Bit Capital Rolls Out a Digital Financial Solution in Under 3 Months and at 2/3 the Cost
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In the past few years, a series of new technologies and regulatory changes has been transforming Brazil’s financial industry. The concept of blockchain added security and agility to financial transactions. The open banking system being deployed by the country’s Central Bank (BC) allows for platform integration and data sharing -with the user’s consent- between financial institutions. Recently, the launch of Pix, also by the BC, has shaken the market by creating a new payment method that is instant, free for individuals and 24/7.
Since 2018, the startup Bit Capital has been working on the development of 100%-digital financial solutions in the cloud to help its clients adapt to this new scenario in a convenient way, without the need to build an infrastructure for that or hiring different providers. Pix was not the exception.
In less than 3 months, the team was able to build and go live with a solution that can be used by both direct participants (i.e. those with banking licenses granted by BC) and indirect participants (i.e. companies depending on direct participants to offer payments on Pix) through Bit Capital’s platform.
Besides this differential, Pix and the startup’s other solutions are based on blockchain and Google Cloud’s cloud, ensuring greater security, scalability and availability for customers and allowing for an easy integration between Bit Capital’s platform’s solutions and those from other companies.
“Now we have a platform with an API and various microsservices, allowing clients to connect and develop their own financial product without having to start from scratch.”
—Francesco Miolo, CFO, Bit Capital
A robust structure to handle Pix’s high demand
Bit Capital was established with a certain urgency to develop its platform. The idea was well-developed and customers were interested. The fast deployment of Google Cloud’s tools was one of the main factors that attracted the company to use it as a basis for its infrastructure.
“We managed to deliver everything we have today in Google Cloud with a small team. That was another challenge: being able to grow with a few people,” says Juliano Souza, the startup’s head of IT infrastructure. “We sought other cloud partners, but they had a steep curve. We chose Google Cloud because we needed quick, quality scaling.”
The same thing happened when they built the solution for Pix, despite the specific challenges involved. The BC had performance requirements that led the company to spend some time experimenting until they reached the best suite of tools to meet those requirements. The startup wanted to create a unique architecture that could help both large and small customers and be integrated to the platform’s other microservices.
With the support from Google Cloud’s team to answer doubts and make the best decisions, the solution was built in a few months, at a cost about two-thirds lower than other providers.
The solution’s architecture is based on apps running in Docker in Google Kubernetes Engine (GKE), with interconnected microservices. The blockchain system runs on Compute Engine, its rows are managed in Pub/Sub and Dataflow, and persistent data, in Cloud SQL. Cloud Interconnect is used for the connection with BC. Cloud KMS and Secrets Management store the company’s pre-credentials. All of this is supported by Cloud Load Balancing for load balancing and autoscaling.
According to the team, GKE was essential for the solution’s success. Using infrastructure as code in the tool made uploading of a group of microservices significantly easier. Developers are able to help set up this environment, which has helped spread the DevOps culture in the team. Besides orchestrating and integrating apps, GKE also provides an elastic structure to handle demand peaks and visibility to monitor internal components.
“Google Kubernetes Engine was the only way to ensure availability, observability and elasticity for all clients, whether small, middle-sized or large.”
—Juliano Souza, Head of IT Infrastructure, Bit Capital
Nowadays, the solution’s environment has 20 clusters with over 1,500 pods – 250GB in data per month, providing a robust structure to support a service involving periods of intense demand such as Black Friday.
Ease to monitor, fix and improve
Just 10 days after Pix’s official launch date, the company had its first test: Black Friday. This allowed a major e-commerce customer to test the scale and see the success of the architecture that had been built. “It worked great regarding what the cloud could deliver. And we found what we needed to fix very quickly. Operations [formerly Stackdriver], in particular Cloud Trace, showed us clearly what needed to be done to improve performance,” Souza explains.
Using Operations added reliability by putting deliveries into production. Checking Cloud Trace to see if there were any performance issues and the exact point where they were happening became routine for Bit Capital’s developers. Google Cloud’s security tools and Google Safety Center provided the resources needed to monitor and secure the environment, with automatic data encryption at rest and in transit.
“Google Cloud has security as a premise. When we upload any kind of component, like a database or a virtual machine, the drive is encrypted by default. With other providers, you must specify that you want it encrypted.
”—Juliano Souza, Head of IT Infrastructure, Bit Capital
The easy service monitoring and management accelerates product and technology development because the team no longer needs to worry about infrastructure. Automated management allows professionals to spend more time on new projects and the company’s business. Also, the deployment of services in a Google Cloud multi-region impacts on customer experience, providing high availability for their solutions.
