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Today during its Google Cloud Next 2021 conference, Google unveiled a range of data-focused products including Intelligent Product Essentials and enhancements to Vertex AI, BigQuery, Contact Center AI (CCAI), and DocAI. The new analytics and industry solutions are designed to simplify how organizations derive value from data, Google says — whether they’re developing a new product or enhancing existing ones.

AI adoption and analytics are rising during the pandemic, with 20% of companies claiming they’ve boosted their usage of business analytics compared with the global average. But while 97% of execs say data science is “crucial” to maintaining profitability, several major challenges stand in the way. A Dremio report found that only 22% of data leaders have realized a return on investment in data management in the past two years.

“The focus on intelligent products that Google Cloud is [launching] provides a digital option for [customers],” IDC group VP Kevin Prouty said in a statement. “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.”

Intelligent Product Essentials

Intelligent Product Essentials aims to assist manufacturers in developing hardware products. With it, they’re able to deliver AI-enabled devices that can update over-the-air and provide insights using analytics in the cloud, according to Google.

Intelligent Product Essentials can be used to create personalized customer experiences — for example, a chatbot that contextualizes responses based on product status and customer profiles. The service can also deploy updates to products in the field and gather performance insights, as well as evolve capabilities over time with monetization opportunities.

Intelligent Product Essentials predicts parts and service issues, detecting operating thresholds, anomalies, and failures so it can proactively recommend service using AI. Customers can leverage the offering to connect and ingest raw or time-series product telemetry from various device platforms to support over-the-air updates. In addition, Intelligent Product Essentials lets developers build companion apps that work on smartphones, tablets, and computers using a prebuilt API that incorporates product and security, device registration, and app behavior analytics.

“Intelligent Product Essentials [can] manage, update and analyze fleets of connected products via APIs,” Google wrote in a blog post. “[Companies can] create new features or capabilities for [their] products using AI and machine learning … [and] integrate data sources such as enterprise asset management, enterprise resource planning, customer relationship management, systems and others.”

Vertex AI, BigQuery, and Spark

Google introduced Vertex AI, a managed AI platform, in May at Google I/O 2021. Today, it’s expanding the service with Vertex AI Workbench, a user experience to build and deploy AI models faster, accelerating time-to-value for data scientists and their organizations.

Data scientists spend the bulk of their time cleaning and organizing data, according to a 2016 survey conducted by CrowdFlower. In a recent Alation report, a majority of respondents (87%) pegged data quality issues as the reason their organizations failed to implement AI. That’s perhaps why firms like Markets and Markets anticipate that the data prep industry, which includes companies that offer data cataloging and curation tools, will be worth upwards of $3.9 billion by the end of 2021.

Whereas Vertex AI is designed to help companies accelerate the deployment and maintenance of AI models, Workbench focuses specifically on integrating data engineering capabilities into the data science environment. Workbench incorporates Dataproc, BigQuery, Dataplex, Looker, and other Google Cloud services, facilitating the ingestion and analysis of data from a single interface.

“Delivered through managed notebooks, these capabilities help data scientists rapidly build workflows and perform the coordination, transformations, security, and machine learning operations, all within Vertex AI,” Google wrote.

On the BigQuery side, Google is making generally available BigQuery Omni, which allows businesses to analyze data across Google Cloud, Amazon Web Services, and Microsoft Azure. The managed, cross-cloud analytics solution helps to answer questions and share results from a single pane of glass across datasets, complementing Google’s Dataplex service (which will be generally available this quarter) to make data accessible to more analytics tools.

Google also today announced a preview of Spark on Google Cloud, which the company claims is the world’s first autoscaling and serverless Spark service for Google Cloud. It allows data engineers, data scientists, and data analysts to use Spark from their preferred interfaces, writing apps and pipelines that autoscale without manual infrastructure provisioning or tuning.

