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An API is an interface that allows other isolated interfaces to communicate with one another, by integrating all information into a single, shareable source. In the context of healthcare, such devices aggregate masses of patient, government, and research data from internal and external sources across one single pane of glass. The ramifications of this functionality are significant. Researchers that need data for detecting breast cancer or diabetes no longer need to travel from institutions to libraries for their sources. Instead, all they need to do is access one centralized hub. Hospital and healthcare managers can analyze live data for ML-programmed insights. They can also provide faster, more up-to-date patient information. Insurance providers can use these same analytics to analyze and adjust patient information in bulk. Stakeholders involved in patient care and billing can share medical records, reducing error and allowing hospital staff to focus on healing, rather than paperwork.This technology also makes a difference on the individual level. Patients can have safe and convenient access to their data for more informed health decisions, anytime, anywhere, mostly from their smartphones. Doctors and nurses can more easily access government and research data that might help them make smarter diagnoses. Application developers can work on one centralized platform, enabling them to implement institutional upgrades and updates with half the usual time and cost. Managers can adhere to changing HIPAA standards while driving innovation in healthcare. The Google Cloud Healthcare API The Google Cloud Healthcare API meets the latest HL7, DICOM, and FHIR standards, making it uncommonly secure. According to an IDC InfoBrief titled “The Role of Customer Experience Networks in Delivering Value-Based Digital Transformation,” one of the biggest issues healthcare APIs have is how they handle security and regulatory requirements. Healthcare APIs are more vulnerable to security issues and their ramifications than APIs from most other industries, since they deal with sensitive patient information, accessed by diverse stakeholders and integrated with third-party integrations.Google Cloud assures vendors that its API “protect[s] your healthcare data with security and privacy controls you can trust.” Its system uses REST-based APIs, making the system faster and more lightweight, with increased scalability, perfect for Internet of Things (IoT) applications, such as for patients (or providers) to access health records via their mobiles. McKesson Corporation and Google Cloud In 2019, McKesson selected Google Cloud as its preferred cloud provider. A Fortune 6 company, McKesson is a global leader in healthcare supply chain management solutions, retail pharmacy, community oncology and specialty care, and healthcare information technology. The company’s objectives were to “create and modernize next generation solutions to deliver better healthcare, one patient at a time.”To that end, McKesson adopted Google Cloud Platform’s managed services, along with healthcare-specific services that included the Google Cloud Healthcare API, to enhance its platforms and applications. The company transferred its on-premise resources to Google Cloud, using the Google Cloud Healthcare API to ingest, store, analyze, and integrate the company’s healthcare data across its cloud applications. It also used Google’s analytics function to make data-driven decisions for product manufacturing, specialty drug distribution, and pharmacy retail operations. “This partnership [with Google] will not only accelerate and expand our strategic objectives,” said Andrew Zitney, senior vice-president and CTO of McKesson Technology, “it will also help fuel next-generation innovation by driving new technologies, advancing new business models, and delivering insights.”Offerings like the Google Cloud Healthcare API will continue to impact the healthcare industry in various ways, but across potential industry use cases, data production overall will become more accurate and storage more secure and safe. In short, hospitals, healthcare systems, insurers, and life sciences companies have a better chance at increasing their productivity and efficacy with these API integrations. Extra Credit:
Challengers in the financial services industry—existing firms looking to innovate, start-ups looking to scale, and everyone in between—will gain an in-depth understanding of the banking and payments system from this Deep Dive. The recording from this Deep Dive includes:(1:15) Introduction to Simon Taylor and 11:FS (4:00) Introduction to banking as a service (BaaS) and its role across brands (7:20) Understanding the depth of service from BaaS API providers (13:10) How API providers enable focus on building user experience and expediting time to market (15:15) Embedding financial services into various customer experiences (17:25) Go-to-market requirements for launching FinTech products (20:10) Overcoming challenges between FinTech vendors and BaaS providers (21:20) Building finance operating systems (22:00) The four core issues and challenges: provider lock-in, geographic limits, flexibility vs. speed, and product configuration gaps (24:35) Open audience questionsFeatured in this session: Simon TaylorCo-Founder and Chief Product Officer, 11FS Simon Taylor is the Co-Founder and Blockchain Practice Lead at 11:FS. Simon has been immersed in the technology of financial services for as long as he’s been working. He is consistently voted one of the most influential people in Banking, Insurance, and Fintech by banks, his peers, and industry bodies. Simon led Blockchain Research and Development at Barclays. In his time there, Barclays became the first bank in the world to perform a live trade finance transaction over a Blockchain / DLT with a real customer attached. Today Simon advises governments, regulators, and some of the worlds largest banks, financial institutions, and corporations on how Blockchain and DLT will impact their business in the short, medium, and long term. Previously, Simon helped build the Barclays www.thinkrise.com program and held a number of roles in payments, banking, and the telco sector. Extra Credit11:FS Pulse Report 2022 Banking as a Service: the future of financial services 11:FS podcast Decoding: Banking as a Service - Episode 1 11:FS YouTube
Use cases for artificial intelligence (AI) are so many and varied that the meaning of the term itself can be hard to pinpoint. The Google Cloud Platform supports a host of products that make specific AI functions easy to apply to the problems they’re designed to solve. Vision AI is a cloud-based application designed to make computer vision applicable in a wide variety of cases. But what is computer vision, exactly?On December 8, 2021, C2C invited Eric Clark of foundational C2C partner and 2020 Google Cloud Partner of the Year SpringML to answer this question. Clark’s presentation, a C2C Deep Dive, offered an enriching explication of the concept of computer vision, as well as projections for its impact on the future of AI. Most notably, Clark used Vision AI to present multiple demonstrations of computer vision in action.To set the stage for these real-world applications, Clark offered a breakdown of the essential functions of computer vision:Next, Clark used real footage of traffic at a busy intersection to demonstrate how computer vision monitors this footage for incidents and accidents to calculate travel times:To showcase Vision AI’s video intelligence capabilities, Clark uploaded a video and applied different tags to demonstrate how computer vision recognizes and identifies individual elements of different images.Clark’s final demonstration was an in-depth look at several infrastructure maintenance use cases, starting with a look at how computer vision can be used to detect potholes and other impediments to safe road conditions:Clark’s demonstrations made clear that Vision AI is as user-friendly as it is powerful, and Clark made sure at the end of his presentation to invite attendees to make a trial account on the Google Cloud Platform and try out the API themselves. Alfons Muñoz (@Alfons), C2C’s North American Community Manager, echoed his encouragement. “It’s really easy to try it out,” he said.If you haven’t already, set up an account on the Google Cloud Platform and try using Vision AI for help with a current project, or even just for fun. Write us back in the community to let us know how it goes!