In the coming months, Bit Capital aims to ramp up service usage and the creation of new projects in Google Cloud by adding more customers to Pix’s solution and Banking as a Service (BaaS) solutions. Anthos will be incorporated to the tool suite to make it easier to connect apps with the customers’ on-prem environments. And the deployment of open banking has the potential to be a business driver for the company.
“We joke that we’re already doing open banking, because we have various connections with different providers, which allows us to offer an integrated solution,” says Francesco Miolo, the startup’s CFO. “With the arrival of the new regulations, something we are waiting for since we started out, we will be able, through our platform, to take our customers to the open finance ecosystem, enabling the development of disruptive business models.”
AirAsia Leverages Google Cloud to Enhance Pricing, Revenue, and Customer Experience
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With Google Cloud, AirAsia is now a “data first” business that can capture, analyze, and report on rising volumes of data to address complex problems and increase revenue streams. The airline is also using AI-powered chatbots to streamline internal operations and provide a faster, more efficient service.
Google Cloud results
- Increases employee survey response rates by 30% and improved safety, scheduling, and employee orientation
- Enables movement towards a zero trust security model while reducing administration load on IT team
- Increases business agility by deploying faster and more frequently
Become a digital airline powered by data and machine learning
AirAsia’s vision is simple: allow everyone to fly. Founded in 2001, the airline and sister company AirAsia X have grown to service 150+ destinations in 25 markets, using 274 aircraft to operate 11,000+ weekly flights from 23 hubs across the region. While the airline is known as a provider of low-cost airfares to locations across Asia, this is only one part of its value proposition. AirAsia also aims to deliver innovative, personalized products and services that meet the needs of each of its passengers.
Technology is key to AirAsia’s success and, with strong support from Group Chief Executive Officer Tony Fernandes, in 2016 the airline began a five-year program to become a data-first business and a digital airline.
“We wanted to better ensure we were using data correctly to become more agile, efficient, and customer oriented,” says Lye Kong Wei, Chief of Data Science, Group Head at AirAsia.
AirAsia needed technologies and services that could capture, process, analyze, and report on data, while delivering value for money and meeting its speed and availability requirements. The airline also wanted to minimize infrastructure management and system administration demands on its technology team.
Google Cloud the best fit
AirAsia realized only a cloud service could meet its needs and began evaluating the market. The airline then conducted a proof of concept and found Google Cloud was the best fit for its business. It was already familiar with Google Cloud, having deployed Google Workspace collaboration and productivity applications to its workforce in all countries except China. According to Kong Wei, products such as Forms, Docs, Sheets, and Gmail delivered a considerable improvement in collaboration between various departments, as well as streamlining and automating a range of processes.
The business was particularly excited by the potential of the BigQuery analytics data warehouse to power its digital transformation. “We knew data was a big part of making decisions in the future,” says Kong Wei. “So we needed a platform that could scale to meet our growing appetite for it. Google Cloud—in particular BigQuery—was ideal for this task.”
The AirAsia technology team was impressed by the ease and flexibility with which it could extract, transform, and load customer data from its systems, websites, and mobile applications into BigQuery for analysis. Data, reports, and dashboards were delivered and visualized through Looker Studio.
BigQuery also scaled seamlessly to support data growth and, as a managed service, required minimal administration from the airline’s technology team. “In addition, with BigQuery, we could process queries and requests much faster than previously and tackle more complex problems,” says Kong Wei. “As a result, we could be more innovative about realizing opportunities,” he adds, citing the benefits of being able to view and understand historical measures of booking curves—a measure of how long it took customers to book before a flight. This improves the airline’s ability to manage revenues.
A broad ecosystem
BigQuery and Looker Studio are just two components of a broad ecosystem—powered largely by Google Cloud services—deployed by AirAsia. Pub/Sub provides a scalable message queue that enables AirAsia developers to integrate systems hosted on Google Cloud or externally. Apache Airflow enables the business to create, schedule, and monitor workflows, while Cloud Composer manages the dependencies of PHP and related libraries. App Engine allows AirAsia personnel to develop and host web applications. “Thanks to App Engine, we’ve easily been able to create new applications, services, and APIs powered by the data we have been collecting,” says Kong Wei.
AirAsia is also running the middleware for its APIs in a managed Google Kubernetes Engine environment for increased scalability, resource optimization, and reliability, while Cloud Storage provides storage for data from a range of systems and sources. Dataflow enables the business to transform and process data in stream and batch modes from its website search page as customers look for flights.
