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CIOs want HR leaders to know about ETL. Here’s why!

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Exciting times are ahead for HR.

Generative AI and all sorts of AI technology are opening up many new doors of possibilities. Automated processes, personalization at scale, faster recruiting and onboarding, predictive analytics, and data-driven decision-making - there is so much technology can do for you.

But the catch is that they all feed on data. And not just any data, but clean, organized, and meaningful data.

The amount of data being collected by enterprises is growing not just in volume, but also in complexity. According to Mulesoft’s Connectivity Benchmark Report 2024, a staggering 81% of companies are struggling with data siloes and disparate systems. As a result, HR struggles with process duplication, inaccurate data analysis, compliance risks, lack of visibility into people and processes, and a complex network of technology and systems that has compounded manual work.

In response to the above, CIOs worldwide are now cautioning HR to build a strong data foundation to be able to leverage AI, automation and analytics fully.

And to build that data foundation, you need ETL.

So, what is ETL?

ETL is simply short for Extract, Transform and Load.

It is the process that consolidates data from multiple sources into a single, coherent data library that can then be utilized to train AI, automate processes or run analytics. 

  1. Extract involves collecting data from all your HR and business systems including your Applicant Tracking System, HRMS, L&D platform, R&R software, Performance Management System, Payroll, email, etc. to bring them together to a centralized location.

  1. Raw data extracted from multiple sources are usually not in the same format, and have duplications and errors. Transform, as the next step, harmonizes and consolidates this data. It makes the data clean, consistent and removes duplicates and errors. 

For example, employees’ date of birth in different systems may be in different formats. This process will recognize that all the different formats are the date of birth and not throw up an error (which is what usually happens, bringing your entire data consolidation effort to a standstill). It then standardizes ‘date of birth’ into one format. 

Similarly, if one system has the demographic data of an employee and another has the behavioral data, then this process can recognize and unify all that data. 

  1. In the last step, the transformed data can be stored as a data library and/or loaded to a data warehouse or other systems when needed. 

For example, suppose your employee background verification vendor needs the address and employment history of employees. In that case, this information or data can be sent to the background verification check system in the format it needs it in.

So in simple terms, ETL breaks down the silos that make data inaccessible for the effective execution of any HR initiative - be it AI, automation, or analytics.

How will ETL enable your AI, automation and analytics projects?

ETL and AI

AI is only as good as the data you feed it. It trains and learns from your data, and therefore, providing complete & good, clean data is important to ensure that your AI tool isn’t delivering half-baked insights to you.

Here’s where ETL comes into play. ETL cleans, standardizes, and consolidates data from various sources, creating a high-quality dataset that is crucial for training accurate and reliable AI models for improved decision making.

ETL and automation

ETL can automate the process of collecting and analyzing employee performance data by extracting performance metrics from various sources such as performance reviews, employee surveys and productivity tools, transforming it into standardized formats, and loading it into the performance management system. These kinds of automation rules that often involve multiple systems can function effectively only if data is accessible, reliable, and has a standardized format across all systems. That’s what ETL does. In its absence, automation can never fully be achieved.

ETL and analytics

Like your AI projects, your analytics too is only as good as the data you have. Interestingly, the challenge that most people face is not in terms of the quantity of data but in their inability to prepare this data to be put to the right use. 

Analytics and reporting thrive on consistent, up-to-date and quality data. An ETL process ensures that the data available for analytics is all the above.

Here’s an example to understand ETL further.

Employees share a large amount of data with their employer during onboarding. This data is often collected in your recruitment platform, in emails, on HRIS systems and more. With ETL, all this data can then be automatically brought together, standardized and sent to other relevant departments, like the payroll system for salary processing, IT for laptop allocation and Admin for ID creation. That’s automation facilitated through ETL. The same data can also be used to understand new hire behavior patterns that lead to understanding attrition, productivity, etc. That’s analytics in action.

Employee Data Platform: The foundation for AI adoption, automation and people analytics

Your HR tech stack that holds the capabilities of AI, automation and analytics needs a strong foundation in the form of an Employee Data Platform.

An Employee Data Platform (EDP) makes ETL possible.

It sits right in the center of your HR tech stack and creates a centralized nervous system to receive and send data signals across systems without any glitches. It facilitates the ETL process. Plus, it preps the stage for automation, AI adoption and analytics. An EDP like Tydy can do all this for you. You could dive into those details here.

So, if you’re just starting your AI, automation or analytics projects, this might be the perfect time to bring an EDP into the scene. 

And if you’re in the middle of these projects, we are going to guess that you’ve already run into data challenges. Even then, it is never too late to save the day with an EDP.

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