Clean Data – A Guide for Recruiters
How clean is your data? Probably not a question you often ask yourself, but having clean data is important for recruiters aiming to succeed. Similar to other important systems, your database and the data it holds is only as good as what you put in. Even if that one specific candidate is outstanding, it won’t make a difference if a vital field isn’t completed correctly on their candidate record – suddenly your database candidate search becomes more needle in a haystack than the efficient CRM you had hoped. Aside from the clear benefits for candidate searches, clean data can also help the wider business – enabling you to take a step back and understand your return on investment (ROI).
New Year, Fresh Start?
Recruiters across the country will currently be taking a deep-dive into their data to build a picture of how the business is performing against Key Performance Indications (KPIs) or ROI. But in reality they need real-time access to review business performance on quarterly, monthly or even a weekly basis. Without accurate data, this isn’t possible.
So what can you do to help clean up your data? In order to achieve a truly effective data approach you need to take into consideration both the short and long term activity. In the short term, for example, if you are transferring data from one system to another, you need to decide whether to achieve this with a data migration or via manual data entry.
The speed benefits of technology-led migration are compelling, but it is important to take into consideration the state of your existing data and the need for additional proofing elements. This can be conducted by reviewing and running tests. Take care not to move poor data from one system to another.
Alternatively, there is manual migration. With this approach you may have specific control over the exact elements of data entry however, this can have additional costs associated as you will need to either get the current team to undertake this activity or hire some temporary workers to manage the data entry.
The volume of the migration can make a big difference. Setting boundaries of time frames – such as only candidates from the last two years – can help reduce data volumes whilst going through the cleaning process. Taking a practical, process-driven approach – to add filters – will help keep the data clean.
Start the Synchronisation
On an ongoing basis, whether you initially choose manual entry or not, you need to make sure that people follow the same process. To attain clean data in the long term, outline strict guidelines at the start of the process and engage with team members to ensure all criteria are met when inputting and amending. Unfortunately, there is no one-switch approach to transform ‘messy’ data into clean data. What may seem like an obvious point when inputting a piece of data – for example using upper or lower case for company names – may be forgotten or ignored by manic recruiters busy on the road. Look at what data is required to make a candidate profile: name, age, address, mobile number etc. This may all seem obvious but can easily be overlooked. Completing the same fields, in the same way, will keep the data clean.
Ensure the team members using the database have a sense of diligence and ownership when handling data in the system; cutting corners isn’t an option when it comes to data.
Get Your Data Geek On
Provide the team with some data. Dashboards are a quick, hassle-free and engaging way to get your recruitment team members on board with keeping to the clean data ‘mantra’. With fast access to valuable insight they may have been unable to access before, recruiters quickly realise that investing in clean data management will be investing in themselves.
cube19 works in partnership with Bond International Software to help recruitment companies improve data quality, increase revenue, scale efficiently and motivate teams.
If you would like to find out more about data quality and how you can use Cube 19 with Adapt, click here to sign up to a free Cube 19 webinar on 4th February.