Faster deployment and testing
The stability of Google Cloud service means AirAsia has a reliable base from which to launch new products and features. “If we have consistent reliability from our core systems—and Google Cloud incorporates monitoring tools such Cloud Monitoring that enable us to identify issues quickly—developers and product engineers can focus on turning ideas into reality,” says Kong Wei. “With a minimal number of people involved, we can very quickly transform an idea or thought process into a deliverable. Prior to Google Cloud, bringing those ideas to fruition would have been impossible.”
Robust security
AirAsia is also relying on Security Health Analytics, a product that integrates with Security Command Center, to identify misconfigurations and compliance violations in its Google Cloud resources and take action. Security Health Analytics ensures the airline’s budgets go to keeping customers’ travel costs low rather than recovering from security breaches.
The product enables AirAsia to check that resources are configured properly and are compliant with CIS benchmarks as its critical workloads run in Google Kubernetes Engine and App Engine.
“Being able to go to the new Security Health Analytics dashboard eliminates the guesswork of what we have running and if it is secure,” says Muhammad Faeez Bin Azmi, Information Security and Automation Solution Architect. “Now anyone on our team, even non-security professionals, can go to this dashboard and see a list of the misconfigured assets and compliance violations across all of our Google Cloud resources. We can also see the severity of misconfigurations, which helps us prioritize our response.”
“Security Health Analytics has really helped us reduce the amount of time we spend trying to figure out what’s wrong with our resources. It’s allowed us to use our time more effectively to identify and resolve more security issues than we could before.”
A new identity solution
AirAsia had also used a legacy on-premises directory for many years. However, as the company grew and expanded to new markets and regions, it had to manage multiple servers across a number of on-premises data centers and the public cloud, which proved costly and time-consuming.
Its Allstars—the airline’s name for its employees—needed to easily access a number of legacy on-premises apps in addition to a growing number of SaaS apps. As a business, it also needed a more seamless integration between its HR system of record and its identity solution for user provisioning and life cycle management. Solving these challenges with its existing on-premises directory was simply not feasible.
AirAsia brought up its identity concerns with the Google Cloud team, and after a number of conversations, decided to deploy Cloud Identity, Google’s cloud-based Identity and Access Management solution, to help address the identity challenges it was facing.
The airline chose Cloud Identity for a number of reasons—first, it was eager to move to the cloud as quickly as possible. Moving identity management to the cloud was a key enabler of this and the airline’s broader digital transformation. Managing identities from the cloud also enabled the airline to have a single identity and set of credentials for each employee, which they could use to access all the applications they need to be productive, both in the cloud and on-premises.
In addition, deploying Cloud Identity was a key step towards enabling the zero trust security model, which the airline felt was the best approach to strengthen its security posture and fight modern threats. Cloud Identity also integrated seamlessly with its existing technologies, which include not only Google Cloud products like Google Workspace and Chrome OS but also third-party tools like Citrix, Papercut, and others.
And finally, Cloud Identity offered significant cost and resource savings. With Cloud Identity in place, AirAsia’s IT department could spend less time worrying about managing multiple on-premises directory servers and and could instead focus on delivering value to Allstar employees.
Machine learning employed to increase ancillary revenue
In March 2018, AirAsia established the groundwork to use machine learning to optimize pricing for a range of services and began by using AI Platform to sort and predict demand for ancillary services such as baggage, seats, and meals. “By using AI Platform, we can sort based on data about history to predict the future,” says Kong Wei.
Dialogflow in wide use
With Google Cloud well established within the business, AirAsia is using Dialogflow, a voice and conversational interface development suite (and one of the core components of Contact Center AI) that enables businesses to create engaging AI powered voice- and text-based interfaces such as chatbots and voice apps—to streamline operations and reduce costs
“Tony Fernandes, our Group Chief Executive Officer, motivated us to create a range of metrics that would enable us to respond more quickly and efficiently to customers,” says Yuashini Vellasamy, Product Manager at AirAsia.
This resulted in the initial deployment of Dialogflow in AirAsia’s operational areas, from crew scheduling to internal business tasks.
With Dialogflow, AirAsia is able to provide pilots, crew, catering, and other teams with flight times, capacity and any other relevant information about their assignments. Another bot accepts medical certificates and updates from pilots and crew members unable to make rostered assignments and switches those assignments to other pilots and crews. “This improves our on-time performance and operational efficiency, as we are able to optimize the creation of crew schedules,” says Vellasamy.
AirAsia also uses Dialogflow to power a “safety bot” for airport ground staff. “We tried a lot of tools to encourage staff to advise us when they saw a safety issue—but employee usage was low,” says Vellasamy. “However, when we set up the safety bot, which asks users just four questions about an issue they have observed, we saw an increase in employee interaction, which helps ensure proper safety measures.
