4 Steps to Cleaner and Better HR Data
Your data quality shouldn't be a barrier to beginning an analytics project. Here are four things to consider when managing your dirty HR data.
Dirty data refers to the errors or gaps within your workforce data—think missing personal information, the folder of performance reviews you haven’t collated with your HRIS, typos, and other simple human errors. HR data deals with a lot of moving parts, making it notoriously difficult to keep everything up to snuff. It’s next to impossible to have perfectly clean HR data. But many organizations still make the mistake of seeing their dirty data as a barrier to utilizing people analytics.
The good news is, this doesn’t have to be the case. You can begin using people analytics today, regardless of the quality of your data. Many metrics and measures in people analytics that won’t be affected by the quality gaps in your data.
By getting started with the data you already have, organizations are able to see the impact of analytics almost instantly. Once the initial ROI of people analytics is felt, some organizations determine that it’s worthwhile to undergo a thorough data cleansing. Plugging data into a people analytics platform can actually help you understand where your biggest gaps are, making cleanup faster, easier, and more strategic. Others find that their analyses are already accurate enough with their data as-is, or opt to spot-clean as they go.
No matter where you sit with your data quality today, it doesn’t have to be a barrier to beginning an analytics project. Here are 4 things to consider when managing your dirty HR data.
Don’t clean data just for the sake of it
A data cleanup project for the sake of simply having clean data does not have any immediate, actionable outcomes. Raw data by itself itself is not particularly meaningful to HR leaders or their business, even when it’s spotlessly clean. It quickly becomes a low priority task on the corner of your HR coordinator’s desk, and often is never fully completed.
“By getting started with the data you already have, organizations are able to see the impact of analytics almost instantly.”
When utilizing people analytics, your main goal is to gain visibility into your workforce and information that drives you to make better business decisions. The data you already have in your HRIS is already valuable enough to begin making incremental changes on how you manage your workforce. Putting time and resources into reviewing analyses and actioning on strategies is a far more effective use of time than a data cleanup will ever be.
Put your data to work while you clean it
No data cleanup can happen without reviewing the data you already have. Implementing a people analytics solution becomes an easy way to identify the meaningful gaps in your data, rather than cleaning all your data just for the sake of it.
What’s the most effective way to start cleaning your data? Start with a relevant business question, such as, “Do we have a diversity problem?” Begin to pull relevant data, questions, answers, and information. Put the good data you already have to use. If there are still significant gaps, it may be worth cleaning your data on an as-needed basis. Gain insight as you spot-clean, rather than performing a complete overhaul.
Don’t let perfectionism get in the way of progress
Good business decisions require accurate data, but not always perfect data. Consider how other departments utilize data on a regular basis to make future projections, without getting bogged down by perfection. Your finance department makes projections, then changes decisions based on adjusted costs, restated data, and realignments. Your marketing department uses data to validate instinct on marketing activities, but this data can never perfectly predict the human response to marketing activities. Progress is more important than perfection.
“When utilizing people analytics, your main goal is to gain visibility into your workforce and information that drives you to make better business.”
Stela Lupushor, Founder of Reframe.Work Inc., states, “With the increasing amount of data in HR systems, there will be more opportunities for error. People will discover ‘Bob is missing in this report’ and then discredit the whole analysis as a result. However, is the analysis directionally correct? Is 80% accuracy sufficient to make the right decision? Would you get more value from your people analytics project by running another analysis, or would you get more value from going on a ‘finding Bob’ expedition within your data?”
Consider the impact of perfect accuracy
Analytics provides the insight to make business decisions, but not all business decisions are made equally. There will be times when perfect accuracy is necessary in HR data, analyses, and projections. For example, your salary bands will need to be entered accurately in order to determine if you are paying diverse employees fairly compared to non-diverse employees.
However, there are decisions that can be made that have a high impact on employee satisfaction that really don’t require a high level of data accuracy. Perhaps in your last employee engagement survey, 73% of your respondents stated that additional health benefits are more important to them than vacation time flexibility. It’s time to rethink your total rewards strategy—missing responses from a handful of employees won’t dramatically shift your results.
Perfectly clean data is an unrealistic goal for any organization. But more importantly, unclean data doesn’t actually need to stop you from getting the best value out of a people analytics solution.
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