Johnson & Johnson Increases it’s Ability to Find Highly Qualified Staffers for Business Critical Roles by 41% with Easy-to-Use AI
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Job seekers can often feel lost or disconnected—like the right opportunity is out there, but they don’t know where or how to look. Employers face a similar challenge when trying to attract the right candidates. Many companies, especially large enterprises, face a talent shortage across a range of critical roles.
For global companies like Johnson & Johnson (J&J), their online career website is an important recruiting tool. It’s the “front door” for talent that could make a vital difference in the company’s future and drive innovation for years to come.
However, career sites are often underutilized. If a job seeker doesn’t find a good job match with a quick search, they will likely move on. Too often, that represents a lost opportunity for both the company and the job seeker that could have been avoided with better search results.
“Partnering with Jibe and using Cloud Talent Solution for our career site allows us to do a much better job matching opportunity to talent on a very large scale.”
—Sjoerd Gehring, Global VP of Talent Acquisition, Johnson & Johnson
While J&J receives approximately 1 million applications for 25,000 positions each year, the percent of applicants that were highly qualified for open positions was low.
Although the company always has a variety of open jobs on its career site, it noticed that even when strong matches existed between online job seekers and available positions, search results often didn’t highlight or even display the right opportunities. The user interface wasn’t intuitive enough, and job seekers couldn’t easily find their ideal positions.
As J&J began to reevaluate recruiting to take a more relationship-centric and digitally-driven approach, the company began working with Jibe, a career-site solutions provider.
Jibe introduced J&J to Cloud Talent Solution, which uses machine learning to better match job listings with job seekers’ interests and qualifications. Using Cloud Talent Solution, companies can build a compelling career-site search experience that helps candidates easily find the jobs most relevant to them. With smarter job searches and recommendations, J&J improved the effectiveness of its career site in just a few weeks.
“Jibe and Google make it easy for a large company to make a real difference in the candidate experience without investing a lot of time, money, or internal resources,” says Sjoerd Gehring, Global VP of Talent Acquisition at Johnson & Johnson. “Now that we’re using Cloud Talent Solution, our career site search results are exponentially better.”
Transforming job searches with better matches
Cloud Talent Solution better connects job seekers with jobs, because it understands the nuances of job titles, descriptions, industry jargon, and skills, matching job seeker preferences with relevant listings based on sophisticated classifications and relational models. It helps decipher job seeker queries and employer job postings, removing the manual effort of optimizing job content for search.
By using the Jibe platform to integrate Cloud Talent Solution with its career site, job seekers are more easily finding what they’re looking for and J&J is filling business critical roles more efficiently.
Since integrating Cloud Talent Solution, J&J has seen a 41% increase in high-quality job applicants per search and a nearly 45% increase in click-through rate on its career site.
“Partnering with Jibe and using Cloud Talent Solution for our career site allows us to do a much better job matching opportunity to talent on a very large scale,” adds Sjoerd. “We’re able to take a more personal approach and really connect with job seekers, which is a win.”
“Today’s job seekers expect a prospective employer’s career site to work like the other cloud services they use. Using Google’s machine learning and artificial intelligence, we can help customers like J&J get better search results and return jobs that candidates are more likely to apply to.”
—Joe Essenfeld, Founder & CEO, Jibe
Connecting people with opportunities
J&J is now offering job seekers experiences in line with what they have come to expect as consumers—searching for a job should be as easy as searching for flights, restaurants, products, and other services. Because candidates are familiar with the experience, their level of interaction and engagement goes up, creating a larger pipeline of qualified candidates and filling jobs faster.
“Today’s job seekers expect a prospective employer’s career site to work like the other cloud services they use,” says Joe Essenfeld, Founder & CEO at Jibe. “Using Google’s machine learning and artificial intelligence, we can help customers like J&J get better search results and return jobs that candidates are more likely to apply to.”
A new digital revolution for recruiting
J&J continues to work with Jibe and Google to offer new features which make its career site even more effective. By offering job seekers a transformative, engaging experience, J&J is a more attractive and visible employer, increasing the value of its brand. It’s also continuously improving its recruiting process with end-to-end visibility and feedback from interactions with a million people every year.
“Transforming our career site with Jibe and Cloud Talent Solution directly impacts our ability to attract high-quality talent and hire those candidates faster,” adds Sjoerd. “Lots of people are looking for their dream job, and if it’s here at J&J, we want them to find it quickly and easily.”
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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.
