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The best database I ever saw belonged to Crone Corkhill in 2001. It was the most accurate and up to date. It was the key to their success and why they dominated Secretarial recruitment at the time. I remember watching a consultant putting a client on hold, standing up and shouting out a fellow consultant for not updating a candidate, then sitting down and continuing a very pleasant conversation.
Databases full of poor or irrelevant rubbish are a real thing. I recently came across an article on Forbes on data cleaning. Data scientists spend 60 percent of their time on cleaning and organising data, and with 19 percent, collecting data sets comes second. But it seems like this is the total opposite with recruitment.
Most of the time we gather more data and fill up our database instead of cleaning it. I remember FSS having a Data Builder team in the early 2000’s. But hardly any recruitment agency has the time or the right people to clean up their database. In our webinar ‘Cleaner Data: a better Candidate Experience’ we address the impact a cluttered database has.
A cluttered database can cost you valuable time in finding the right candidate or contractor when in a race for your biggest client. That’s why I got 3 tips for you on how to clean your database.
1. Define the required information for candidates and contacts
The required data fields in your database must be relevant. Think along the lines of the obvious: First Name, Last Name, Phone, Email, Location and Current Job Title and Company. A current Salary and CV are needed for a candidate. Think of the things you need to quickly contact and assess who the person is. Too many will be equally as bad as too few as it needs to be practical, your consultants are busy and need to buy into the importance too.
Therefore, if you have records that are blank in these core fields, find them and do something with them. Think about how you are going to filter out this data. Our tips would be: missing contact details, outdated notes and information and incomplete data will cause you problems.
2. Run searches and transfer data to hotlists
Now you have identified your fields you can start to filter your data, this will create multiple search results or hotlists. But before you do so you need to keep your next action in mind on how you will be segmenting (categorising) your data. The next step is to create hotlists based on your categories and the filtering can begin.
Filter out all contacts with no contact details, by looking for blanks. Sometimes people are reluctant to share details, or you don’t have them at the time. That’s why contact details can include faulty information, like '[email protected]' or phone numbers that only have zeros.
When you have found all blanks put them into the correct hotlist. Hotlists will be your working sheet and will hold all contacts and candidates that have faulty information or missing data. Not exclude this from your other searches and you can focus on the outdated and the incomplete data.
3. Archive old data
Up next is to amend, archive or delete the data. The hotlist with missing data will be best off deleted or if necessary, archived. The other hotlists, however, will be your working sheets.
For the lists with missing and or faulty data, this is where your Data Controller or Marketing team will be best to advise. You can send these lists an email with a data request, if relevant. Otherwise, archiving these candidates will be your best choice. If a candidate returns to your agency, you will still be able to pick out the archived data. Just keep data regulations compliance in the back of your mind.
There is still a lot of manual work involved in cleaning out your data. It also depends on the capabilities of your CRM or database. Data scientists heavily rely on their clean data, so why don’t recruiters? With data regulations right around the corner, you need to start thinking of ways to keep your database clean.
Stop gathering bad data, start cleaning first. Cleaning your database will make you money, you will find people you have lost connection with, and either place them or place with them.
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