Let's face it, data management is among the most painful duties when it comes to talent tech. With data increasingly important, managing it well it is essential to maintain quality, and having bad, dirty data could undermine almost everything you do in talent. Data cleanup comes with all kinds of risks, but trust us, it's doable - and it's worth it.
What 2020 cleanup projects could ease the burden and ensure clean talent data systems? Let us explore.
Why prioritize clean talent data?
For many reasons, lots of talent data gets stored with a lot of errors. Missing data, duplicates, disconnected systems, changing sets of organizational codes, lack of normalization, free text when you really need consistent codes, stale data, etc, etc, etc. The list is LONG.
On the other hand, as AI, machine learning and analytics get more important across talent functions, data becomes increasingly important. So the opportunity cost, or the cost of doing nothing, if you DON'T clean your data is getting higher and higher as every day passes. Uncurated data is literally costing you money.
Some of the key benefits of data cleanup include improved decision making, savings on money, and reduced wastes. The process also streamlines business practices to save on time, minimizes compliance risks, and increases productivity.
Two kinds of data "cleaning"
You might have noticed I'm using term "data cleansing", and also "data curation". Both of them are important in maintaining a clean dataset, but they are different:
Data cleansing involves updating data at the source. For example, you might update job title data in the HRIS directly so that you get a clean set of job information (well, at least for a while...). Sometimes that what you need to do, primarily if there are processes dependent on that data, but it can be really painful
Data curation is a broader term which gives you more options. You could choose to normalize or translate data inside a data lake rather than changing it at the source.
Data curation helps identify and rectify - or at least manage - any corrupt, missing, miscategorized or inaccurate data in your systems. The cleanup is usually done on systems with incorrect, incomplete, or irrelevant data. The flawed sections are usually identified, modified, deleted, or replaced. Considering businesses rely on this data to make key decisions, facilitate payments, or compute employee benefits, it is crucial that you maintain clean records for accuracy.
The best cleanup projects for talent data systems in 2020
Here are the best cleanup practices to guarantee accurate data:
1. Data standardization
Most of the bad data is usually a result of human error. Lack of policies or standards that articulate how information is entered into your systems could lead to different iterations of your staff information. The solution to this is data standardization.
The process facilitates the creation of an enforced, consistent, and organized environment to enter information into the system.
Standardization demands that you input data into the system in a single standard format. You also need to make the data consistent so that the output remains in the exact format. Standardized inputs are more accurate and allow you to report your data in a consolidated manner.
Mind you, if you can't standardize your past data, or the effort to standardize future data exceeds the benefit (eg if it causes pain to recruiters and managers), you can use algorithms to connect your messy data to a normalized or canonicalized set. Job titles are a great example of this - even though you might place an ad for a "coding guru", an algorithm can automatically attach the normalized title "senior software engineer" as well. A data lake like SwoopTalent's will have that as a standard feature.
2. Tackling duplicate and incomplete data
These are also essential cleanup activities that could ensure an accurate talent data system. Duplicates can really throw analytics, integrations and all kind of things off kilter. You can remove them, like a lot of people recommend (a process called deduping), or you can connect them! Connecting duplicates means you don't lose the fact that a duplicate existed, which might be really important for things like multiple job applications from the same candidate!
Missing data is also a very common problem in talent data systems. It is an issue since it adds ambiguity when analyzing the information, and it reduces the ability for algorithms to be their best. Also, it could lead to flawed results since metrics can be computed based on the available information. To address this, you might want to enrich and update your data using other data sources - including what you find in your data lake!
3. Connecting all your silos
In the bad old days of connecting data, we used old fashioned data warehouses, and built complicated "extract, transform and load". We build system integrations in a 1:1 say, so we got a spiderweb of integrations that really bogged us down. Well, why not make 2020 the year you put the foundations in place to get rid of that approach forever? Implement a data lake and you set yourself up for a single dataset, easier integrations, easier system migrations, no more lost data...and so much more.
With all these cleanup projects on your fingertips, you can ensure that your talent data and systems are in tip top shape for the future!
We would love to listen to you. Contact us today for apt guidance on how to maximize the impact of HR tech to your operations and bottom line.