heartened by customers’ early successes. Watch this space for additional insights, reference architectures and technical white papers around data-first. And if you think this approach may be right for you, reach out to mainframe@google.com.

Learn more:

• Liberating your mainframe data with Confluent and Google Cloud
• Beyond mainframe modernization: The art of possibilities

Customers today expect brands to know what they want. In fact, Forbes cited that 71% of customers feel frustrated when the shopping experience is impersonal. From interests to purchase habits, companies need to target customers effectively in order to stand out from the plethora of competitors in the online marketplace.

“Our mission is to help brands engage customers with the most appropriate marketing messages at the right place and at the right time while respecting consumer privacy and ensuring regulatory compliance. The experience of Google Cloud in working with large-scale datasets makes the platform an excellent fit for us.”

—Projjol Banerjea, co-founder and Chief Product Officer, Zeotap

Leveraging first-, second-, and third-party data, Zeotap’s Customer Intelligence Platform (CIP)—a CDP with additional identity resolution and third-party data enrichment capabilities—allows brands to engage with their known and unknown users with tailored marketing messages and next best offers or actions. It also offers out-of-box algorithms for standard marketing use cases such as promoting a specific product that will resonate with audiences and custom algorithms for very niche and specific use cases.

“Our mission is to help brands engage customers with the most appropriate marketing messages at the right place and at the right time while respecting consumer privacy and ensuring regulatory compliance. The experience of Google Cloud in working with large-scale datasets makes the platform an excellent fit for us,” says Projjol Banerjea, co-founder and Chief Product Officer at Zeotap.

“With BigQuery, we are able to operate more efficiently, removing 80% of the operation load that we would otherwise have to manage on a different cloud provider. As a result, we get to build and deliver products faster.”

—Sathish K S, VP Engineering, Zeotap

Improving data processing to deliver products faster

The cloud-native company migrated from its previous provider to Google Cloud in October 2019 because it was attracted to the benefits of BigQuery as a serverless cloud data warehouse. This was a feature that wasn’t available with its previous provider.

Sathish K S, Engineering Vice-President at Zeotap says, “With BigQuery, we are able to operate more efficiently, removing 80% of the operation load that we would otherwise have to manage on a different cloud provider. As a result, we get to build and deliver products faster.” Since coming onboard BigQuery, Zeotap has been able to increase the speed of its product delivery by 40%.

BigQuery also offers a much higher query limit, which is helpful because Zeotap can converge all of its previous operation load across different platforms into one data warehouse without worrying about it being overloaded.

One of the key services Zeotap offers is customer relationship management (CRM) platform integration. Using Dataflow, Zeotap can easily stream first-party data into BigQuery at scale.

The data integration capabilities of Cloud Data Fusion also complements Dataflow to help Zeotap enhance its API offerings. To illustrate, an ecommerce company may use Zeotap’s internal API to understand their customer’s cart and check-out habits and add Zeotap’s extra layer of data intelligence to it, so they can improve their marketing efforts. Cloud Data Fusion enables a seamless flow of real-time data so that the company can deliver the relevant ads or suggest products just before they make a purchase, which increases the likelihood of them buying more.

Zeotap uses Dataproc and Pub/Sub to manage the large flow of data that goes in and out of its database. Because many of its customers are pushing data in real time, Pub/Sub acts as a queuing engine that feeds them into Dataflow. Likewise, the data from Dataflow is also being channeled back smoothly to the customer endpoints using Pub/Sub.

Being able to automate all of its processes benefitted the DevOps team, as they now have time to focus on developing and improving Zeotap’s product offerings instead of spending time and energy on operations.

Managing a full migration seamlessly

Today, all of Zeotap’s data is hosted on Google Cloud. The company began its lift and shift migration in October 2019 and was complete by April 2020. “We have close to 405 data pipelines, and even after the migration, we had to verify the right datasets. We had terabytes of data to move, and despite this, our operations were not affected,” says Aditya Chandra, Vice-President of Infrastructure and Security at Zeotap.

Apart from the BigQuery capabilities, there were other major things Zeotap looked out for in its decision to move to a new cloud provider. The functional parity of Google Cloud was important so that it did not have to rebuild many of its existing structures. Zeotap also didn’t want any drop in performance during and after the move to a new cloud provider. Finally, as a multi-region company, it used to have to spot instance problems and run them on demand, which had cost implications. With the move, it no longer had to face such issues.

“Whatever Spark code that was already running on our existing system had to be lifted and shifted into Dataproc without any library changes because 60% of our codebase is out of there. We’re glad that this was possible,” says Ameya Agnihotri, Chief Technology Officer.

Managing business continuity with a trusted partner

Zeotap now works with Rackspace Technology (Rackspace) as it continues to optimize its infrastructure operations. As a marketing and technology company, Zeotap needs niche expertise to manage big data processing issues.

Ameya Agnihotri shares, “Given its global expertise in dealing with on-premises as well as cloud solutions, Rackspace can really add value because they are better equipped to troubleshoot and handle complex data operations.” Zeotap has plans to explore more of Google Cloud’s artificial intelligence (AI) and machine learning (ML) capabilities, so having a partner in this journey will be valuable to the team.