Machine Learning (ML) is a major solution business and technical leaders can use to drive innovation and meet operational challenges. For managers pursuing specific organizational goals, ML is not just a tool: it’s a mindset. C2C’s community members and partners are dynamic thinkers; choosing the right products for their major projects requires balancing concrete goals with the flexibility to ask questions and adapt. With these considerations in mind, C2C recently invited Google Cloud Customer Engineer KC Ayyagari to host a C2C Deep Dive on The ML Mindset for Managers.Ayyagari started the session by asking attendees to switch on their cameras and then ran a sentiment analysis of their faces in Vision API:After giving some background on basic linguistic principles of ML, Ayyagari demonstrated an AI trained to play Atari Breakout via neural networks and deep reinforcement learning:To demonstrate how mapping applications can use ML to rank locations according to customer priority, Ayyagari asked the attendees for considerations they might take into account when deciding between multiple nearby coffee shops to visit:As a lead-in to his talking points about the ML mindset for managers, Ayyagari asked attendees for reasons they would choose to invest in a hypothetical startup he founded versus one founded by Google’s Madison Jenkins. He used the responses as a segue into framing the ML mindset in the terms of the scientific method. Startup management should start with a research goal, he explained, and ML products and functions should be means to testing that hypothesis and generating insights to confirm it:Before outlining a case study of using ML to predict weather patterns, Ayyagari asked attendees what kinds of data would be necessary to use ML to chart flight paths based on safe weather. Guest Jan Strzeiecki offered an anecdote about the flight planning modus operandi of different airports. Ayyagari provided a unique answer: analyzing cloud types based on those associated with dangerous weather events.The theme of Ayyagari’s presentation was thinking actively about ML: in every segment, he brought attendees out of their comfort zones to get them to brainstorm, just like an ML engineer will prompt it’s machines to synthesize new data and learn new lessons. ML is a mindset for this simple reason: machines learn just like we do, so in order to use them to meet our goals, we have to think and learn along with them.Are you a manager at an organization building or training new ML models? Do any of the best practices Ayyagari brought up resonate with you? Drop us a line and let us know! Extra Credit:
Michael Pytel (@mpytel), co-founder and CTO at Fulfilld, shares stories from the team’s wins and losses in building out this intelligent managed warehouse solution.The recording from this Deep Dive includes:(2:00) Introduction to Fulfilld (10:15) Natural Language Processing use case for warehouse guidance (11:40) Generating directions using Dijkstra’s algorithm (commonly used in mapping applications) to connect the shortest route between two points (13:10) Generating audio guidance for a custom map using Google Cloud Run and Text-to-Speech API (14:15) Using WaveNet to create natural-sounding, multi-language voices for text-to-speech scenarios (16:45) Building a digital assistant with Google Dialogflow Intent matching and other features Other use case examples of Google Dialogflow (21:30) Integrating voice while building applications on Flutter (22:35) Natural language alerts for warehouse operations (23:50) Big ideas: looking to the future of Fulfilld Other ResourcesWaveNet: A generative model for raw audio Google Cloud hands-on labs Google documentation: Creating voice audio files Build voice bots for mobile with Dialogflow and Flutter | Workshop The Definitive Guide to Conversational AI with Dialogflow and Google Cloud Find the rest of the series from Fulfilld below:
Application programming interfaces (APIs) simply connect apps. Think of them as a scalable application interface, with diverse code running on a server where all of that code needs to fit together perfectly for any particular application to work. Example: The Uber application comprises various interfaces: one for payment, another for calculating your current location, a third for handling the review you leave at the end of the trip, and so forth. Like a microservice, each API focuses on its aspect and “communicates” with other Uber APIs. When communication is fluid, and all the Uber APIs seamlessly interconnect, the Uber application works. API Design The first step in creating an API is to ask yourself: Why do you want to make that particular API - or why do you need it? What do you want it to accomplish? What’s your process of execution? API design is followed by API productization, which is the process of creating the actual API. An effective API is: Easy to use Easily understood or attractive in its simplicity Clear and concise, with “just enough” data to do its job. API ManagementThe larger and more complex your application, the more APIs you’re likely to have (think of microservices). All of these APIs need to be secured, observed, updated, scaled (as the enterprise or application grows), cataloged, and retired when no longer necessary. Some core components of API management are: API gateway, an API management tool that makes it easy for developers to design, develop, maintain, monitor, and secure APIs. API dashboard, a space where you can observe the health and usage of your APIs to troubleshoot issues. API catalog, a library of the relevant APIs for organizing, managing, and sharing these APIs with relevant developers and end-users. API SecurityHackers have a wonderful time with APIs. After all, applications such as Facebook or Tinder, which reveal your deepest confidences, are commonly used and are open to vulnerabilities, like excessive data exposure, incorrectly applied authentication mechanisms, and code injection.Most APIs are composed of either: SOAP (Simple Object Access Protocol) - A highly structured message protocol that uses built-in protocols, or Web Services Security, to shield APIs. Organizations with highly sensitive data prefer SOAP. REST (Representational State Transfer) - A more straightforward approach that uses HTTP/S and Transport Layer Security (TLS) encryption for security. Common API Terms Web API security - transfer of data through APIs that are connected to the Internet API Keys - requests made to an API which require a specific key for authentication GraphQL - a popular query language used for designing and developing effective APIs JSON - the language that the applications use to “talk” to one another Wrap-Up APIs)are simply applications that communicate with one another to answer your commands and give you access to required data. When it comes to Google, examples include Search, Gmail, Translate, or Google Maps. Google Cloud APIs help you create, deploy, and manage APIs on the Cloud through your favorite API language. Do you use APIs? Which are your favorites? Reach out and let us know!
