OnRecruit is a recruitment analytics company like no other. Their platform helps organisations recruit much better by actually looking at the results of campaigns and helping them improve based on data. We asked Dirk Meeuws, their CTO & COO, what the 4 most common questions are asked by clients on the topic of data analytics and what his answers are.
1. I have the data, now what do I need to be successful with the data?
You need the right people to understand the data. Someone able to create meaningful and actionable insights from the data and who is able to make the connection between the data, the business and how they influence each other. You also need the right processes in place, to be able to gather the data and use it to improve your business. Last but not least, you need the right platforms to tie all the data together and have good overview of what is going on.
2. What is the technological landscape I need in order to be successful?
First, you need an ATS which heavily invests in a partner ecosystem. We see that these ATS-es understand that by making it easy for partners to partner with them, they can make their own ATS stronger. They also are the ones that are easier to integrate with. Second, you need a great website, which entails and empowers (potential) candidates to stay connected. Third, you need a great business intelligence dashboard to be able to report on your data and understand what is going on. And fourth, you need a great e-mailmarketing or chatbot platform, so you can stay in touch with your candidates in the database.
3. How do I create a data driven culture?
Make sure to balance detail with generics; really follow 80-20 methodology and be pragmatic. Make sure to have at least one data champion internally. Make one person per team responsible for the data for that team:
– Helping him/her understand the data within his/her dashboard
– Helping him/her change/create his/her dashboard
– Test & optimize new initiatives based on the gathered data
4. How do I improve the quality of my data?
It starts by making data quality an issue to begin with. Once you prioritized data quality, you need to have dedicated resources sanitizing and looking at your data. Constantly checking some key metrics to understand whether they really make sense. Understand and question both the input as well as the output. If the output isn’t what you expected it might be your expectations are wrong, but it could also be a mistake in the data model or the input data.
Want to know more? Go to the breakout of Dirk Meeuws at TA-Live, 18 april 2019 in Amsterdam.