Beyond crew optimization and overall safety, AirAsia’s HR department in Asia uses a Dialogflow-powered bot to run a post-orientation engagement program for new employees. The bot follows up with employees about issues from parking to AirAsia’s onboarding buddy program, saving HR employees time from scheduling one-on-one meetings. Dialogflow also powered an employee satisfaction survey of 3,000 team members, resulting in a 30% increase in response rate compared to the previous year.
One of Dialogflow’s benefits is its language coverage, which the airline’s marketing team uses to target customers and prospects across a region with diverse languages and cultures. “For weekly campaigns and promotions, our sales and marketing teams simply update the campaign materials and the changes are reflected automatically in Dialogflow and consequently to users,” says Vellasamy. “This enables us to quickly scale and reach our customers globally.”
An upward trajectory
AirAsia is poised to reap further benefits from Google Cloud as its deployment matures. “On the data side, we’re more advanced, while on the application and programming side we have a long way to go,” says Kong Wei. “We’re on a trajectory upwards—we’re looking at a lot of new ideas and how to embrace and deploy them so we can further our data-first and digital airline agendas.”
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Don’t sweat the big stuff. Make it Google’s problem
Need to interpolate new time series data values over 5 billion rows? Don’t reach for python. Make that Google’s problem and do it in BigQuery.
Need to aggregate petabytes of geospatial data across arbitrary polygons and put it on a map for analysis? Make that Google’s problem and use BQ-GIS.
Great – your map was awesome and now we need it every hour. Roll your own Airflow server? Nope. Make that Google’s problem. You see the pattern.
Geotab, which operates in in the telematics space, applies instrumenting to vehicles to learn how to optimize fleet maintenance, routes and costs, and even find way to optimize city infrastructure.
Geotab has been a long-time customer of Google Cloud products. They leverage the entire GCP suite to empower data scientists to efficiently ingest, process, and analyze petabytes of IoT data.
One of the keys to their pace of innovation and growth has been to focus on their key competencies and partner with others to fill in the gaps.
For Geotab’s data scientists this means focusing on the data and model-building and whenever possible making the processing, storage, and orchestration, well Google’s problem.
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The True Story of How HotStar Broke a World-Record–Thanks to Firebase and Google BigQuery
Hotstar, India’s largest video streaming platform with 150 million monthly active users around the world, provides live-streaming of TV shows, movies, sports, and news on the go.
By using a combination of Firebase products together, Hotstar safely rolled out new features to its watch screen during a major live-streaming event without disrupting users, sacrificing stability, or releasing a new build. They also used Firebase with BigQuery to analyze their event data and reduce app startup time.
“We have an ambitious mission, but our engineering team is only a fraction of the size of most of our competitors. But we are still keeping up, and we are doing it with the help of Firebase,” says Ayushi Gupta, Android Engineer, Hotstar.
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Breakthroughs in artificial intelligence (AI) have captured the imaginations of business and technical leaders alike. The AI techniques underlying these breakthroughs are finding diverse application across every industry. Early adopters are seeing results, particularly encouraging is that AI is starting to transform processes in established industries, from retail to financial services to manufacturing.
However, an organization’s effectiveness in applying these breakthroughs is anchored in the basics: A disciplined foundation in capturing, preparing and analyzing data.
Data scientists spend up to 80% of their time on the “data wrangling,” “data munging” and “data janitor” work required well before the predictive capabilities promised by AI can be realized.
Capturing, preparing and analyzing data creates the foundation for successful AI initiatives. To help business and IT leaders create this virtuous cycle, Google Cloud has prepared a CIO’s guide to data analytics and machine learning that outlines key enabling technologies at each step. Crucially, the guide illustrates how managed cloud services greatly simplify the journey—regardless of an organization’s maturity in handling big data.
This is important because, for many companies, the more fundamental levels of data management present a larger challenge than new capabilities like AI. “Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies,” noted Oliver Wyman consultants Nick Harrison and Deborah O’Neill in a recent Harvard Business Review article (aptly titled If Your Company Isn’t Good at Analytics, It’s Not Ready for AI). “Like it or not, you can’t afford to skip the basics.
Building on new research and Google Cloud’s own contributions to big data since the beginning, this guide walks readers through each step in the data management cycle, illustrating what’s possible alongside examples.
Specifically, the CIO’s guide to data analytics and machine learning is designed to help business and IT leaders address some of the essential questions companies face in modernizing data strategy:
- For my most important business processes, how can I capture raw data to ensure a proper foundation for future business questions? How can I do this cost-effectively?