Streamline Your Business Processes with Google Cloud’s Custom Document Splitter
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Businesses rely on processing an inflow of documents to drive processes and make decisions. Many such documents are combined into a single file. For example, a loan application may have a driver’s license, paystub, W2, bank statement, and other document types within a single file. The complexity of handling many document types within a single file makes it difficult for businesses to manage at scale.
At Google Cloud, we’re committed to solving these challenges with continued investment in our Document AI solutions suite which offers machine learning products for document processing and insights. Document AI Workbench helps users quickly build ML models with world-class accuracy, trained for their specific use cases. In February 2023, we launched the Custom Document Extractor (CDE) in General Availability (GA) to help users extract structured data from documents in production use cases. In March 2023, we launched the Custom Document Classifier (CDC) in GA to help automatically classify document types. Today, we announce the newest feature of Document AI Workbench, Custom Document Splitter (CDS) in GA to help users automatically split and classify multiple documents within a single file.
CDS provides tangible business value to customers by helping them sort and classify documents. For example, businesses can validate if they have all the needed documents from an applicant. Furthermore, individually classified documents enable businesses to better automate downstream processes, including selecting the proper storage, analysis, or processing steps based on the document type. The efficiencies enabled by CDS helps businesses lower their document processing time and cost.
Benefits of splitting and classification models in Document AI Workbench
Document AI Workbench can save time and money by simplifying model training, from dataset management, to testing, to deployment. CDS helps businesses achieve higher automation rates to scale processes while lowering costs.
Sean Earley, VP of Delivery Services at Zencore said, “We completed a project for a large bank using Document AI Workbench to split, classify, and extract data from documents to automate Home Mortgage Disclosure Act reporting. Given the accuracy of the models we built, our client estimated increasing loan reporting coverage from 20% to 100% while eliminating thousands of errors per year, drastically reducing the operational cost of the bank’s compliance reporting procedures.”
Fabian Beckmann, Manager Artificial Intelligence & Data at Deloitte Consulting GmbH said, “By leveraging Document AI’s Custom Document Splitter, our client, Commerzbank, a large european bank, can effortlessly segment customer submissions tailored to their back-office requirements, significantly diminishing the need for extra manual sorting or routing. This integration paves the way towards seamless automation within the Document AI pipeline, delivering substantial business benefits.“
According to Kaïs Albichari – ML Tribe Tech Lead, G Cloud at IT services firm Devoteam, “Custom Document Splitter (CDS) has helped one of our clients in the financial services industry save significant time and improve data accuracy. By identifying which parts of documents they can discard and which they retain for entity extraction, CDS has helped the company automate its document processing tasks. The implementation resulted in a more efficient and streamlined workflow, freeing employees to focus on other tasks. Devoteam’s G Cloud team helped the company implement CDS and achieve these benefits.”
Frank Neugebauer, a Google Cloud Insurance Solutions Consultant, worked with a Fortune 100 insurance company and used CDS to create a model to split and classify millions of insurance documents with up to 98% accuracy. With this information, the insurer can better understand the nature of their unstructured data to inform business strategy, including volume for specific document types to inform extraction work. The customer considers this level of insight unprecedented in their 200+ year history.
How to use Custom Document Splitter
You can leverage a simple interface in the Google Cloud Console and a set of public APIs to prepare training data, create and evaluate models, deploy a model into production, and call an API endpoint to split and classify document types. You can follow the documentation for instructions to create, train, evaluate, deploy, and run predictions with models.
Import and prepare training data
To get started, import and label documents to train and evaluate an ML model.
To quickly build a training dataset, import single documents, one document per file, and bulk label them with the relevant document type. You can import one folder or multiple folders at once and choose the correct document type per folder. As shown in the next image, one import could have a folder with 200 bank statements, another folder with 200 W2s, another folder with 200 paystubs, etc., all of which are labeled at once while imported. Up to 30,000 documents and 100,000 pages can be inputted for training. This way, you can build a training dataset with hundreds of labeled documents per class in minutes. As always, if documents are already labeled using other tools, simply import labels with JSON in the Document format.
You can initiate training with a click of a button. Once you have trained a model, you can use it to automatically label documents added to your dataset, letting you quickly build robust test and training datasets to evaluate and improve model performance.
To accurately evaluate a CDS model, import files which contain multiple document types within the same file and assign them to the test dataset. Then, use a simple interface to define document boundaries and types.
The ground truth you label in the test dataset is used to evaluate splitting and classification predictions from the CDS model.
Going into production
Once a model meets accuracy targets, it’s time to deploy into production and call the API endpoint to split and classify document types.
Getting started with Document AI Workbench
Custom Document Splitter is publicly available in GA and ready to help customers automate document splitting and classification. Learn more via our Document AI Workbench web page, Document AI Workbench documentation or try it out in the Google Cloud Console.
