Apache and Dataflow Help with Real-time Indices Processing for Financial Institutions
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Financial institutions across the globe rely on real-time indices to inform real-time portfolio valuations, to provide benchmarks for other investments, and as a basis for passive investment instruments including exchange-traded products (ETPs). This reliance is growing—the index industry dramatically expanded in 2020, reaching revenues of $4.08 billion.
Today, indices are calculated and distributed by index providers with proximity and access to underlying asset data, and with differentiating real-time data processing capabilities. These providers offer subscriptions to real-time feeds of index prices and publish the constituents, calculation methodology, and update frequency for each index.
But as new assets, markets, and data sources have proliferated, financial institutions have developed new requirements. Financial institutions will need to quickly create bespoke and frequently updating indices that represent a specific actual or theoretical portfolio, with its unique constituents and weightings.
In other words, existing index providers and other financial institutions alike will need mechanisms for rapid creation of real-time indices. This blog post’s focus—an index publication pipeline collaboratively developed by CME Group and Google Cloud—is an example of such a mechanism.
The pipeline closely approximates a particular CME Group index benchmark, but with far greater frequency (in near real time vs. daily) than its official counterpart. It does so by leveraging open-source models such as Apache Beam and cloud-based technologies such as Dataflow, which automatically scales pipelines based on inbound data volume.
Machine learning’s production problem
In the past decade, advances in AI toolchains have enabled faster ML model training—and yet a majority of ML models are still not making it into production. As organizations endeavor to develop their ML capabilities, they soon realize that a real-world ML system is comprised of a small amount of ML code embedded in a network of complex and large ancillary components. Each component brings its own development and operational challenges, which are met by bringing a DevOps methodology to the ML system, commonly referred to as MLOps (Machine Learning Operations). To apply ML to business problems, a firm must develop continuous delivery and automation pipelines for ML.
This index publication collaboration is instructive because it demonstrates MLOps best practices for just such a pipeline. One Apache Beam pipeline, suited for operating on both batch and streaming data, extracts insights and packages them for downstream consumers. These consumers may include ML pipelines that, thanks to Apache Beam, require only one code path for inference across batch and real-time data sources. The pipeline is run inside Google Cloud’s Dataflow execution engine, greatly simplifying management of underlying compute resources.
But the collaboration’s value is not constrained to the ML and data science realm. The project shows that consumers of the Apache Beam pipeline’s insights may also include traditional business intelligence dashboards and reporting tools. It also demonstrates the simplicity and economy of cloud-based time series data such as CME Smart Stream, which is metered by the hour, quickly and automatically provisioned, and consumable at a per-product-code (not per-feed) level.
A focus on real-time processing for financial services
To illustrate the above points, the collaboration applies data engineering and MLOps best practices to a financial services problem. We chose the financial services domain because many financial institutions do not yet have real-time market data processing or MLOps capabilities today, owing to a significant gap on either side of their ML/AI objectives.
Upstream from ML/AI models, financial institutions often experience a data engineering gap. For many financial institutions, batch processes have sufficiently addressed business requirements. As a result, the temporal nature of the time series data underlying these processes is deemphasized. For example, the original purpose of most trade booking systems was to capture a trade and ensure that it found its way to the middle and back office for settlement. It was not built with ML/AI in mind, and its underlying data therefore has not been packaged for consumption by ML/AI processes.
And downstream from ML/AI models, financial institutions often encounter the aforementioned “ML production problem.”
As ML/AI becomes ever more strategic, these two gaps have left many financial institutions in a conundrum—unable to train ML models for lack of properly packaged time series data, and unmotivated to package time series data for lack of ML models. By recreating a key energy market index using open-source libraries and cloud-based tools, this collaboration demonstrates that for the financial services domain a solution to this conundrum is more accessible today than ever.
Creating a new index
We modeled our new index after one of CME Group’s many index benchmarks. The particular index expresses the value of a basket of three New York Mercantile Exchange—listed energy futures as a single price. Today, CME Group publishes the index at the end of the day by calculating the settlement price of each underlying futures contract, and then weighing and summing these values.
