How to Get Data to Propel Your AI


Understanding the Data That Powers AI-driven HCMs

No artificial intelligence is effective without the right data feeding it. Learn how to gather the data that matters to build algorithms that make a difference.

Artificial intelligence (AI) is taking over the world. No, not in the Hollywood Doomsday way, but rather in the helpful way for which it was intended. Research suggests that nearly 40% of enterprises have deployed some sort of AI technology and that spending on the technology is expected to double in just the next year and a half. 


Companies of all sizes, across all industries, sectors, and geographies are using it for a range of purposes — powering autonomous vehicles, enhancing customer service, and even revolutionizing healthcare. It’s also becoming an increasingly important part of the human resources sphere, too, as human resources technology providers are building AI and machine learning into mission-critical systems like human capital management (HCM) platforms.


The potential for AI revolutionizing a stalwart industry like HR is immense. In the future, it’ll help transform the way companies recruit new talent, engage with employees, and optimize budgets for the perfect balance between performance and cost — provided it’s properly deployed and has access to a steady stream of clean, accurate, and complete data.  


AI’s dirty little secret

When most people think of artificial intelligence, they envision an all-powerful computer that magically replicates and will eventually replace the tasks humans are paid to do right now. 


However, AI isn’t magic, it’s a complex mesh of high-powered computers that execute sophisticated algorithms capable of helping teams predict future trends, automating specific trends, and (hopefully) get progressively better at doing both. 


But the dirty little secret about AI is that it isn’t an autonomous technology. It doesn’t work on its own or determine what or how it’s going to achieve certain goals. Rather, AI relies on data — and lots of it — which presents a few unique challenges for today’s businesses to take full advantage of the awesome computing power next-generation technologies have.


You need a lot of data 

There are debates about the exact amount of data required to power an artificial intelligence or machine learning (ML) model, but depending on the application it could be thousands or millions of data points. 


For AI to function properly, the data required to “train” the machine needs to be robust, expansive, and come from many sources to create the most complete model possible and enable particular calculations and outcomes.


Fortunately, most organizations have a ton of data they can use. Enterprise Resource Planning (ERP) and human capital management (HCM) platforms are data-rich environments that, when used properly, can help accelerate HR’s shift from administrative tasks to improving workforce productivity, improve efficiency, and make more informed people decisions that directly impact the bottom line.


You need good data, not just a lot of it

Having a lot of data isn’t very helpful in building effective AI models if the data is “dirty.” Dirty data is any record with any kind of error, such as typos, duplication, or is otherwise outdated, incorrect, or incomplete. Any time you’re merging data from multiple sources, the risk of mislabeled, incomplete, or duplicate data increases exponentially.


Data cleansing is the process of de-duping or fixing known errors in your data sets. Historically, it’s been a time-consuming manual process that turns many businesses off from employing AI in the first place, simply because the time, effort, and money required to do it effectively is often just too much to bear. 


Fortunately, there are a host of new technologies available that automate data cleansing to unburden human teams of the work and give enterprises hope they’ll be able to deploy AI models across their business. Organizations should also employ strict data governance policies to ensure data quality and integrity in the future. 


High-quality data is essential not only for creating AI models and functionality, but also for removing inherent biases from the data. Inherent bias occurs when the available data is not representative of the population or subject of the data model, which can happen either because data doesn’t include variables that properly capture the subject you’re trying to predict or because the data you’re using is produced by humans, which may contain latent biases against particular groups of people. 


While that might not seem like a big deal on the surface, it’s important to remember that biased data will produce biased models that can be discriminatory to your employees and generate incorrect conclusions that can harm your business. 


You need useful, complete data on top of high-quality


Like most things in life, data is only useful if it’s relevant. Having a lot of clean data is vital to effective AI modeling, but data appending takes cleansing several steps further, replacing outdated or incorrect data with new, up-to-date information such as swapping an employee’s title from their old job with their new title and direct report in their new department. 


The real value in data appending is that it takes the data points you already have and supplements them with correct and (often) new information that helps your AI model contextualize the information it’s fed to generate more accurate, precise, and correct outcomes.


Using AI in HR and how to choose the right HCM

As more HR teams start adopting AI to simplify and enhance essential functions like recruiting and onboarding, training, and performance management, they’re also beginning to realize the importance of choosing technology partners who implement data management best practices. 


This is especially true when choosing an HCM provider. Next-generation HCM platforms are infusing a range of robotic process automation (RPA), AI, and machine learning capabilities into their software to create higher-value, higher-performance solutions. 


But many of them tend to be more focused on creating more sophisticated algorithms and bells and whistles than they are ensuring the data those algorithms process is as clean, complete, and current as possible. 


When looking for an HCM provider, it’s important to investigate and fully understand their data governance and hygiene strategies. Do they natively integrate systems like payroll, benefits administration, and core HR systems? How is the data from disparate sources managed, in multiple databases or from a centralized data store? How often is the data updated, synchronized, and cleaned? And how experienced is the team in managing all those moving pieces and effectively building algorithms that can meet your specific needs instead of generic use cases? 


Artificial Intelligence has long been promoted as a game-changer for virtually every industry and application. But for HR teams eager to take advantage of such powerful technology, they’ll still need to rely on good old-fashioned human intellect and curiosity to make sure the machines are getting the right data at the right time from the right places to make it all happen. 

Click here to learn more about SyncHR’s robust data governance, hygiene, and integration practices that support revolutionary AI technologies HR teams need to succeed.



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John Cuellar

John Cuellar

John is responsible for SyncHR’s product, engineering, and system operations teams. He is focused on streamlining the business processes related to HCM and finance by distributing SyncHR to all members of the workforce and by using patented security and workflow to control these developments. John is also responsible for delivering SyncHR as a cloud based application with “extreme ratio” financial metrics.

He has a background in engineering, workplace applications, and business administration, bringing over 25 years of experience deploying strategic HCM applications. Prior to co-founding SyncHR, John was the CEO of Harbor Technologies, since acquired by Mellon Financial Corporation. Previous to Harbor Technology Group, he spent an internship with the Swiss Bank Corporation in their derivatives pricing and trading group and also worked as a senior manager for the US Navy. John received his Bachelor of Science degree in Electrical Engineering from the University of California at Santa Barbara, and his Master of Business Administration from the Haas School of Business at the University of California at Berkeley.

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