Looker and Spanner

To complement the rest of its data-focused offerings, Google is continuing to make Cloud Spanner, its fully managed, relational database, available to customers via a PostgreSQL interface (in preview). The interface supports several popular PostgreSQL data types and SQL features, allowing schemas and queries built against the PostgreSQL interface to be ported to another Postgres environment.

Beyond this, Google debuted new integrations with Looker that it says will allow customers to “operationalize analytics” and more effectively scale deployments. Tableau customers and Connected Sheets users will soon be able to leverage Looker’s semantic model, with the Connect Sheets integration launching in preview by the end of the year. Looker’s new solution for CCAI will help to contextualize support calls coming in to enterprise call centers. And the forthcoming Looker Block for Healthcare NLP API, which is compatible with the Fast Healthcare Interoperability Resources (FHIR), will provide health care providers, payers, and pharma companies access to insights from unstructured medical text from clinical sources.

Google Earth Engine

Touching on the geospatial, Google unveiled Google Earth Engine on Google Cloud, which makes Google Earth Engine’s catalog of over 50 petabytes of satellite imagery and geospatial datasets available for analysis. Google says that Google Cloud customers will be able to integrate Earth Engine with BigQuery, Google Maps Platform, and Google Cloud’s AI technologies, giving data teams “a way to better understand how the world is changing and what actions they can take” — from saving energy costs to understanding business risks and serving customer needs.

Investments in “green” practices aren’t just beneficial for the environment — they make business sense. According to a 2017 study on corporate social responsibility, 87% of consumers have a more positive image of companies that support social or environmental issues. Moreover, 87% say they’d buy a product with a social and environmental benefit, and 88% would more loyal to a company that supports those efforts.

“For over a decade, Earth Engine has supported the work of researchers and nongovernmental organizations from around the world, and this new integration brings the best of Google and Google Cloud together to empower enterprises to create a sustainable future for our planet and for your business,” Google wrote.

CCAI and DocAI

Google Cloud’s CCAI, which offers AI-powered virtual agents and other features, entered general availability in 2019, while the company’s AI-powered document processing service DocAI rolled out in April. Now, the two services are each gaining new features in CCAI Insights and Contract DocAI. CCAI Insights provides out-of-the-box and custom data modeling techniques, and Contract DocAI — now in preview — brings features purpose-built for contract lifecycles and processing.

Over the past several years, businesses have increasingly turned to cloud-based contact centers to address budding customer service challenges. The pandemic accelerated that move — service conveniences were put in place out of necessity, which gave customers more options for interacting with companies. For example, 78% of contact centers in the U.S. now intend to deploy AI in the next 3 years, according to Canam Research. And research from The Harris Poll indicates that 46% of customer interactions are already automated, with the number expected to reach 59% by 2023.

CCAI Insights uses AI to mine raw contact center interaction data for actionable information, regardless of whether that data originated with a virtual or human agent. It provides out-of-the-box analytics on customer conversations including Smart Highlighters, which automatically highlights important conversation moments such as when an agent authenticates or a customer confirms that their issue has been resolved. Meanwhile, integration with Google’s Cloud Natural Language Processing (NLP) identifies positive or negative sentiment and labels various entities within conversations by types, including date, person, contact information, organization, location, events, products, and media.

CCAI Insights — which can hand off calls and chats handled by Dialogflow and Agent Assist — also categorizes conversations with custom highlighters, which let customers defines rules, keywords, and natural language training phrases. Topic modeling — another capability — leverages NLP technologies so teams can create an AI model of their data to define the taxonomy of conversation drivers.

As for Contract DocAI, it taps NLP, knowledge graph technology, and optical character recognition to parse contracts for key terms like those involving start and end dates, renewal conditions, parties involved, contract type, venue, or service level agreements. It automatically discerns important terms and the relationships among them, potentially leading to faster and less expensive contract processing, Google claims.

“All of these new additions will help transform businesses by making the power of AI more accessible and more focused on achieving business outcomes,” Google wrote. “[The] announcements build on the momentum we’ve been seeing with our AI solutions in delivering business value to our customers.”

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