While CME Group does not currently publish this index in real time, this collaboration aims to create a near real-time solution leveraging Google Cloud capabilities and CME Group market data delivered via CME Smart Stream. However, in order to publish the value so frequently—every five seconds, with 40-second publish latency—this collaboration’s pipeline has to solve a number of challenges in near-real time.
First, the pipeline must process sparse data from three separate trades feeds in memory to create open-high-low-close (OHLC) bars. More specifically, for five-second windows for each of the three front-month (and sometimes second-month) energy contracts, a bar must be produced. This is solved by using the Apache Beam library to implement functions which, when executed on Dataflow, automatically scale out as input load increases. The bars must be time-aligned across the underlying feeds, which is greatly simplified by Beam’s watermark feature. And for intervals in which no tick data is observed, the Beam library is used to pull forward the last value received, yielding perfect gap-free bars for downstream processors.
Second, the pipeline must calculate volume-weighted average price (VWAP) in near real-time for each front-month contract. The VWAP calculations are also written using the Beam API and executed on Dataflow. Each of these functions requires visibility of each element in the time window, so the functions cannot be arbitrarily scaled out. Nonetheless, this is tractable because their input—OHLC bars—is manageably small.
Third, the pipeline must replicate CME Group’s specific settlement price methodology for each contract. The rules specify whether to use VWAP or another source as price, depending on certain conditions. They also specify how to weigh combinations of monthly contracts during a roll period. The pipeline again encapsulates these requirements as an Apache Beam class, and joins the separate price streams at the correct time boundary.
The end result is a new stream publishing bespoke index data to a Google Cloud Pub/Sub topic thousands of times daily, enabling AI models as well as traditional industry index usage, dashboards, and other tools to assist real-time decision making. The stream’s pipeline uses open source libraries that solve common time series problems out-of-the box, and cloud-based services to reduce the user’s operational and scaling burden.
The importance of cloud-based data
The promise of cloud-based pipeline execution services cannot be realized using legacy data access patterns, which often require market data users to colocate and configure servers and network gear. Such patterns inject expense and scaling complexity into the pipeline’s overall operation, diverting resources from the adoption of MLOps best practices. Instead, a newer, cloud-based access pattern—in which resources subscribe to data streams inexpensively, rapidly and programatically—is necessary.
In 2018, CME Group identified the customer need for accessible futures and options market data. CME Group collaborated with Google Cloud to launch CME Smart Stream, which distributes CME Group’s real-time market data across Google Cloud’s global infrastructure with sub-second latency. Any customer with a CME Group data usage license and a Google Cloud project can consume this data for an hourly usage fee, without purchasing and configuring servers and network gear.
CME Smart Stream met this index pipeline’s requirements for cost-effective, cloud-based streaming data, but this is just one use case. Since the launch of a CME Smart Stream offering on Google Cloud, globally dispersed firms have adopted the solution. For example, Coin Metrics has been using the offering to better inform its customers in the crypto markets. According to CME Group, Smart Stream has become popular with new customers as the fastest, simplest way to access CME Group’s market data from anywhere in the world.
Adapt the design pattern to your needs
By combining cloud-based data, open-source libraries, and cloud-based pipeline execution services, we created a real-time index using the same constituents as its end-of-day counterpart. Additionally, financial institutions will find this approach addresses many other challenges—real-time valuation of a large set of portfolios; benchmark creation for new ETPs; or external publication of new indices.
Give it a try
This approach is available to help you meet your organization’s needs. Please review our user guide, whose Tutorials section provides a step-by-step guide to constructing a simple Apache Beam pipeline to generate metrics on streaming data in real-time, and connecting a new data source to the pipeline. We’ll be discussing this topic in CME Group’s webinar End-to-End Market Data Solutions in the Cloud at 10:30 am ET on June 16th.
