Why are data errors such a problem, why do they happen, and where do they occur? This blog post considers these questions, introducing the three I’s—incomplete, inaccurate, and inconsistent data—and exploring why organizations are only as strong as the informed decisions that workforce analytics helps them to make.
The Risks Associated With Poor Data
Poor data creates risk by:
- Diverting time and resources towards data corrections
- Exposing your business to inaccurate discrimination claims
- Undermining your ability to isolate problem areas and limit liability when defending against discrimination claims
- Misleading your strategic decision-making based on inaccurate reporting trends
Organizations must ensure they have ways to moderate and mitigate these risks, assuming they cannot be avoided outright. The time and resource burden is one that organizations likely cannot eliminate entirely. An understanding of your systems and who owns the data is ideally needed upfront: a good working relationship with those who can change how data is gathered and corrected will expedite the process and lead to fewer errors.
For most organizations, this time and resource burden is really the largest and most routine risk. Identification and correction involves multiple parties, and the additional legwork of delving into individual records and querying personnel in order to uncover a truth can add hours to an already busy schedule. But these kinds of errors can also come to a head in a highly consequential legal context—and in a time when the old regime of regular audits and federal checks is being dismantled, we’re likely to see this kind of risk becoming ever more prominent.
With discrimination claims, the risk is that if a claim is inaccurate you need to be able to present data that refutes it. A plaintiff could, for example, engage on the basis of data that appears to show adverse impact within your organization. With clean data you could demonstrate how the data is based on false external assumptions, or that while there are genuine adverse indicators, they can be explained by additional context that the data provides.
But if your data is riddled with errors, at best you’re going to be scrambling to correct it, and at worst you and/or the legal system will accept the plaintiff’s version of events because your own faulty data supports it (or is in such disarray that its basic integrity is called into question).
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Why Data Errors Typically Happen: An Introduction to the 3 I’s
Data errors are rarely a consequence of outright negligence or intentional sabotage—more often than not, they’re a natural consequence of the complexity of modern work. Data is captured by people dealing with many competing priorities, and it usually passes through multiple different systems built for different purposes. Employees and employment itself can also be more complex than systems or processes rigidly require. In combination, this leads to three main types of data error, or the “3 I’s”:
- Incomplete data: Information that is lost or never captured in the first place
- Inaccurate data: Information that is untrue due to flawed input or collation
- Inconsistent data: Data that is contradictory between records captured at different times or on different systems
It’s important to stress that such errors will typically recur if their root causes aren’t addressed or at least counteracted. Extra effort upfront will ensure that different systems work well together and data isn’t somehow changed or updated improperly. This is a collaborative process across all your systems and within your HR, HRIS, talent acquisition, and other teams.
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An Overview of Where to Look and How to Start
Incomplete, inaccurate, and inconsistent data can occur in multiple places, but there are three that require particular attention:
- Employee snapshot files
- History files
- Applicant files
Whether attempted fully in-house or with Affirmity’s support, a full reconciliation effort to address the issues in these files is a significant undertaking, even if you have robust checks and balances in place to ensure a high level of data integrity during ongoing collection. As an initial step, we recommend that your teams perform an initial sweep on the above files for clear examples of missing and/or mislabeled data—this data is the cause of substantial extra project time. This is especially true when you’re working with an outside workforce compliance provider like Affirmity, as finding and correcting this data will usually rest with the employer anyway.
It’s also important to identify fundamental inaccuracies as early as possible in the process. For example, if digging into the data means that you discover that a certain snapshot is from a different window than initially indicated, this may require you and your support to start the entire process over again.
Data Ownership
Establishing who owns, who can access, and who routinely works with these key files and with which systems they interface should also be an early priority. Some organizations limit access to their HRIS and other key systems to a certain group of people, either specific teams, functions, or seniority levels. Others may operate more open systems—particularly, you’ll need to be conscious of whether there’s a self-service element to your information systems, i.e. where employees can update certain information (and be the source of apparent contradictions).
So, consider what happens when you need to hire a new employee: if you need to make a new employee record or update one, do those requests go through the HRIS, or does it all kick off with a manager? Know your business processes and consider who touches the data along the way—from an Affirmity perspective, this is invaluable context as organizations often do things very differently.
Obtaining Your Data
This brings us to how to obtain your reports for the purpose of data reconciliation. Some organizations will have an automated setup using pre-saved queries, and things may be as simple as asking your HR team for a data dump based on the specific date ranges you’re looking for.
Others will have to rely on a more manual process after this data export, where HR or HRIS teams have to review the output before providing it for further analysis (either to catch common errors or format the data in a certain way).
A number of organizations may have a more intensive manual process where data must be pulled from multiple sources and collated and extensively re-formatted before it is ready to be worked on.
So, to align with Affirmity or any other external consultant team, you’ll need to ask yourself the following:
- Who will be responsible for gathering your employee snapshot, history, and applicant files?
- How will the employee snapshot get to the external consultant team?
- Does anybody review and/or edit the snapshot before it goes to the external consultant team?
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Dig Into the Detail of the Poor Data Problem
This article is an extract from the beginning of our ebook, “The Poor Data Problem: How to Prevent and Correct Incomplete, Inaccurate, and Inconsistent Data”. The full guide goes on to explore the three I’s in greater detail, covering:
- The characteristics of incomplete data, inaccurate data, and inconsistent data
- What to look for in your employee, history, and applicant files
- Specific problem fields and common errors
- Overarching best practices for data review
Ensure your data avoids liability—and take your data collection practices beyond simple compliance. Download the full ebook, then contact us to find out how we can help.
About the Author
Stephen Caldwell is a Manager of Consulting Services at Affirmity. In this role, he leads and manages a team of Affirmity consultants, providing consulting and project management in workforce compliance programs. He has assisted clients in the utility, healthcare, defense, telecommunications, energy, chemical, and other industries.
Mr. Caldwell has been with Affirmity for more than sixteen years and has over 30 years of experience in human resource consulting and diversity planning.