From chatbots to predictive text, all kinds of applications are using AI to navigate language barriers and facilitate communication across different communities. Many of these applications focus on text, but there is more to language than written words. Sometimes even fluent speakers of a second language will experience challenges when communicating face-to-face with native speakers. One of the best ways to overcome these challenges is to practice pronunciation.Markus Koy (@MarkusK) is an IT projects analyst with 18 years of experience across various industries. He is also a native German speaker living in an English-speaking part of Canada, and a regular visitor to C2C’s AI and ML coffee chats, which are hosted in the U.S. Koy’s experiences working in English-speaking countries as a non-native English speaker inspired him to create thefluent.me, an AI-powered app that tests speech samples and scores them based on how well they correspond to standard English pronunciation.On thefluent.me, users record themselves reading samples of English text (usually about 400 characters long), and then post them either publicly or privately on the app’s website. Within about 30 seconds, the app delivers results, reproducing the text and indicating which words were pronounced well and which can be pronounced better. Even native English speakers may find that they can improve their pronunciation, sometimes even more so than someone who speaks English as a second language.We recently approached Koy with some questions about thefluent.me, Google Cloud products, and his experience with the C2C Community. Here’s what we learned: What inspired you to develop thefluent.me? Koy began working on thefluent.me after contributing to a research project with an international language school. As a second-language English speaker himself, he had already taken the International English Language Testing System; he had found pronunciation to be the hardest part of the process.“Immediate feedback after reading a text is usually only available from a teacher and in a classroom setting,” he says. Teachers only listen to a speaker’s pronunciation once, and will likely not provide feedback on every word. Tracking progress systematically is just not feasible in a classroom setting, and sometimes non-native speakers will feel intimidated when speaking English in front of other students.Koy continued his research on AI speech-recognition programs and also graduated from Google’s TensorFlow in Practice and IBM’s Applied AI specialization programs. He decided to build thefluent.me to help students struggling to overcome these challenges. What makes thefluent.me unique? There are many apps on the market for students studying English as a second language, and thefluent.me is not the only app of this kind that uses AI for scoring. However, apps combine different features to support distinct learning needs. Koy kept these concerns in mind when designing and building the following features for thefluent.me: Immediate pronunciation feedback: The application delivers AI-powered scoring for the entire recording and word-level scoring on an easy-to-understand scale. Immediate feedback on reading speed: Besides pronunciation, the application provides feedback on the reading speed for each word. Own content: Users can add posts they would like to practice instead of using content only published by platforms. They can immediately listen to the AI read their post before practicing. Progress tracking and rewards: Users can track their activities and progress. They can revisit previous recordings and scores, check their average score, and earn badges. Group learning experience: By default, user posts are not accessible to others. However, users can also make their posts public and invite others to try, or they can compete for badges. How do you use the Google Cloud Platform? Do you have a favorite Google Cloud product? Koy runs thefluent.me on App Engine Flexible. He likes how easy the deployment process is, especially when managing traffic between different versions. Two key Application Programming Interfaces (APIs) Koy is using are Speech-to-Text and Text-to-Speech, which Koy says allow the Wavenet voices to sound more natural. He also likes that both allow him to choose different accents for the AI speech. Koy is also using Cloud SQL and Cloud Storage, which he finds easy to integrate. What do you plan to do next? “There are many other items for horizontal and vertical scaling on my roadmap,” Koy assures us. He is planning to add additional languages and enhance the app’s group features. He has also been approached by multiple companies who want to use thefluent.me for education and training. Koy plans to publish APIs to accommodate these requests in the coming weeks. Why did you choose to join the C2C community? Like so many of our members, Koy joined the C2C community to meet people and collaborate, but his experience here has informed his work on thefluent.me beyond friendly conversation. Recently, a community member expressed to Koy that thefluent.me is an ideal tool to use when preparing for a job interview—a user can rehearse answers to interview questions to learn to pronounce them better. For Koy, this is not just nice feedback; it is also a use case he can add to his roadmap.Still, community itself is enough of a reason for Koy to return on a weekly basis. “Mondays are just not the same anymore without our AI and ML coffee chats,” he says.