Google Unveils New Cloud Region in Delhi NCR to Power India’s Digitization
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In the past year, Google has worked to surface timely and reliable health information, amplify public health campaigns, and help nonprofits get urgent support to Indians in need. Now, we are continuing to focus on helping India’s businesses accelerate their digital transformation, deepening our commitment to India’s digitization and economic recovery. To support customers and the public sector in India and across Asia Pacific, we’re excited to announce that our new Google Cloud region in Delhi National Capital Region (NCR) is now open.
Designed to help both Indian and global companies alike build highly available applications for their customers, the Delhi NCR region is our second Google Cloud region in India and 10th to open in Asia Pacific.
What customers and partners are saying
Navigating this past year has been a challenge for companies as they grapple with changing customers demands and economic uncertainty. Technology has played a critical role, and we’ve been fortunate to partner with and serve people, companies, and government institutions around the world to help them adapt. The Google Cloud region in Delhi NCR will help our customers adapt to new requirements, new opportunities and new ways of working, like we’ve helped so many companies do in the region:
- InMobi scaled a personalized AI platform to support 120+ million active users. “With the arrival of the Google Cloud Delhi NCR, InMobi Group sees the opportunity to continue closing the gap between our users and products,” says Mohit Saxena, Co-founder and Group CTO of Inmobi. “Glance, especially, has been serving AI-powered personalised content to over 120 million active users. We can’t wait to continue giving them truly meaningful experiences that are speedy, scale well, and are relevant to them, by expanding the use of our current tools working on Google Cloud with the opening of a new region.”
- Groww now supports a sizable user base. “Google Cloud provides great technology that enables us to build and scale infrastructure to millions of users, and the new Google Cloud region in Delhi NCR will continue to help more businesses and startups in India access powerful cloud-based infrastructure, products and services,” says Neeraj Singh, Co-founder and Chief Technology Officer, Groww.
- HDFC Bank is positioned for the future. “At HDFC Bank, we are harnessing technology platforms to both run and build the bank. As we progress to be future ready, the objective is to invest in future technologies that give us scale, efficiency and resiliency. Towards this the Google Cloud region in Delhi NCR will enable us to enhance our resiliency and help us in building an active-active design framework for our new generation applications on cloud,” says Ramesh Lakshminarayanan, CIO, HDFC Bank.
- Dr. Reddy’s Lab built a modern data platform with Google Cloud. “At Dr Reddy’s, we pride ourselves in helping patients regain good health, acting quickly to provide innovative solutions to address patients’ unmet needs and in accelerating access to medicines to people worldwide. Our Google Cloud-powered data platform is helping us realize these objectives and we welcome Google’s investment in the new Delhi NCR region as helping us and other businesses in India make further contributions to our social and economic future,” says Mukesh Rathi, Senior Vice President & CIO, Dr. Reddy’s Laboratories.
- “To survive the disruption caused by the pandemic and to succeed in the long term, organizations need to become digital natives, so they can be more agile, explore new business models and build new capabilities that boost resilience. A cloud-first strategy plays a key role in enabling businesses to do this,” said Piyush N. Singh, Lead – India market unit & lead – Growth and Strategic Client Relationships, Asia Pacific and Latin America, Accenture. “Harnessing the potential of cloud requires the right data infrastructure and this expansion by Google Cloud will undoubtedly help Indian enterprises in their digital transformation journeys.”
A global network of regions
Delhi NCR joins 25 existing Google Cloud regions connected via our high-performance network, helping customers better serve their users and customers throughout the globe. As the second region in India, customers benefit from improved business continuity planning with distributed, secure infrastructure needed to meet IT and business requirements for disaster recovery, while maintaining data sovereignty.
With this new region, Google Cloud customers operating in India also benefit from low latency and high performance of their cloud-based workloads and data. Designed for high availability, the region opens with three availability zones to protect against service disruptions, and offers a portfolio of key products, including Compute Engine, App Engine, Google Kubernetes Engine, Cloud Bigtable, Cloud Spanner, and BigQuery.
Supporting India’s recovery with training and education
Google and Google Cloud will also continue to support our customers with people and education programs. We’re investing in local talent and the local developer community to help enterprises digitally transform and support economic recovery.
Through the India Digitization Fund, we expanded our efforts to support India’s recovery from COVID-19—in particular, through programs to support education and small businesses. In addition to expanding internet access, and investments to help start-ups accelerate India’s digital transformation, we’ve grown our Grow with Google efforts. Businesses can access digital tools to maintain business continuity, find resources like quick help videos, and learn digital skills—in both English and in Hindi.
