Why Is Data Collection Important for HR?
By: Arden Harper
Modern life runs on data. Every decision made for and by us is structured by data. Nearly all business decisions are made from the information gathered by corporate data. So why, then, do we spend time using methods of data collection that are ineffective, broken, or nearly nonexistent? Data collection is one of the fundamental pillars to understanding what works and what doesn’t in every business. Data collection exposes weaknesses and places to grow.
Better data collection is the way to understand where changes can be made, and what aspects of a company are prospering. Most importantly, data collection shows to what extent a practice might need to be changed, or exactly how well a prospering sector is doing.
Before we go on, a disclaimer; it’s important to acknowledge that human beings are not machines, and in knowing that, it’s useful to track trends and behavior in an objective and non-judgmental way that promotes sustainable and healthful practices.
What can better data collection do for the company? According to nektardata.com, two vital-yet-simple things:
- Make better business decisions
- Save time at work
Make Better Business Decisions
Nektardata.com states that “According to a Helical IT survey, if a decision is made relying on data rather than pure intuition, the chances of succeeding are 79% higher. By implementing a centralized data collection process, an organization can make data-driven decisions. Once a system is in place, the quality and quantity of data that a business collects over the years, and how it decides to use it, will strongly influence its competitive advantage and sustainability.” What they are saying is that humans are subject to logical fallacies, subjective reasoning, and personal attachment that all affect how decisions are made. Facts cannot lie, and therefore will show no tendencies towards wants and feelings, but rather will paint a clear picture about exactly what is going on.
There are four types of data that will affect how business decisions will be made:
- Personal data: identifying factors such as demographics, email addresses, and names
- Transactional data: clicks, purchases, web page visits, engagement
- Web data: outside research, “catchall for anything you can find on the web that is public-facing”
- Sensor data: produced by objects, affected by variables, “it covers everything from your smartwatch measuring heart rate to weather sensors”
For most companies, the overwhelming majority of data gathered will come from personal, transactional, and web data. Sensor data is often associated with science-based specialized businesses (weather services, exercise science programs, scientific research labs, medical services, etc).
All four types of data can be used in regard to customers or clients, but personal data also affects employees. No employee exists in a vacuum, and their personal data can indicate some trends among others who have similar data. Here are a few examples:
- An employee who travels an hour and a half to get to and from work every day might behave differently than one who can walk to work
- An employee that has young children may use more personal or sick days, but may also get work done in a timelier manner
- An employee who is a POC (person of color) or who identifies as LGBTQ may have unique stressors that affect performance
This does not inherently make some employees better than others, but their differences can provide varied advantages and disadvantages. Humans are not machines, and their personal data is an important aspect of company data that can be studied, especially if the data helps illuminate how to support all employees better.
Hire & Retain Efficiently
Employees are the heart of a corporation. They do everything to generate income and innovation for the company, and the best way to get good results out of them is through retention. Employees start with recruiting, move to hiring, and hopefully stay at retention without turnover. Retention is a difficult, ambiguous subject that is difficult to control. However, what is controllable are the trends with employees.
Recruitment statistics can be simple or incredibly in depth. For example, on an application there could be a drop down box or input field asking where the employee found the listing, or could be as sophisticated as tracking how many people open up a web page (the listing) and from which websites applicants are redirected.
Hiring is based from recruitment data. How many applicants fit well enough to warrant an interview? What are the demographics of people who apply and are a good fit? What kind of employee is being attracted? These are all questions that data can objectively answer. Changing the wording of a listing could attract or detract different types of applicants and is measurable with statistics.
Retention is keeping an employee year after year. Turnover is when the employee leaves the company, for any reason. Turnover can be turned into a statistic that, compared to retention, can show trends.
In a major school district, it costs about $120,000 to hire and train a new teacher, who will be more ineffective (on average) due to a learning curve in understanding internal procedures (and sometimes experience). If the teacher is retained, they usually cost about $75,000 a year (depending on pay level increases), vs hiring another at $120,000 again, with a savings of $45,000 each year. Retention pays, big time.
Statistics such as personal data (demographics) help predict trends to better help retention. For example, a company who offers better maternity benefits (e.g., more than 6 federally mandated weeks postpartum) might have better retention of female employees aged 25-35. This trend seems to suggest that better maternity benefits can lead to better retention, which leads to a more stable workforce and less turnover.
Another, albeit more extreme, real life example is a company based out of Washington state who raised their minimum salary to $70,000/year who saw the rate of employees purchasing homes (choosing to stay in the area and with the company), having babies, and contributing to their 401ks more than double the rate they had previously. Turnover decreased and dedication to the company increased (measurable by worker output) according to statistics cited by the Company’s president. He was able to measure this growth by keeping data and generating statistics from the information gathered from employees before and after his experiment.
Statistics gathered through personal data of employees can help with decision making in other areas aside from turnover and retention as well. For example, worker output (how much is getting done in a certain amount of time) can be measured through ideas such as ergonomics, office layout (open plan vs closed plan), PTO (paid time off), health insurance benefits (healthy employees tend to do better work), remote working options, paid lunches, childcare benefits, and more (ranging from huge overhauls to small adjustments).
Data in the forms of statistics and trends will not always predict the future, but it can provide a much clearer idea of what the future might be than just guesswork alone. This is especially true for working with people; people will never behave exactly as planned.
In Summary
This article delves into the top two reasons to collect data: making better business decisions and saving time. The idea is not to completely overhaul every part of your company and turn it into a data-collection machine, but instead to start looking into areas that generate data and begin recording information there.
There is always room for improvement and innovation. By collecting, recording, and analyzing data (internal or external to the company), processes can be streamlined, employees can be happier, and ultimately the consumer can get a better product or service.
About the Author
Arden Harper has a bachelor’s degree in music education, with a minor in linguistics from West Chester University of Pennsylvania. She is always liked to write (starting with many abandoned chapter stories as a preteen), play instruments (and sometimes gets paid to do so!), and has become a competent home cook in recent years.
Arden currently works at Boxplot, a data analytics consulting firm that helps organizations to confidently make accurate, data-driven decisions. They are experts at automating data movement and cleaning, analyzing, and visualizing datasets, and creating reports and dashboards. Boxplot helps a department or business keep a pulse on the most important metrics.