This C2C Deep Dive was led by Sindhu Adini (@Sindhu Adini) , director of Google Cloud for HLS at SpringML, a C2C foundational platinum partner. Joining Sindhu from the Peerlogic team were CEO Ryan Miller (@ramill401) and Alex Maskovyak, engineering and product development executive.Peerlogic is an innovative provider of cloud communications, building better conversations and high production through the power of AI. Their products allow individuals to work more productively, teams to collaborate more freely, and organizations to better understand their data.The full recording from this session includes:(2:05) Speaker introductions (4:20) SpringML’s specializations and industry reach (6:50) An introduction to Peerlogic and how they are empowering dental practices with improved communications between staff and patients (12:05) Analyzing patient sentiment with AI and ML Adopting call center best practices and front desk assistance Identifying revenue leakage Benchmarking and understanding conversion opportunities (17:10) Overview of Peerlogic’s application (21:15) Google Cloud services and components used Choosing Google Cloud Spectrum of AI in the Google Cloud ecosystem Google Cloud Vertex AI for pre-trained APIs and end-to-end integration for data and AI (31:25) Architectural overview of the solution and model Data pipeline to ingest audio scripts and Google Cloud Speech-to-Text Enhanced augmentation of the solution using custom ML algorithms FireStore to authenticate AppEngine access only to Service Accounts (38:50) Key considerations for Machine Learning Identifying the business problem that needs to be solved How predictions are made Supervised learning (44:55) Custom patient call analysis modelWatch the full recording below: Connect with SpringML here in the C2C Community.
While cloud computing has come a very long way since the nascent days of Google App Engine, we’re still only beginning to understand the areas in which cloud technology can make the greatest impact in our lives. One industry that recently took steps toward a more technology-based model is healthcare and wellness, with the adoption of data repositories like Google Cloud Healthcare API for storing medical records and telehealth communication to make doctor’s visits and therapy appointments more accessible. Cloud computing has given providers options when it comes to communication, but the growing popularity of wellness and cognitive behavioral therapy (CBT) apps could soon lead to better data visualization and even personalized treatment when it comes to mental health. What Is an Evidence-Based Mental Health App? Even though it seems like there are apps for anxiety and depression of all shapes and sizes on the market today, the number of evidence-based mental health apps is still relatively small. That’s because, in order for an app to be considered evidence-based, it needs to meet certain requirements by the U.S. Food and Drug Administration or have one randomized clinical research study that supports its effectiveness, as reported by PsychCentral. An evidence-based mental health app would be the biggest improvement because it gives the patient and provider confidence in their diagnosis and treatment. This improvement also saves time in unnecessary diagnostic testing and consultations with specialists regarding knowledge gaps, essentially giving everyone a system that consists of tasks. Complexity ranges from reference retrieval to the processing of relative transactions, complex data mining, and rule-driven decision support systems. This gives all users a trusted, scientific embedded system to back up their mental health data. Useful Features of CBT Apps Many evidence-based mental health apps come with a variety of features that individuals can use to manage or track some area of mental health. From setting reminders for taking medication to deprogram negative thought tendencies, mental health and CBT apps come with a variety of features that can help users build awareness around their mental health. CBT apps have features that can improve our daily habits, willpower, and give us a growth system that improves our way of thinking. Not only do they provide a space for tracking systems, but they are also able to hold journaling and notes on the important impacts of daily habits, creating an overall location where the user has the power to destroy bad habits and start healthier new ones. Self-Management and Tracking One evidence-based mental health app called Medisafe allows users to set alerts to remind them when to take medication. While other CBT apps, such as Worry Knot by IntelliCare, actually use cognitive-behavioral principles like “tangled thinking” to teach users how to manage everyday worries and anxiety.Tracking and self-management can help users understand more about thought patterns and side effects from medication, all of which can help patients and doctors find a treatment path and clinical plan that works best for them, including being able to create therapy techniques for our health and build confidence. Apps for anxiety and