Data is important. It helps us make informed decisions. What happens when the data isn’t accurate? Whether you’re building a predictive revenue model or aiming to use your data to make informed decisions, your data quality will be a key indicator of your success. In this post we’ll provide tips for assessing and consistently improving your data quality.


There are a few things that you can do right now, whether you’re on Bullhorn, Bond Adapt, RDB Pronet, Microdec, Salesforce, Talent Rover, or something else, which will tell you just how good/bad your data quality is.


Your CRM may have a tool that will help you to clean out duplicate clients, contacts, and candidates. If you want to run reporting based on your clients or candidates, then this is the quickest way improve your data quality.


However, you do it currently, run a report on your CRM data that allows you to sort the results by one of these metrics, then look for abnormalities and clean them out. Sometimes, these are simply the gateway to the rabbit hole, which makes them a good place to start.

Time to Fill – look for permanent placements that took one or zero days to fill. This indicates that your recruiters are adding the key data to the CRM after the placement is made. Aside from being bad practice, it often leads to other data quality problems. It also ensures that you won’t accurately know which stage of the process your live jobs are at. In most situations, the data offender (your recruiter) only adds the interview of the candidate that was placed. He or she won’t add the interviews of those who were rejected, and likely won’t track which CVs were sent either. This will throw out all of your conversion ratios and likely means that your trusty interview to placement ratio may be completely false.

Placement Fees, Fee %, Pay/Bill Rates – If you’re seeing fields with a zero value, then you know something is wrong. It may sound obvious but this will also call your total sales number into question – we frequently see companies where 20% of placement records are lacking some of the key data.

Candidate Source – Is this always blank, or does it say “headhunt” or “referral” frequently? This is a particularly big problem for agencies who pay higher commission for headhunted candidates. Aside from that, it means you’re unlikely to understand the real ROI from job board and social media advertising.

Contact Data – take a look at all your clients’ data and see if there are gaps in the first name, last name, phone number, email address, job title or location fields. You should be tracking the contact at each client who your team member is speaking with, so that someone can pick up the ball should the need arise – but if there’s incomplete data then it’s more difficult for everyone involved. If you’re an international consultancy, then you’ll want to segment your data by country. If you’re nationally-focused, then make sure you’ve got cities or regions.


I’m guessing that you’ve probably got a dashboard that clearly shows your recruitment process metric ratios. You should be able to drill into that data to see the numbers behind the ratios, as well as the consultant who added the job, sent the cv, logged the first interview, or placed the candidate. Looking at this data is a great way to see who your data offenders are.

Job Added: CV Sent – Quite frequently, we see that consultants haven’t sent any CVs to a particular job. Sometimes, that job still gets filled and sometimes it’s okay that this happens, but if you’re seeing less than 2 CVs sent per job on average, then your team may be skipping steps and hurting your data quality.

CV Sent: First Interviews – Similar to the problem above. Does every CV sent result in an interview? Wow. Your team is legendary. Do you have MORE first interviews than CVs sent? Wow. Your team isn’t entering data.

First Interviews: Placement – This goes a bit beyond the scope of data quality, but it’s really interesting so I’ll say it: Our research shows that perm jobs with three 1st interviews are 6x more likely to be placed than jobs with one interview (on average). Consider the amount of work that goes into getting the 1st interview arranged – you still have to pull the job, agree terms, do all the advertising and candidate sourcing, qualify all those candidates and submit them. The vast majority of the effort required is done for one interview so you get much better value and ROI by ensuring that if you’re going to do all that work you get 2/3/4 etc and get best value for your effort. If you’re seeing only one first interview per placement, then your data may be off OR your team might not be running optimally. You could fill more jobs if you encourage your team to get 2-3 more first interviews be opening. See Tip #2 below!



Recently, we did a case study with Prime People, asking them about their tips for building a data-driven recruitment consultancy. One of their key points was this: increasing visibility leads to improved team performance (they increased their Net Fee Income by 24% within 6 months of starting to use cube19), and it also leads to improved CRM usage and data quality.

If your consultants, managers, directors, and managing director can all see what each consultant does, and it’s easy to visualise, then you’ve created an environment where data mistakes have a good chance of being caught quickly. Well done.


“People do what you inspect, not what you expect”

One way to encourage consultants to follow your optimised processes is to set targets on each KPI that identifies a stage in the placement process. Common KPIs include: New Jobs Added, CVs Sent, First Interviews, and Placements. If you only track placements, then don’t be surprised if you only see placement data in your CRM.

The exercise of setting a target is a great way to help onboard new employees and to set expectations for all team members. It’s a good idea to visualise team members’ targets on a leaderboard, so that team members know who is excelling, what targets are reasonable for experienced consultants, and where they fit in the team structure. KPIs serve to motivate, set expectations, and keep your data clean and complete.


The Data Warehouse Institute released the above image after their report entitled: Best Practices for Real-Time Data, BI, and Analytics, where they outline the benefits cited by companies who have implemented real-time analytics. Yet, I still hear this story frequently:

Them: “We track everything we do and create reports on it at the end of the month.”

Me: Oh yeah?

Them: “Well, it’s not really the end of the month, we need a week or two to generate all the reports”.

Me: How do you know that the data is correct? Can you dig into the data on the spot?

Them: “Hahahaha, yeah right. We debate accuracy in our meetings all the time.”

So what you’re saying is: it’s more valuable for your business to have your team run reports than work on generating or closing business. And you don’t know if the data is accurate. Plus, you only know what happened in February by mid-March. That means it’s too late to do anything about February’s results.

Once you move to a system that gives you access to your live real-time data, you’ll never ever go back. It’s the same paradigm shift as the first time you got a smartphone.. can you imagine going back to this puppy? Didn’t think so.


There are hundreds of discussions every month that could benefit from facts. If your team is afraid to use your data to make decisions, then get them to write down the information they would like to have to make a better decision.

You’ll find that as people begin asking for data, they’ll begin to think about the processes that affect their data (or lack of it). Combined with the tips above, they’ll start to see data quality improving, and start using data in more and more conversations. Pretty soon, they’ll have high confidence in the data itself and they’ll be making informed decisions because of it.


If you’ve got a list of situations where someone needs data, then assign that list to a leader in your organisation. This could be your CIO, Head of IT, Head of Ops or someone else. Make it their task to improve data quality, availability, and visibility. They’ll likely look deeply at your processes, at your data entry points, and at the infrastructure, you’re using.