What would you ask if you truly knew your data? Many organizations claim to be data driven but don’t know what the data is meant to be telling them or how they’d fix the issues they think they have. Now this may not be you, but like many CIOs and VPs of Talent Acquisition, you might be grappling with the sudden influx of new figures, data streams and the demands from multiple departments to make sense of it all…so you can solve their issue. Instead, take a step back and look at the 6 questions you’ll be asking soon:
Now: Are we hiring the right people?
Soon: Are we hiring the right people for next year?
The Data Difference: Hiring tells one story, retention tells another, predictive succession planning is something else entirely. Many analytics providers give companies a look back into their own messy data and mistake correlation for causation. By knowing the questions to ask (and your plans for solving any issues that crop up) you turn data from a chart into a map.
Now: Who should we promote/eliminate/replicate?
Soon: How can we maximize the effectiveness of our pipeline?
The Data Difference: Call it the “do you want fries with that?’ of talent management. Learning to see your talent pool (internal and external) as a pipeline can help your recruiting and planning team determine phases and compensation strategies with an eye to how effective the overall workforce can (and should!) be. Marketing folks understand this which is why CRMs are loaded with pipeline identifiers and stage setting goals.
Now: How can we maximize our recruitment spend?
Soon: Where can we eliminate recruitment spend?
The Data Difference: In every BDAS instance (still in beta though!) we’ve implemented, we’ve pulled budget from a large vendor. You’d think the VP of HR would be thrilled but because the data almost never shows the original model to be effective or efficient, it’s hard to have people get excited about cutting that spend. Insider tip? Decide ahead of time where you will reallocate money.
Bad data or poor data quality costs US businesses $600 billion annually.
Now: What can we learn from our historical data?
Soon: What do I need from our historical data?
The Data Difference: I’ve said it before, digging around in historical data and assigning numerical scores in the hopes that some workforce analysis will jump out at you is NOT a good use of big data in talent acquisition. Instead, identify the levers you want to pull, the ones that will make a difference in your business. Once you’ve identified them, map out your solutions and then get into the data. With a purpose and a plan; that’s the best way to start rooting around in that stuff.
“Organizations see a potential boon in actionable insights derived from big data, not only to sell more widgets and services, but also to better manage healthcare, stop the flow of counterfeit drugs, track terrorists, and maybe even track your phone calls. Hence it's a given that big data isn't inherently good or evil. It's how you use it that counts.” - Jeff Bertolucci
Now: How do we manage our big data?
Soon: How do I manage multiple sets of small data?
The Data Difference: Savvy strategists are re-focusing their attention back on the value that data brings. They understand that data is just as meaningless dusty and unused, as spiffed up and misunderstood. We’ve (all) started learning what “big data” really is…and how much of it is actually marketing hype. (Some companies do truly have big data concerns and issues, but not near as many companies as you may think.)
Now: How do we get from data to information?
Soon: How do we get from information to understanding?
The Data Difference: While the term big data seems ubiquitous, the term itself hasn’t been used for all that long. It’s overwhelming for companies looking to extract meaning and squeeze understanding from a concept whose very name is behemoth. Right now, few companies are scratching the surface of the understanding piece, but the few that are, have spent the time trying to get their data in a place where it can provide ongoing, and useful information. Understanding, a mere few steps away, comes from essentially getting your data ducks in a row and learning from that before trying to delve into the understanding (or predictive piece. And that…takes time.
90% of the world’s data has been created in the last two years.
Data - driven companies love Broadbean, because we tell them the truth about how big data works. Learn more about what Broadbean can do for your big data recruitment this year.