depression have even created mindfulness techniques that help with meditation, quick mental start programs, and even SOS buttons if the user is feeling the need of urgency. Data and Analytics Users that want to manage their mental health through better sleep and routine exercise can use an app like Whoop to track their respiratory rate and sleep quality. Whoop is not a CBT or mental health app, but its ability to track patterns in sleep and recovery can help users zero in on the behavioral patterns that may be negatively impacting their health and, by extension, their mental health. Personalized Recommendations Other mental health apps, such as Breathe2Relax, equip users with recommendations for breathing exercises to soothe symptoms of PTSD, general anxiety, and more. Calm is an app made to enjoy listening to therapeutic music and has the ability to track and create sleep patterns. We can’t forget Mood Kit, which allows users to create customized journal entries for moods. With mental health apps expanding and becoming more specific, having many personalized touches bring a more manageable, enjoyable, and convenient way to manage our health. Current Limitations of CBT Apps While apps for anxiety and depression have grown in popularity, many apps are still created with little evidentiary support that they work. Having the ability to track and manage health with CPT Apps can seem like users have the world at their hands, but it's important to commit to oneself. We all need accountability, and we sometimes create more personalized relationships with a friend, family member, or provider, making an overall safe space to push forward. So, we still have to show up and put forth the effort. Being more self-reliant, CPT apps may also not be suitable for people with complex mental health needs or learning difficulties. Some critics actually argue that these apps only address current problems and very specific information that not much of the possibility of underlying causes of mental health is given, such as an unhappy home. Participating and involving with CPT apps can create and build more pressure to face fears, but it takes true honesty to involve oneself in things that eventually can change our lives for the better. How Evidence-Based Mental Health Apps May Improve Treatment and Patient Plans While we may still be in the beginning stages of understanding just how cognitive behavioral therapy apps meaningfully fit into the treatment of mental health, there are some early indications that a hybrid treatment plan could provide more mental health services to rural areas to bridge the mental health treatment gap. Treatment plans are a good place to start when wanting to improve one’s mental health. A mental health treatment plan creates teamwork between patient and provider, which can greatly enhance client engagement. Storing data in evidence-based resources makes the treatment for patient plans more trusted and heavily experienced. Goals, milestones, and timelines make it easier to store information. Providers are now able to know where you will go or maybe are headed. Having credible knowledge brings worldwide sources all in a digital space that is accessible and highly specific to the data captured by CPT apps. Evidence-based mental health apps will be a part of our evolving health systems, with lowering costs for healthcare, easy access to update a network of medical data, creating a more customized experience and road map to care for you. Extra Credit: https://psychcentral.com/blog/top-7-evidence-based-mental-health-apps#1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5897664/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381081/#:~:text=Published%20reviews%20have%20found%20that,including%20substantial%20heterogeneity%20across%20studies. https://riahealth.com/2019/10/15/mental-health-apps/ http://www.thecbtclinic.com/pros-cons-of-cbt-therapy
Yesterday, Google Cloud introduced request priorities for Cloud Spanner APIs in this blogpost. Today we’re happy to announce that you can now specify request priorities for some Cloud Spanner APIs. By assigning a HIGH, MEDIUM, or LOW priority to a specific request, you can now convey the relative importance of workloads, to better align resource usage with performance objectives. Internally, Cloud Spanner uses priorities to differentiate which workloads to schedule first in situations where many tasks contend for limited resources.Customers can take advantage of this feature if they are running mixed workloads on their Cloud Spanner instances. For example, if you want to run an analytical workload while processing DML statements, and you are okay with your analytical workload taking longer to run. In that case, you’d run your analytical queries at a LOW priority, signaling to Spanner that it can reorder more urgent work ahead if it needs to make tradeoffs.Check out the blogpost for more details, including using and monitoring request priorities.What do you think? Are you likely to use request priorities? What question do you have?Share your thoughts in the comments below.
