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.

Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities. 

However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features. 

That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones. 

With Intelligent Products Essentials, manufacturers can:

• Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
• Manage and update products over-the-air: Deploy updates to products in the field,  gather performance insights and evolve capabilities over time with monetization opportunities.
• Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.

In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:

• Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
• Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
• Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
• AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.

Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.

Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve. 

“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”

Customers adopting Intelligent Products Essentials

GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:

• Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
• Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
• Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.

“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.https://www.youtube.com/embed/zaWAJN8aKOw?enablejsapi=1&

Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences. 

“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1. 

Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.

“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap

Intelligent Product Essentials is available today. To learn more, visit our website.

It’s 2022 and nanosatellites, NFTs, and autonomous cars that deliver your pizza are in full force. In a world where people rely on simple technology to untangle complex problems, companies must deliver simple experiences to be successful in today’s landscape. For many cloud providers this means enabling tightly integrated data offerings that simplify the data delivery process without losing sight of the sophisticated needs of the modern data consumer. 

But while the name of the game is helping companies reach informed decisions from their data simpler and faster, what about the data practitioners – data analysts, data engineers, database administrators, developers, etc – who use these cloud data tools and technologies everyday? To proactively stay ahead of data cloud market trends in 2022 should data practitioners invest their time in specializing their data cloud skill sets (e.g. go deep in, say, data pipelining skills) or instead invest their time generalizing their data cloud skill sets (e.g. growing proficiencies in a mix of data analytics, databases, AI/ML, and more domains)? 

Skill deep or wide with data – that is the question

For Abdul Razack, VP, Solutions Engineering, Technology Solutions and Strategy at Google Cloud, the answer is a bit of both. 

“Data practitioners need to be broad in terms of their technology skills, but specialized with respect to the domain or domains in which they apply them. The reason why is because many things that used to be separate skill sets are now converging – like business analytics, streaming, machine learning, data pipelines, and data warehousing. Data practitioners need to be able to implement end-to-end workflows that solve specific business problems using skills from each category.”

It’s true, thousands of customers are choosing Google’s data cloud because it offers a unified and open approach to cloud that enables their practitioners to break down silos, begin and end projects without leaving the data platform, and innovate faster across their organization. 

The data practitioners who mirror Google data cloud’s frame of mind of being smart and agile across data domains in their skilling and learning will reap the benefits of solving more nuanced problems – building out internet-scale applications, fine tuning smart processes with analytics and AI, constructing data meshes that make product building simple, etc – at a larger scale than they would if they specialized in just one or two areas alone.

“Of course at the end of the day it depends on what tools a data practitioner is using to complete their workflows. There’s only so much you can learn and skills you can develop when you’re using limited tools. Growing data proficiencies across the board is made a lot easier when you’re using a data platform like BigQuery to address all these needs. BigQuery eliminates the choices you have to make – for instance you don’t have to choose between streaming data and data at rest, batch and realtime, or business intelligence and data science. This freedom gives data professionals a huge advantage when they’re building their skill sets and taking on more complex projects.” -Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud

Knowing your value is half the battle when upskilling

While some experts think technology is the limiting factor of whether or not you can even go wide or go deep in the first place, others like Google Cloud’s Head of Data and Analytics Bruno Aziza purport that it also depends on who you are, who you wish to be, and what investments your company is making to ensure you can become that person. 

“If you wish to set yourself up to be a Chief Data Officer, then you’ll want to understand how technologies fit together across your data estate first” said Aziza. “Only after you feel like you’re the go-to ‘data person’ can you then decide which part of the technology stack you want to double-down on.”

But technology isn’t everything. Aziza notes, “Make sure you focus on the  business impact that your data work provides.  You want to spend as much time as you can with your business counterparts to understand their business goals and challenges. The Harvard Business Review provides great guidance on how to succeed as a Chief Data Officer.”

Even if you don’t have your sights set on a C-suite role, both Aziza and Razack contend that the number one skill data practitioners should tackle in 2022 is actually a broad and perhaps abstract one: develop and exercise the curiosity to solve problems with a data-driven strategy. 

That is, today’s data practitioners should always be interested in educating themselves in the industry and continually upskilling in something. And their employers should also be invested in helping practitioners develop those interests, most likely through exposure to learning materials, engaging in career conversations, subsidized courses, or incentives attached to pursuing a new certification or skill.  

“Every industry is going through a digital transformation and the ability to identify what data to collect, how to prepare the data, and how to derive insights from it is critical. Therefore, the ability to find business challenges and formulate a data-driven approach to address those problems is the most important skill to have.” Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud.