With so much data on hand, security is paramount to Zeotap. It does a lot of internal security assessments daily. This is one of the areas where the team works with Rackspace to ensure that it adopts best practices and stays on top of the latest updates. With its ability to handle big data operations, Rackspace also acts as an extension of the Zeotap team that supports the infrastructure side of the business.

“The level of engagement from Google Cloud has been excellent, particularly the ability to bring in expertise from different parts of the organization. The personal touch meant we could reach product owners and managers of specific products. This kind of support is what we really value and appreciate.”

—Ameya Agnihotri , Chief Technology Officer, Zeotap

Future plans and collaboration with Google Cloud

Zeotap is currently exploring Cloud Bigtable and API Gateway as it continues to grow the business and explore new types of services to offer to its customers. Although it has a software-as-a-service (SaaS) offering for customers to self-serve, some may prefer to do a server-to-server integration so that they do not have to worry about the back end at all. For this reason, Zeotap is exploring the possibility of integrating these customers’ APIs in the back end of its API, using Google Cloud’s API Gateway features that are available out of the box. The company is also looking forward to the new features being released on BigQuery, as well as exploring more of the alerting and monitoring capabilities of Google Cloud.

“The level of engagement from Google Cloud has been excellent, particularly the ability to bring in expertise from different parts of the organization,” says Ameya. He shares that the team has had many fortnightly calls when there were issues to address, and the Google Cloud team would help to fix the problems. “The personal touch meant we could reach product owners and managers of specific products. This kind of support is what we really value and appreciate.”

With big plans ahead for Zeotap that include becoming a Google Cloud Technology Partner in the near future, Swapnasarit Sahu, Chief Data and Analytics Officer, is confident that the company has chosen the right cloud solution. “We see Google as one of the frontrunners in the space of AI and ML solutions, which can really help data scientists in developing new and more innovative products and solutions, and we look forward to building great products with them,” he says.

AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

Skyscanner is a leading global travel search company covering flights, hotels, and car hire around the world. Founded in 2003, the company helps over 40 million people each month find the best travel options across its portfolio of websites and mobile apps.

Skyscanner wanted to understand the anonymized behaviour of consumers on a more granular level than was possible via standard reports in the Google Analytics Premium web interface or even using the reporting APIs.

“These methods work well for high-level analysis and standard marketing reporting, but we were keen to dig deeper into the data to get more insight and further optimise our products,” explains Mark Shilton, Principal Analyst in the Skyscanner Data Team.

For example, the company wanted to create detailed cohorts to understand how users interacted with Skyscanner over time. Also, different teams in the business were keen to understand the performance of individual pieces of functionality that fell within their remit.

To do this, they needed to understand not just the overall conversion rate, but how users who interact with a given piece of functionality convert compared to those who do not. Skyscanner also wanted drill down into specific markets, devices types, and marketing channels.

Mapping a plan for deeper insights

Skyscanner opted to address all of these needs by integrating Google Analytics Premium with BigQuery. The integration has become the starting point for many detailed investigations across the business.

For example, analysts and engineers now run cohort analyses to understand how frequently users return to Skyscanner and which channels are most effective at which part of the customer journey.

“This type of analysis is allowing a much deeper understanding of our marketing activity and is informing our future strategy and spend,” Mark says.

He reveals that using BigQuery in conjunction with other tools such as Tableau and Python also helps Skyscanner execute analysis much more quickly and efficiently than before.

“While in the past it was tricky to get a fully unsampled report based on specific segments of users flowing directly from Google Analytics Premium into a Tableau dashboard, now it is simply a matter of writing the query, creating a connection in Tableau to automatically refresh the data daily, and publishing this dashboard to the rest of the company.”

Another key benefit of using a flexible combination of tools is the ability to keep an eye on costs.

“Where the aggregations and segments of data are required on a regular basis, we have to consider the potential cost of querying the entire BigQuery dataset multiple times for the same data,” Mark explains. “To minimise this, we use Python scripts to automate these aggregations into new, smaller tables that are much more cost efficient to query.”

Excellent visibility and a clear path ahead

Combining Google Analytics Premium with BigQuery has supercharged Skyscanner’s ability to turn raw data into deep understanding of consumer behaviour.

“We have been using BigQuery for various pieces of analysis, from one-off investigations to powering daily dashboards,” Mark affirms. “In all cases it has speeded up our workflow and enabled us to gain greater insight more quickly. In a fast moving internet economy, this is key. Instead of setting up and scheduling one or more unsampled API reports, analysts can now write a query against BigQuery and have results almost instantly.”

“BigQuery has also allowed us to more easily isolate the effects of marketing from the effects of site changes,” Mark says. “By writing queries that focus on the conversion rate from specific pages in the funnel, we can better segment our traffic. We can separate traffic from various sources, including: specific marketing campaigns, users who make it to specific parts of the funnel, or users who interact with new functionality. This greater understanding has played a key role in improving overall conversion rates on our websites, particularly on mobile where we’ve achieved conversion rate improvements of 30 to 40% on smartphone and tablet devices in the last six months.”

Looking to the future, Skyscanner’s next steps include exploring how this data can be used to segment, cluster, and classify users for machine learning analyses, which would not have been possible using standard reporting functionality.