Global human capital management (HCM) solutions provider Automatic Data Processing (ADP) recently took a bold step as part of its strategy to be on the “innovation offensive.” ADP’s digital transformation unit, Lifion, launched ADP’s next gen HCM, a system designed to model a dynamic modern workplace where work gets done in teams rather than traditional hierarchal structures. But the system’s original database solution used multiple self-managed databases—including relational databases like multimaster MySQL clusters—and wasn’t ideal for completing the complex queries that ADP needed to deliver advanced capabilities to its customers. Plus, the effort of managing those databases was a burden on the next gen HCM staff.
Having already used Amazon Web Services (AWS) for self-managed database provisioning, the company looked to AWS for a fully managed database solution, and it found one in Amazon Neptune, a fast, reliable graph database that makes it easy to build and run applications that work with highly connected datasets. This purpose-built, high-performance graph database engine is optimized for storing billions of relationships and querying the graph with milliseconds latency. By using Amazon Neptune, ADP’s next gen HCM cut costs by eliminating database licenses and increased staff productivity and time to market while delivering customers a unique set of customizable HR solutions.
We like app-level encryption in addition to database-level encryption. When we use Amazon Neptune, the data is already encrypted before it gets to the database, and then it’s encrypted again at rest.”
Chief Architect, ADP’s next gen HCM
ADP offers HCM software solutions for automating payroll, core human resources (HR), talent management, benefits, and workforce management for companies ranging from small businesses to global corporations. Since its founding in 1949, ADP has stayed on the cutting edge of HCM technologies, which led to the launch of next gen HCM. “Today there are many types of workplace structures, and traditional HCM systems have not embraced that,” says Zaid Masud, chief architect for ADP’s next gen HCM. The Dynamic Teams features of ADP’s next gen HCM aim to help organizations break out of the traditional workplace hierarchy by taking team members out of silos, improving engagement and performance, and creating a culture of connectivity.
Building such a solution required a database that could seamlessly manage an extensive network of complex data points. Yet ADP’s next gen HCM first launched using dozens of various self-managed databases with a microservices-driven architecture running on Amazon Elastic Compute Cloud (Amazon EC2), a service that provides secure, resizable compute capacity in the cloud. The collection of databases included relational databases like multimaster MySQL clusters, distributed key-value stores, and column family stores. “We have more than 200 microservices,” Masud explains, “and each domain has its own data storage needs. As you can imagine, this grew in complexity and became unmanageable very quickly.”
The company knew it needed a managed database solution to reduce staff workload. It also needed to move away from a relational database to a graph database. Querying relational databases is challenging and required the next gen HCM team to denormalize the data, or add redundant data, in order to speed retrieval, which wasn’t efficient. “When you start trying to ask relational databases complex questions like approval flows, it becomes pretty unwieldy,” explains Masud. In contrast, a graph database offered ADP’s next gen HCM the agile storage it needed. “Graph databases naturally represent your structure in the way it’s designed or visualized, which enables us to build much more dynamic queries.”
The next gen HCM team had a number of requirements for a graph database in addition to a fully managed service. It needed a low-code app development framework that would enable it to develop highly customizable HCM applications without writing code. “We needed to manage people, data, benefits, parallel integrations such as time and attendance, vacation balances, and time off,” explains Masud. “So many things must be customized at every level to account for client-specific needs, regional needs, and compliance needs.” ADP’s next gen HCM also wanted a graph database with open-source standards, which would help the team avoid lock-in. The team first heard about Amazon Neptune when it was announced at the 2017 AWS re:Invent conference. “We felt that Amazon Neptune was a slam dunk because our application was already using these open standards,” says Masud.
Amazon Neptune easily builds queries that efficiently navigate highly connected datasets, enabling the next gen HCM team to build applications that use ADP’s wealth of data to answer complex workplace questions for a variety of use cases. For example, a company that wants to internally fill an open position can use ADP’s next gen HCM to search for existing employees who satisfy the skill sets and requirements for the role. “Writing a query, plugging in criteria, and viewing a list of employees who qualify are things that Amazon Neptune is very well suited for,” says Lucky Jain, engineering manager, next gen HCM. “It can answer these questions and quickly return the data.” Users of ADP’s next gen HCM also can access a range of capabilities such as data reporting based on specific use cases and criteria-based authorization. This enables customers to build their own teams, or groups of employees, and then create authorization rules for that group, limiting or granting them permission to view information like salaries, personally identifiable information, and business plans. “It has given us broader use cases in our current features for customers that we never thought we could have accomplished in our early products,” says Jain. Key to this customization are next gen HCM’s low-code development platform and Amazon Neptune’s flexible query capabilities. “One of the really powerful things about our low-code app development platform is that it enables you to build no-code graph traversal queries,” adds Masud. “That’s what we use Amazon Neptune for.”