Take the example of the “Data Mesh” I just wrote about in VentureBeat.  You’ll find 3 types of attitudes towards this new concept. There are Disciples who encourage continued learning only from the source – like the author of a new book or the creator of a theory. There are Distractors who tell you that new skills, trends, and technologies are fake news. And there are Distorters like vendors who will sell you one easy fix solution. But it’s the data practitioner who needs to proceed with caution when interacting with all three types  and forge their own path to discovering the truth when they’re learning and building skills. And for better or worse, this comes with trial and error, experimentation, and an eagerness to grow relative to where they began.”

Ready to start data upskilling? Start here.

For those interested in keeping up their data curiosities, check out our Data Journeys video series. Each week Bruno Aziza investigates a new authentic customer’s data journey – from migrating to cloud or  building a data platform to carrying out new data for good initiatives. Learn how they did it, their data dos and don’ts, and what’s next for them on their journey. These videos include a flavor of both specializing your data competencies and broadening your data competencies. 

For those interested in deepskilling, connect with Google’s data community at our upcoming virtual event: Latest Google Cloud data analytics innovations. Register and save your spot now to get your data questions answered live by GCP’s top data leaders and watch demos from our latest products and features including BigQuery, Dataproc, Dataplex, Dataflow, and more.

If you have any questions or need support along your learning journey – we’re here for you! Sign up to be a Google Cloud Innovator, and join the Google Cloud Data Analytics Community.

Vertex AI launched with the premise “one AI platform, every ML tool you need.” Let’s talk about how Vertex AI streamlines modeling universally for a broad range of use cases. 

The overall purpose of Vertex AI is to simplify modeling so that enterprises can fast track their innovation, accelerate time to market, and ultimately increase return on ML investments. Vertex AI facilitates this in several ways. Features like Vertex AI Workbench, for example, speed up training and deployment of models by five times compared to traditional notebooks. Vertex AI Workbench’s native integration with BigQuery and Spark means that users without data science expertise can more easily perform machine learning work. Tools integrated into the unified Vertex AI platform, such as state of the art pre-trained APIs and AutoML, make it easier for data scientists to build models in less time. And for modeling work that lends itself best to custom modeling, Vertex AI’s custom model tooling supports advanced ML coding, with nearly 80% fewer lines of code required (compared to competitive platforms) to train a model with custom libraries. Vertex AI delivers all this while maintaining a strong focus on Explainable AI. 

Yet organizations with the largest investments in AI and machine learning, with teams of ML experts, require extremely advanced toolsets to deliver on their most complex problems. Simplified ML modeling isn’t relegated to simple use cases only.

Let’s look at Vertex AI Neural Architecture Search (NAS), for instance. 

Vertex AI NAS enables ML experts at the highest level to perform their most complex tasks with higher accuracy, lower latency, and low power requirements. Vertex AI NAS originates from the deep experience Alphabet has with building advanced AI at scale. In 2017, the Google Brain team recognized we need a better way to scale AI modeling, so they developed Neural Architecture Search technology to create an AI that generates other neural networks, trained to optimize their performance in a specific task the user provides. To the astonishment of many in the field, these AI-optimized models were able to beat a number of state of the art benchmarks, such as ImageNet and SOTA mobilenets, setting a new standard for many of the applications we see in use today, including many Google-internal products. Google Cloud saw the potential of such a technology and shipped in less than a year a productized version of the technique (under the brand AutoML). Vertex AI NAS is the newest and most powerful version of this idea, using the most sophisticated innovation that has emerged since the initial research.

Customer organizations are already implementing Vertex AI NAS for their most advanced workloads. Autonomous vehicle company Nuro is using Vertex AI NAS, and Jack Guo, Head of Autonomy Platform at the company, states, “Nuro’s perception team has accelerated their AI model development with Vertex AI NAS. Vertex AI NAS have enabled us to innovate AI models to achieve good accuracy and optimize memory and latency for the target hardware. Overall, this has increased our team’s productivity for developing and deploying perception AI models.” 

And our partner ecosystem is growing for Vertex AI NAS. Google Cloud and Qualcomm Technologies have collaborated to bring Vertex AI NAS to the Qualcomm Technologies Neural Processing SDK, optimized for Snapdragon 8. This will bring AI to different device types and use cases, such as those involving IoT, mixed reality, automobiles, and mobile.

Google Cloud’s commitments to making machine learning more accessible and useful for data users, from the novice to the expert, and to increasing the efficacy of machine learning for enterprises are at the core of everything we do. With the suite of unified machine learning tools within Vertex AI, organizations can take advantage of every ML tool they need on one AI platform. 

Ready to start ML modeling with Vertex AI? Start building for free. Want to know how Vertex AI Platform can help your enterprise increase return on ML investments? Contact us.