Leading Verve Group’s CX Innovation with Google Cloud Vertex AI
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Verve Group is an ecosystem of demand and supply technologies fusing data, media, and technology to deliver results and growth to both advertisers and publishers – no matter the screen or location, no matter who, what, or where a customer is. Classifying massive amounts of this unstructured data at scale is the first step in helping to surface relevant, high-quality content to users—and that’s where natural language processing (NLP) comes in.
Verve Group uses the NLP API from Google Cloud’s Vertex AI to fetch data for their internal content classification quality verification and as an additional source for building categorization models. By leveraging the NLP API’s Content Classification models, which are now generally available and offer Google’s latest large language model (LLM) technology, Verve Group powers classification through an updated and expanded training data set with over 1,000 labels and support for 11 languages (Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch join previously-available English).
Verve Group has been using Google Cloud’s NLP API since day one, because of both the ease of implementation and the quality compared to competing NLP products. With documentation that is “comprehensive and self-explanatory,” the NLP API “allows for fast adoption and implementation, from test models all the way to production,” said Rami Alanko, GM of Verve Group.
Leveraging the NLP API has facilitated Verve Group’s fast go-to-market motions by enabling its customers to quickly discover and classify new content. “Operating on a global level with tens of different regions and languages, we have still been able to maintain high quality for our product and high retention rates with our clients,” Rami shared. “In a recent client case, we achieved 82% improvement in CTR when optimized with content quality measurements enabled by the API. In another client case, we drove brand safety risk down to 0.16% from 4% thanks to classification quality. Along with the new functionalities of the Google NLP, I can only see this trend continuing to strengthen.”
Verve Group is excited to further expand their NLP use cases by leveraging the new Content Classification models, which have already helped them expand their classification inventory, improve the quality and performance of their quality verification for customers, and unlock new use cases for NLP. “Our classification model accuracy improved 41% using Google NLP as a verification partner,” said Rami.
Additionally, Verve Group is now using the API for metadata analysis on a large image database. “We browse the database and run the image metadata via our classification. This flow enables us to classify images reliably aligned with our standard classification. We pretty much use the same data flow for our runtime in-app textual content analysis, therefore allowing for close to real-time consumer engagement,” Rami added.
To learn more about how companies are leveraging NLP API from Google Cloud Vertex AI, click here, and to learn more about Google Cloud’s work with foundation models and generative AI, read The Prompt on Transform with Google Cloud.
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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.
redBus: Mastering Big Data with Google BigQuery
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In 2006, online travel agency redBus introduced internet bus ticketing in India, unifying tens of thousands of bus schedules into a single booking operation. (Think of it as Expedia for bus booking.) Using BigQuery, redBus crunches terabytes of booking and inventory data in mere seconds and at a fraction of the cost of other big data services. BigQuery also helps engineers fix glitches quickly, minimize lost sales, and improve customer service.
Challenge
Executives at the Bangalore-based redBus needed a powerful tool to analyze booking and inventory data across their system of hundreds of bus operators serving more than 10,000 routes. They considered using clusters of Hadoop servers to process the data but decided the system would take too much time to set up and would require a specialized staff to maintain it. It also would not provide the lightning-fast analysis they needed.
“It would have taken at least a couple of hours to analyze anything,” says Pradeep Kumar, a technical architect at redBus. “Crunching very large data sets would have been a day’s job. We needed something more powerful to get the real-time analysis we were looking for.”
Solution
Kumar and his colleagues learned about BigQuery and realized it was the right match for their data processing needs. The web-based service, which enables companies to analyze massive datasets using Google’s data processing infrastructure, is easy to set up and manage since its simple, SQL-like query language doesn’t require complex technology or specialized personnel. It also has low overhead costs.
The redBus team uses BigQuery as part of an intricate data collection and analysis process. Applications hosted on a range of servers continually pump information related to customer searches, seat inventory, and bookings into a centralized data collection system. Engineers upload the data to BigQuery, which provides answers to complex queries within seconds. For example, BigQuery helps redBus staff:
- Learn how many times customers searched for seats and found none or very few available, indicating more seats should be added to a route
- Investigate decreases in bookings and notify engineers if a technical problem is the cause
- Identify server problems by quickly analyzing data related to server activity
Results
BigQuery provides near real-time data analysis capabilities at 20% of the cost of maintaining a complex Hadoop infrastructure. Queries that would have required a day to analyze on a Hadoop framework take less than 30 seconds using Google’s web-based service.