- What about unstructured data outside of my operational/transactional databases: raw files, documents, images, system logs, chat and support transcripts, social media?
- How can I tap the same base of raw data I’ve collected to quickly get answers as new business questions arise?
- Rather than processing historical data in batch, what about processes where I need a real-time view of the business? How can I easily handle data streaming in real time?
- How can I unify the scattered silos of data across my organization to provide a current, end-to-end view? What about data stored off-premises in the multiple cloud and SaaS providers I work with?
- How can I disseminate this capability across my organization—especially to business users, not just developers and data scientists?
Because managed cloud services deal with an organization’s sensitive data, security is a top consideration at each step of the data management cycle. From data ingestion into the cloud, followed by storage, preparation and ongoing analysis as additional data flows in, techniques like data encryption and the ability to connect your network directly to the Google Cloud must reflect data security best practices that keep data assets safe as they yield insights.
Wherever your company is on its path to data maturity, Google Cloud is here to help. We welcome the opportunity to learn more about your challenges and how we can help you unlock the transformational potential of data.
How AI and Analytics in EdTech are Living Upto the Hype
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Over the last year, COVID-19 presented unforeseen challenges for practically every type of business and organization—including schools, colleges, and universities. For educational institutions, the pandemic was an unapologetic agent of acceleration, shifting one billion learners from in-person to online learning within two months.
The rapid transition to online learning exposed many schools’ lack of readiness for the new online learning environment. It also widened the learning equity gap for students, with fewer than 40% of students from low-income families having access to the tools required for remote learning.
For those who do have online access, today’s students expect everything from engaging and collaborative digital learning experiences to skills-based training for their roles in the future workforce. Expectations are also high for 24×7 multi-channel tech support across all learning devices, applications, and platforms.
In these remarkable times, education technology companies have an important role to play in supporting academic institutions and students. Indeed, this is already happening, as the EdTech (Educational Technology) market is nearly tripling, with total global expenditures expected to reach $404 billion by 2025. However, the success of these EdTech companies depends on their performance in a number of areas, including:
- Content and products: How quickly can they generate new content and react to learner needs with new products to additional markets for broader adoption?
- Personalization: How effectively can they leverage artificial intelligence (AI) to provide a personalized experience to all types of learners?
- Trust and security: How trusted and secure are their services when educational organizations are suffering the highest number of data breaches since 2005?
Here are a few examples of how EdTech companies are successfully using AI and analytics to capture this opportunity and transform their businesses:
- Build better products: iSchoolConnect is an online platform that lets students explore schools, courses, and countries where they might study, and makes higher education admissions accessible to students around the globe. The company leverages AI services to help educational institutions optimize their academic operations by accelerating admission processing by greater than 90%, while saving significant costs.
- Launch in new markets faster: Classroom creativity tools provider Book Creator uses AI APIs to enhance accessibility and improve the learner experience. “The broad suite of intelligent APIs enables us to deliver richer experiences, faster and more easily, without having to be experts in machine learning, drawing recognition, map embeds, or other areas,” says VP of engineering Thom Leggett.
- Scale businesses securely: Using DevOps and CDN [content delivery network] services, Chrome browser recording extension creator Screencastify was able to support eight times growth in users overnight amid the COVID-19 pandemic, while maintaining consistent total cost of ownership. These technologies helped the company rapidly scale operations in response to the overnight increase in demand from consumers and assure student data privacy and security on a budget. “We know this is just the beginning, as more educators rely on technology to deliver richer, more interactive curricula to students,” says CEO James Francis.
- Provide personalized learning and support: Smart analytics and AI can provide personalized support and recommendations for students, forecast demand, and predict shifts in learners’ preferences. Online learning platform Mindvalley uses cloud-based tools to understand and make decisions based on user activity and leverage machine learning to predict behavior.
Google Cloud is partnering with many of these leading EdTech companies, as well as industry-leading consortiums like Ed-Fi and Unizin, to standardize educational common data models and best practices for more agile and cost-effective integration of EdTech into existing environments.
The education landscape is changing rapidly, and EdTech has a major role to play as institutions adapt to the massive shift in learners’ preferences and expectations. We’re committed to empowering EdTech companies with the tools and services they need to help expand learning for everyone, anywhere.
Watch our Spotlight session with EdTechX to learn more.
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AirAsia Flies High With Data Analytics and AI
AirAsia’s vision is simple: allow everyone to fly. Founded in 2001, the airline and sister company AirAsia X have grown to service 150+ destinations in 25 markets, using 274 aircraft to operate 11,000+ weekly flights from 23 hubs across the region. While the airline is known as a provider of
