It’s April, so it’s about that time where we should start looking at our analytics platforms with a critical eye. Big Data is getting bigger by the moment, and until AI is smart enough to gain insight from data on its own, we’re sort of left holding the bag. Businesses need to be sure they’re leveraging data correctly, and not expending time and resources on analyzing unnecessary data. In other words, it’s time to do a bit of spring cleaning.
MIT recently published a promising article about the current state of AI and Big Data. The article quotes a study conducted by NewVantage Partners this January, in which 97.2% of executives report they are investing in launching big data AI initiatives, and 76.5% of respondents believe that greater volumes of data are helping to empower AI in their organizations. That’s all well and good, but when you consider that the study surveyed mostly Fortune 1000 companies like American Express and Verizon, the results are a little less exciting. Deep learning is still a way off for many organizations. In the meantime, most of us are tasked with analyzing the data ourselves.
So Much Data, So Little Time
Having a ton of data to work with isn’t a bad thing. The amount of data collected during the recruiting process can help HR obtain valuable information about who to hire, how to hire, and how long it’s going to take. The issue, for many of us, lies in the gap between collecting data and making it actionable. So, what exactly should we be looking at? Here are some of the top metrics worth tracking:
Submissions to placements ratio. Obviously, placement numbers will always be lower than submission rates, but if there’s a drastic difference between the two, it could mean that hiring managers and recruiters aren’t communicating clearly. Perhaps they need more information to target candidates properly.
Response rates and conversion rates. Low rates here indicate an issue at the top of the hiring funnel. Use them to assess which methods of contact are the most and least successful (e.g., phone, email, social media, and the job ad itself) and pinpoint where the problem is occurring.
Time-to-fill rates. This is a key metric for predicting how long it will take to hire for a variety of roles. You can use this information to inform stakeholders, improve candidate experience (by being transparent about the hiring timeline), and identify holes or bottlenecks in the funnel. Time-to-fill rates are also useful when forecasting business needs and planning future talent acquisition strategies.
New hire performance and turnover rates. These rates provide insight into the quality of hire. Cross-reference these with submission or response rates to get an idea of how effective your sourcing methods are. This information can also be used to create profiles of which types of candidates are most likely to perform well on the job – and which are most likely to become disengaged after getting settled.
Developing an Attack Plan
Analytics and Big Data are still young, and businesses still have hurdles to overcome if they want to gather and interpret data properly. Unfortunately, recruiting and HR technology haven’t yet caught up with the changing landscape, and much of the data they collect remain siloed. If you want to leverage data effectively, you’ll need a plan – and you’ll need a team.
Know the Objectives
Seek to understand what could be different or changed because of the results.
- What are we trying to achieve?
- What information do we ideally need to make a decisive choice or course correct our current direction?
- What is the real business problem we’re trying to tackle
By identifying the answers to these questions, we can work backward to uncover the appropriate data. Once the data is harvested, you can then create an action plan, one step at a time. It’s important to note that, as with any data-driven approach, changes should be implemented incrementally. Trying to achieve too many objectives at once could skew the resulting data and make tracking performance difficult or impossible.
Build the Right Team
Time and time again, research demonstrates that groups outperform individual analysts. Of course, the team needs to collaborate effectively and communicate well. For those who do, predictions and analyses are often far more meaningful than those presented by individuals working alone. With the growing emphasis being placed on Big Data in recruiting, one of the most important things you can do is dedicate a team to processing, reviewing, tracking and reporting on that information.
Designing the right team is imperative and should take place before any data collection or analysis occur. The best teams include a broad swath of representatives. In an outsourced workforce program, that would incorporate professionals from the client organization, the MSP, the VMS, and staffing partner firms. These subject matters experts will be required to address the Whys, the Whats and the Hows of the project.
- Why: hiring managers, operational leaders, and executives to provide the business expertise.
- What: staffing partners, procurement leaders, and HR officers to provide expertise on the talent.
- How: Data analytics specialists from the MSP, client organization or technology provider (e.g., VMS) who understand the information, how to gather it and how to interpret it into meaningful results that decision-makers can act upon.
The Difference Between Big Data and Meaningful Data
“Big Data is going to revolutionize recruiting” is something we hear often, and it’s something we tend to agree with. However, data must be carefully curated and analyzed for it to be meaningful. And to achieve that, we need to both streamline and expand our data analysis strategies. We can streamline the process by identifying and concentrating on the datasets that are most relevant to the objective, and we can expand it by collaborating with representatives of departments or platforms where the data is traditionally stored. By starting with small, targeted efforts, we can effectively scale as big data continues to grow. In this way, Big Data in recruiting becomes an actionable strategy - and not just a fancy buzzword.