“We explored several data analytics solutions. Nothing comes remotely close to the sheer power of Google BigQuery,” Kumar says. “It made large-scale data collection and crunching possible with little effort, which has translated to a significant business advantage.”
Google Cloud Platform results
- Analyzes data sets as large as 2 terabytes in less than 30 seconds using a simple, SQL-like language
- Saves time analyzing technical problems and customer booking trends
- Spends 80% less than they would have on a Hadoop infrastructure and avoids setting up and maintaining a complex infrastructure in-house
- Strengthens the company by improving customer service and engineering quality
The fast insights gained through BigQuery are also making redBus a stronger company. By minimizing the time it takes staff members to solve technical problems, BigQuery has helped improve customer service and reduce lost sales.
“Getting to the root of problems used to be really time-consuming,” Kumar says. “By the time we figured it out, customers might have given up. Now if there are booking problems, BigQuery helps us understand the reason right away. Choosing Google BigQuery was the right decision for our company.”
Google Cloud expands availability of enterprise-ready generative AI
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Generative AI continues to develop at a blistering pace, making it more important than ever that organizations have access to enterprise-ready capabilities to help them leverage this disruptive technology.
Harnessing the power of decades of Google’s research, innovation, and investment in AI, Google Cloud continues to make generative AI available with baked-in security, data governance, and scalability across the board.
To this end, last month, we announced the general availability of Generative AI support on Vertex AI, giving our customers the ability to access powerful foundation models from Google Research and tools for customizing and applying them.
Today we are announcing the general availability (GA) of four important foundation models for Vertex AI. These include Imagen, PaLM 2 for Chat, Codey, and Chirp. For each of these models, organizations can access APIs on Model Garden and do prompt design and tuning on Generative AI Studio.
- Imagen includes four key features:
- Image generation for creating studio-grade images at scale
- Image editing to edit generated or existing images via text prompts
- Image captioning for creating captions of images at scale
- Visual Question & Answering (VQA) for interacting with, analyzing, and explaining images
- PaLM 2 for Chat follows the general availability of PaLM 2 for Text in June
- Codey supports code generation, completion, and code chat
- Chirp supports multilingual Speech AI
We’re also announcing Multimodal Embeddings API in preview, which lets customers combine the power of Vertex AI’s generative AI models with their proprietary data, to generate embeddings, or interchangeable vector representations, of their text and image data. These capabilities can enable data science teams to deliver a variety of downstream tasks such as image classification, content recommendations, and visual search.
In this blog post, we’ll explore what your organization can do with these powerful models and how Vertex AI provides the enterprise-ready capabilities you can use to get up and running with generative AI.
Helping to drive enterprise value from Generative AI models
Powerful models are the foundation of generative AI, but the software, tools, and infrastructure that surround these models are equally important for enterprise adoption. Organizations face challenges not only accessing these models, but also integrating AI while maintaining protection over intellectual property, adhering to regulations around data security and privacy, and ensuring models and applications are safe to use. Many organizations also want to use generative AI without incurring large costs or managing huge clusters.
We help address these challenges head-on with Vertex AI’s platform capabilities for scalable application integration, purpose-built AI infrastructure, secure and private data customization, and responsible use of this technology.
Let’s see how each of these pillars can help your organization.
Access models to build production-ready generative applications
Vertex AI can make it easy to access foundation models, as today’s model announcements attest. While models are an inextricable part of generative AI, the software that helps enterprises use this technology is equally important—which is why Vertex AI also offers a range of tools for tuning, deploying, monitoring, and maintaining models, so you can build differentiated applications using your own data.
