How accurate is your business data?
Data quality is the essence of any CRM system and will help to return consistent, accurate and relevant results, although it's no small task keeping data clean and relevant for your consultants to deliver.
Cleaning your current data is much less costly than the alternative of not having enough insight to make the right business decisions or worse, losing clients or placements to poor service due to bad data. So how do you go about cleaning your data? Here are some steps toward solving poor data quality:
Learn more about data cleansing in the knowledgebase article:
www.chameleoni.com/knowledgebase/database_cleanse.pdf
1. Incomplete records
The first step is to look at your existing database and correct incomplete or wrong data. Recruitment data generally comes from a variety of sources and it's important to remove redundant data and update any incomplete records.
Communicate effectively and further your reach by getting the details right. Using the quick search it's possible to find contact records with blank fields; candidate records that have no CV attached, as well as records containing badly formed email addresses that are likely to bounce when used.
2. Duplicate records
Multiple listings of contact information is often viewed as a trivial problem however eliminating any duplicates will help to remove flawed data allowing users to be more productive and efficient.
By identifying and merging records that share a name, address, telephone number or other field, it will become clearer for consultants to see the contacts relevance and potential and eliminate the possibility of sending multiple emails to the same person.
It's possible to search and identify duplicate records by specific field or by using the duplicate record baskets. Once identified you can decide which record to update or delete from the duplicates.
3. Check and clean your data from the start
As data comes into the database it’s important to make good use of the
Auto-recognition Queue and the
Parsed CVs list and make a point of checking and adding to the data to help protect the future integrity of the database.
Once parsed to the system CVs are automatically stored in either the Auto-recognition queue or Parsed CVs basket. This is to help with your administration, and allows you to double-check the success of the software, and correct any errors that may have been made as well as add to the information for each record.
4. Develop a policy for data standards
Often companies underestimate the size of the data quality problem and tend to look to technology to resolve the issues, whereas often the focus needs to be on the human and business related processes. It's important to educate users about data standards and practices for updating data to help standardise the information that is uploaded and stored in Chameleon-i.
Determine whose responsibility it is to monitor data quality and to maintain a process of feedback relating to any issues your consultants face and any expired data.