Known as a prominent programmer and entrepreneur in the tech space, Andi Gutmans today serves as the General Manager and VP of engineering for databases at Google Cloud. He is responsible for overseeing a group whose goal is to support customers with their data journeys and with transforming their businesses.“It’s a three-step journey,” he said. “We take them through migration, modernization, and then transformation. The best part of what we do is being able to innovate on behalf of our customers.”Innovating is something Gutmans does well. He co-created PHP, the programming language that is the most widely used web language for creating dynamic web pages, and he also co-founded Zend Technologies, which continues to do much of the work in further developing PHP. Gutmans doesn’t shy away from new challenges. He instead thrives on finding solutions for them. “All customers want to eventually get to transformation,” he said. “But it’s not always easy to make the full leap in one step. I’m excited about the opportunity to partner with them on that journey and to really enable that transformation.”Watch the whole interview below.
This article was originally published on November 20, 2020.Hailed as one of the “Founding Fathers” of the internet for co-creating PHP, Andi Gutmans is just getting started. To discuss his new role at Google and the future of data, Gutmans joins C2C for a discussion in our sixth installment of our thought leadership series where we don’t hold back on both the fun and challenging questions. As a four-citizenship-holding and engineering powerhouse, Gutmans brings a global perspective to both tech and coffee creation.“I love making espresso and improving my latte art,” he mused. “I always say, if tech doesn’t work out for me, that’s where you’re going to find me.But, when he isn’t daydreaming about turning it all in to own a coffee shop and become a barista, he leads the operational database group as the GM and VP of engineering and databases at Google.“Our goal is building a strategy and vision that is very closely aligned with what our customers need,” he said. “Then, my organization works with customers to define what that road map looks like, deliver that, and then operate the most scalable, reliable, and secure service in the cloud.”It’s an enormous responsibility, but Gutmans and his team met the challenge to three steps: migration, modernization, and transformation. They accomplished this, even though they’ve never met in person—Gutmans started working at Google during the COVID-19 pandemic.Driven to support customers through their data journeys as they move to the cloud and transform their business, he digs into the how, the why, and more during the conversation, video above, but these are the five points you should know:Lift, Shift, TransformThe pandemic has changed the way everyone is doing business. For some, the change comes with accelerating the shift to the cloud, but Gutmans said most customers are taking a three-step journey into the cloud.“We’re seeing customers embrace this journey into the cloud,” he said. "They’re taking a three-step journey into the cloud. Migration, which is trying to lift and shift as quickly as possible, getting out of their data center. Then modernizing their workloads, taking more advantage of some of the cloud capabilities, and then completely transforming their business.”Migrating to the cloud allows customers to spend less time managing infrastructure and more time on innovating business problems. To keep the journey frictionless for customers, he and his team are working on a service called Cloud SQL. The service is a managed MySQL, PostgreSQL, and SQL server, for clarity. They also handle any regulatory requirements customers have in various geographies.“By handling the heavy lifting for customers, they have more bandwidth for innovation,” he said. “So the focus for us is making sure we’re building the most reliable service, the most secure service, and the most scalable service.”Gutmans described how Autotrader lifted and shifted into Google’s cloud SQL service and was able to increase deployment velocity by 140% year-over-year, he said. “So, there is an instant gratification aspect of moving into the cloud.”Another benefit of the cloud is auto-remediation, backups, and restoration. Still, the challenge is determining what stays to the edge and what goes into the cloud, and, of course, security. Gutmans said he wants to work with customers and understand their pain points and thought processes better.Modernizing sometimes requires moving customers off proprietary vendors and open-source-based databases, but the Gutmans team has a plan for that. By investing in partners, they can provide customers with assessments of their databases, more flexibility, and a cost reduction.Finally, when it comes to transformation, the pandemic has redefined the scope. A virtual-focused world is reshaping how customers are doing business, so that’s where a lot of Google’s cloud-native database investments have come in, such as Cloud Spanner, Cloud, BigQuery, and Firestore.