The migration to Amazon Neptune decreased next gen HCM’s total cost of ownership, eliminating the need to have skilled people operating database clusters 24/7. Now staff can focus on cloud infrastructure and site reliability engineering operations, enabling ADP’s next gen HCM to further grow its platform without adding additional staff. “We were self-managing everything from operating system–level things like patching, backups, and point-in-time restores to security vulnerabilities,” says Masud. “Spending less time on those things significantly improves our time to market.” ADP also avoids paying for database licensing and reduces spending on Amazon EC2. Amazon Neptune provides high availability using a minimum of two nodes compared to three with next gen HCM’s former solution. “We expect an increase in reliability and availability with Amazon Neptune, which means that we’re running less of an exposure risk.”
Amazon Neptune has multiple levels of security, including encryption at rest, which is important in securing next gen HCM’s sensitive data, such as personally identifiable information. On an encrypted Amazon Neptune instance, data in the underlying storage is encrypted, as are the automated backups, snapshots, and replicas in the same cluster. “We like app-level encryption in addition to database-level encryption,” says Masud. “With Amazon Neptune, sensitive data is already encrypted before it gets to the database, and then it’s encrypted again at rest.” Amazon Neptune also satisfies end users’ requirements for compliance with Service Organization Control and General Data Protection Regulation. “We’re very comfortable telling our customers that we are on AWS,” says Masud. “They can even register and download AWS Service Organization Control compliance reports on their own.”
ADP’s next gen HCM is exploring multitenancy using Amazon Neptune to better represent the structure of its customer data: currently, each customer has its own isolated graph. Masud says AWS has been very responsive to ADP’s needs: “AWS has a way of getting things out to market quickly and then refining that and iterating over that. We were able to give some direct service feedback to the Amazon Neptune team.” The company is also investigating serverless frameworks on AWS.
With the fully managed Amazon Neptune, ADP eliminated database licensing, reduced Amazon EC2 costs, and enabled its team to focus on core business operations rather than database maintenance. But most importantly, it was able to use the purpose-built graph database to power complex queries and deliver to its customers advanced HR applications that it wouldn’t have been able to otherwise.
Founded in 1949, ADP designs cutting-edge products, premium services, and exceptional experiences informed by data for HR, talent, time management, benefits, and payroll that enable people to reach their full potential.
As organizations develop their own internal ethical practices and countries continue to develop legal requirements, we are at the beginning of determining standards for ethical use of data and artificial intelligence (AI).
In the past 20 years, our ability to collect, store, and process data has dramatically increased. There are exciting new tools that can help us automate processes, learn things we couldn’t see before, recognize patterns, and predict what is likely to happen. Since our capacity to do new things has developed quickly, the focus in tech has been primarily on what we can do. Today, organizations are starting to ask what’s the right thing to do.
This is partly a global legal question as countries implement new requirements for the use and protection of data, especially information directly or indirectly connected to individuals. It’s also an ethical question as we address concerns about bias and discrimination, and explore concerns about privacy and a person’s rights to understand how data about them is being used.
What is AI and Data Ethics?
Ethical use of data and algorithms means working to do the right thing in the design, functionality, and use of data in Artificial Intelligence (AI).
It’s evaluating how data is used and what it’s used for, considering who does and should have access, and anticipating how data could be misused. It means thinking through what data should and should not be connected with other data and how to securely store, move, and use it. Ethical use considerations include privacy, bias, access, personally identifiable information, encryption, legal requirements and restrictions, and what might go wrong.
Data Ethics also means asking hard questions about the possible risks and consequences to people whom the data is about and the organizations who use that data. These considerations include how to be more transparent about what data organizations have and what they do with it. It also means being able to explain how the technology works, so people can make informed choices on how data about them is used and shared.
Why is Ethics Important in HR Technology?
Technology is evolving fast. We can create algorithms that connect and compare information, see patterns and correlations, and offer predictions. Tools based on data and AI are changing organizations, the way we work, and what we work on. But we also need to be careful about arriving at incorrect conclusions from data, amplifying bias, or relying on AI opinions or predictions without thoroughly understanding what they are based on.
We want to think through what data goes into workplace decisions, how AI and technology affect those decisions, and then come up with fair principles for how we use data and AI.
What Are Data Ethics Principles?
Ethics is about acknowledging competing interests and considering what is fair. Ethics asks questions like: What matters? What is required? What is just? What could possibly go wrong? Should we do this?
In trying to answer these questions, there are some common principles for using data and AI ethically.
As organizations develop their own internal ethical practices and countries continue to develop legal requirements, we are at the beginning of determining standards for ethical use of data and AI.
ADP is already working on its AI and data ethics, through establishing an AI and Data Ethics Board and developing ethical principles that are customized to ADP’s data, products and services. Next in our series on AI and Ethics, we will be talking to each of ADP’s AI and Data Ethics Board members about ADP’s guiding ethical principles and how ADP applies those principles to its design, processes, and products.
Read our position paper, “ADP: Ethics in Artificial Intelligence,” found in the first blade underneath the intro on the Privacy at ADP page.