Helping customers build their transformation clouds
Google Cloud is here to support businesses, helping them get smarter with data, deploy faster, connect more easily with people and customers throughout the globe, and protect everything that matters to their businesses. The cloud region in Delhi NCR offers new technology and tools that can be a catalyst for this change. To learn more, visit the Google Cloud locations page, and be sure to watch the region launch event here.
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As a customer experience platform, Genesys helps clients nurture great relationships with customers, and creates seamless user journeys across all channels and devices. Its technologies are used by more than 10,000 companies in over 100 countries, making Genesys the top provider of its kind for 25 years in a row.
To improve data efficiency and communication across the company, Genesys turned to Analytics Pros, a digital analytics agency. Together, they used the power of Google Data Studio to transform the way teams across Genesys accessed and used data.
Communicating With Data
The marketing and product teams at Genesys had always been passionate about using data to optimize their user experience and marketing channels. But the problem was showing the data. The different offices and executives around the world had trouble logging in to view the data—and once they did, the reports were intimidating and confusing.
After learning more about the capabilities of Data Studio, Genesys decided to run a pilot project. Analytics Pros helped the company combine multiple data sets into self-service, fully-customizable dashboards on Data Studio. And it was a huge success. Regional teams were thrilled to have meaningful dashboards, and even executives started using and sharing reports.
How AI and Analytics in EdTech are Living Upto the Hype
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Over the last year, COVID-19 presented unforeseen challenges for practically every type of business and organization—including schools, colleges, and universities. For educational institutions, the pandemic was an unapologetic agent of acceleration, shifting one billion learners from in-person to online learning within two months.
The rapid transition to online learning exposed many schools’ lack of readiness for the new online learning environment. It also widened the learning equity gap for students, with fewer than 40% of students from low-income families having access to the tools required for remote learning.
For those who do have online access, today’s students expect everything from engaging and collaborative digital learning experiences to skills-based training for their roles in the future workforce. Expectations are also high for 24×7 multi-channel tech support across all learning devices, applications, and platforms.
In these remarkable times, education technology companies have an important role to play in supporting academic institutions and students. Indeed, this is already happening, as the EdTech (Educational Technology) market is nearly tripling, with total global expenditures expected to reach $404 billion by 2025. However, the success of these EdTech companies depends on their performance in a number of areas, including:
- Content and products: How quickly can they generate new content and react to learner needs with new products to additional markets for broader adoption?
- Personalization: How effectively can they leverage artificial intelligence (AI) to provide a personalized experience to all types of learners?
- Trust and security: How trusted and secure are their services when educational organizations are suffering the highest number of data breaches since 2005?
Here are a few examples of how EdTech companies are successfully using AI and analytics to capture this opportunity and transform their businesses:
- Build better products: iSchoolConnect is an online platform that lets students explore schools, courses, and countries where they might study, and makes higher education admissions accessible to students around the globe. The company leverages AI services to help educational institutions optimize their academic operations by accelerating admission processing by greater than 90%, while saving significant costs.
- Launch in new markets faster: Classroom creativity tools provider Book Creator uses AI APIs to enhance accessibility and improve the learner experience. “The broad suite of intelligent APIs enables us to deliver richer experiences, faster and more easily, without having to be experts in machine learning, drawing recognition, map embeds, or other areas,” says VP of engineering Thom Leggett.
- Scale businesses securely: Using DevOps and CDN [content delivery network] services, Chrome browser recording extension creator Screencastify was able to support eight times growth in users overnight amid the COVID-19 pandemic, while maintaining consistent total cost of ownership. These technologies helped the company rapidly scale operations in response to the overnight increase in demand from consumers and assure student data privacy and security on a budget. “We know this is just the beginning, as more educators rely on technology to deliver richer, more interactive curricula to students,” says CEO James Francis.
- Provide personalized learning and support: Smart analytics and AI can provide personalized support and recommendations for students, forecast demand, and predict shifts in learners’ preferences. Online learning platform Mindvalley uses cloud-based tools to understand and make decisions based on user activity and leverage machine learning to predict behavior.
Google Cloud is partnering with many of these leading EdTech companies, as well as industry-leading consortiums like Ed-Fi and Unizin, to standardize educational common data models and best practices for more agile and cost-effective integration of EdTech into existing environments.
The education landscape is changing rapidly, and EdTech has a major role to play as institutions adapt to the massive shift in learners’ preferences and expectations. We’re committed to empowering EdTech companies with the tools and services they need to help expand learning for everyone, anywhere.
Watch our Spotlight session with EdTechX to learn more.
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