“It's really exciting to see our customers make that journey,” he said. “Those kinds of transformative examples where we innovate, making scalability seamless, making systems that are reliable, making them globally accessible, we get to help customers, you know, build for [their] future,” he said. “And seeing those events be completely uneventful from an operational perspective is probably the most gratifying piece of innovating.”Gutmans adds that transformation isn’t limited to customers that have legacy data systems. Cloud-native companies may also need to re-architect, and Google can support those transformations, too.AI Is MaturingGartner stated that by 2022, 75% of all databases would be in the cloud, and that isn’t just because of the pandemic accelerating transformation. Instead, AI is maturing, and it is allowing companies to make intelligent, data-driven decisions.“It has always been an exciting space, but I think today is more exciting than ever,” Gutmans said. “In every industry right now, we’re seeing leaders emerge that have taken a digital-first approach, so it’s caused the rest of the industries to rethink their businesses.”Data Is Only Trustworthy if It’s SecureData is quickly becoming the most valuable asset organizations have. It can help make better business decisions and help you better understand your customer and what’s happening in your supply chain. Also, analyzing your data and leveraging historicals can help improve forecasting to better target specific audiences.But with all the tools improving data accessibility and portability, security is always a huge concern. But Gutmans’ team is also dedicated to keeping security at the fore.“We put a lot of emphasis on security—we make sure our customer’s data is always encrypted by default,” he said.Not only is the data encrypted, but there are tools available to decrypt with ease.“We want to make sure that not only can the data come up, [but] we also want to make it easy for customers to take the data wherever they need it,” Gutmans said.Even with the support through the tools Gutmans’ team is working to provide customers, the customer is central, and they have all the control.“We do everything we can to ensure that only customers can govern their data in the best possible way; we also make sure to give customers tight control,” he said.As security measures increase, new data applications are emerging, including fraud detection and the convergence of operational data and analytical systems. This intersection creates powerful marketing applications, leading to improved customer experience.“There are a lot of ways you can use data to create new capabilities in your business that can help drive opportunity and reduce risk,” Gutmans said.Leverage APIs Without Adding Complexity There are two kinds of APIs, as Gutmans sees it: administration API and then API for building applications.On the provisioning side, customers can leverage the DevOps culture and automate their test staging and production environments. On the application side, Gutmans suggests using the DevOps trend of automating infrastructure as code. He points to resources available here and here to provide background on how to do this.But when it comes to applications, his answer is more concise, “if the API doesn’t reduce complexity, then don’t use them.”“I don’t subscribe to the philosophy where, like, everything has to be an API, and if not...you’re making a mistake,” he added.He recommends focusing on where you can gain the most significant agility benefit to help your business get the job done.Final Words of WisdomGutmans paused and went back to the importance of teamwork and collaboration and offered this piece of advice:“Don’t treat people the way you want to be treated; treat people the way they want to be treated.”He also added that the journey is different for each customer. Just remember to “get your data strategy right.”
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