Turning to today’s announcements, in May we announced Imagen, our foundation model for image generation. Now, we are excited to announce Imagen is generally available with an allowlist (i.e., approved access via your sales representative), letting onboarded customers start using image generation and editing capabilities. Visual Q&A and Captioning for production workloads are also generally available for all customers. Visual Q&A provides new ways to engage with image-based data like retail products or image libraries. This new capability can give you answers to questions about an image, helping you analyze large amounts of data quickly, and it can even help the visually impaired understand images or graphs that they wouldn’t be able to otherwise. Captioning, meanwhile, can make it easy to generate relevant descriptions for your images. Captions can help with indexing and searching, as well as assigning image descriptions to product listings on eCommerce websites.
“Imagen is beginning to power key capabilities within Omni, Omnicom’s open operating system, that will enable 17,000+ trained and certified users to create audience-driven customized images in minutes. Imagen has been instrumental in offering a scalable platform for image generation and customization. Integrating it into our platform allows us to expand the scope of audience-powered creative inspiration, at a scale that wasn’t previously possible,” said Art Schram, Annalect Chief Product Officer at Omnicom. “We’re starting to adopt the latest features like styles and fine tuning, and engineering data-driven prompts. We look forward to continuing to provide our users relevant visual inspiration in a responsible way.”
“The latest improvements in Imagen’s product preservation capabilities are a perfect match for Typeface’s focus on personalized AI for brands,” explained Vishal Sood, Head of Product at Typeface. “By combining Google Vertex AI’s Imagen with Typeface’s brand-personalized AI, we are able to help enterprises to create 10x personalized content in a fraction of time.”
Google Shopping recently built an application called Product Studio using Imagen on Vertex AI. Product Studio can enable merchants to create rich product images quickly and easily, at a fraction of the time it takes to do professional product photo shoots. “We’re excited about the feedback we’re getting from merchants in our early pilots, who say that Product Studio, which leverages Imagen on Vertex AI, helps them generate and publish lifestyle product photos directly to their product catalogs,” says Jeff Harrell, Google’s Senior Director of Product Management for Merchant Shopping.
Announced in May, PaLM 2 is a family of models that power dozens of Google products, including Bard and Duet AI in Google Cloud. With the PaLM 2 for Chat model, now generally available, you can leverage Google’s PaLM’s variety of abilities for multi-turn chat applications, such as shopping assistants, customer support agents, and more.
ThoughtSpot, provider of a widely-adopted business intelligence platform, is using PaLM 2 to build a new feature in ThoughtSpot for Google Sheets called “AI Explain,” which can instantly generate explanations of charts, visuals, and anomalies, and will launch new conversational AI and ML-enabled predictive forecasting capabilities into its analytics platform.
With Codey, your organization’s developers can accelerate a wide variety of coding tasks, helping to empower them to work efficiently and close skills gaps. The model enables not only code completion and code generation capabilities, but also chat to help with debugging, documentation, learning new concepts, and more. Since launching in preview in May, we’ve added additional programming languages including Go, Google Standard SQL, Java, Javascript, Python, and Typescript. We’ve also improved the quality of code responses and increased serving capacity, enabling your developers with the right tools to enter the era of generative engineering.
“Security and privacy are key to incorporating AI into the software development lifecycle,” said David DeSanto, Chief Product Officer at GitLab. “GitLab leverages Vertex AI to deliver new, AI-powered features with a privacy-first approach, including the ability to run our own models and leverage Codey foundation models built on top of PaLM 2. The GitLab DevSecOps platform empowers organizations to harness the benefits of AI for faster software delivery, while ensuring their data, intellectual property, and source code are protected.”
Originally released in May in preview, Chirp is a version of our 2 billion-parameter speech model, which was trained on millions of hours of audio and supports over 100 languages. Chirp achieves 98% accuracy on English and relative improvement of up to 300% in languages with less than 10 million speakers. Whether the use case involves customer support, transcriptions, or voice control, Chirp can help your organization communicate with customers and constituents inclusively, by engaging audiences in their native languages.
Last but not least, our Multimodal Embeddings API, now in preview, can unlock an array of new applications, such as image and text-based recommendations, by enabling the processing of text and images interchangeably. This capability complements our Text Embeddings API, which became generally available in June, and remains a recommended choice for those with fully text-based use cases. Multimodal Embeddings API makes it possible to categorize images and text together and can be crucial for use cases like retail recommendation systems that can provide relevant outputs from both images of products and text descriptions.
Match generative AI with infrastructure
Beyond access to models and tools for building generative AI apps, you need infrastructure to make sure your apps can scale and reliably perform — ideally without running into daunting compute costs or management overhead that distracts your technical talent from building innovative products. Google Cloud offers the choice and power to run smaller models running finite tasks at the lowest latency levels, as well as to run large models capable of cutting-edge experiments.
As our large language model customers are looking to scale up their projects and applications using our models, they often need assurances that their requests will be serviced with acceptable performance. This is especially critical for delivering real-time applications where customer service is paramount. Starting in August, Vertex AI will support provisioned, dedicated generative AI capacity that can deliver guaranteed throughput. This feature can be especially beneficial to customers who have a high volume of sustained workloads.
Leverage generative AI while protecting data and privacy
One capability enabled by Google Cloud is the ability to customize models using your own data. Vertex AI can help customers keep their data protected, secure, and private. When a company tunes a foundation model in Vertex AI, private data, model outputs, and prompts can be kept private, and they are never used in the foundation model training corpus. We recently published a whitepaper, “Adaptation of Large Foundation Models,” which outlines how we help protect customer data.
Auditability and compliance are essential to helping ensure the security and privacy of customer data. We also engage in comprehensive GDPR privacy efforts, including our transparency commitments for customer data usage and the support for our customer’s Data Protection Impact Assessments (DPIAs). Now, we’re excited to support HIPAA compliance for many of our generally available models on Vertex AI, so that healthcare and life science customers with whom we have a Business Associate Agreement can run workloads with Protected Health Information (PHI) data on Google Cloud.
Innovate responsibly
Our AI Principles put beneficial use, user safety, and avoidance of harms above business outcomes and are embedded in how we develop our AI products. We’ve conducted extensive reviews on our generative AI products to identify potential risks and have developed guardrails to mitigate these impacts. For example, to address concerns around safety, we’ve implemented safety filters for bias, toxicity, and other harmful content. We also equip our customers with the tools they need to help reduce risk within their applications and provide recommendations to help navigate responsible AI.
Bring the power of generative AI to your organization
With both a wide selection of foundation models and extensive, enterprise-grade platform capabilities, Vertex AI continues to unlock ways for your business or organization to access foundation models, tune them on your proprietary data, and leverage them for differentiated apps and digital experiences. To take the next step, visit our product page or reach out to our sales representatives to gain access to our latest capabilities.
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Using Google BigQuery for Marketing Analytics: How the7stars Saved Time and Money Using Google Cloud and Matillion
“Businesses that integrate multiple sources of customer and marketing data significantly outperform other companies in terms of sales, profits, and margin. They also had dramatically higher total shareholder returns.”
-HBR Study
For most marketers, this is not news. The challenge is in bringing together multiple silos of customer data—from CRM, EPR, POS, social, online transactions, etc—and gleaning intelligence.
According to Google Cloud, marketing teams use as many as 30+ tools to track customers, but most exist in isolation.
“If you want to unlock the power of your data, you need a customer data platform, not just new tools,” says Andrea Russell, Program Manager, Cloud for Marketing, Google Cloud.
Marketers should ask themselves:
- What are the top 3 data silos that could be combined to get a better view of the customer’s journey?
- What new customer insight could be unlocked by combining customer data?
- How can I connect audience insights to media activation and drive better performance?
In this video, you’ll learn how Google Cloud simplifies the process of bringing multiple silos of data together easily (no IT help needed!), and how it enables marketing teams to query large sets of data in seconds—as opposed to hours using non-Google Cloud platforms.
You’ll also find out how the7Stars, the UK’s largest Independent Media Agency, used the Google Cloud platform to overhaul its reporting and data visualization approach—which entailed extracting data from multiple tools and bringing it together on a spreadsheet—saving hundreds of hours in work.
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