…So this evening I read a great article from Ashley Goodall (Deloitte) in Harvard Business Review. She discusses the process of re-designing Deloitte’s performance management process.
You can read the full piece here, but what was most interesting to me about this article was that it outlines how Deloitte made the decision to re-do their performance management process despite the facts that (i) a majority of the workforce thought the process was fair and (ii) that people liked the predictability of the process. The reasoning for the change in the face of these headwinds is captured in the quote below:
But the need for change didn’t crystallize until we decided to count things. Specifically, we tallied the number of hours the organization was spending on performance management—and found that completing the forms, holding the meetings, and creating the ratings consumed close to 2 million hours a year. As we studied how those hours were spent, we realized that many of them were eaten up by leaders’ discussions behind closed doors about the outcomes of the process.
^So this is powerful for many reasons, but perhaps the most significant of these reasons is the fact that effective performance has to really be a dialogue between the manager and employee. We can spend all the time in the world discussing how to improve an employee’s performance as managers… but the most impactful way to raise an employee’s performance is to have frequent, candid conversations with them about what’s working and what isn’t. As soon as a performance system loses sight of this – once it becomes more concerned with tracking trends and checking boxes than facilitating conversations – it ceases to be effective at the very thing it was designed for.
…I am really big on leveraging data and analytics when it comes to managing people and developing performance. Ergo, if it might be possible to quantify a success or identify a strong correlation between two outcomes I am generally prone to attempt to do so. And yet any insight derived from data is only as good as the inputs used to create said data. Ultimately, if we want to get good data to help us drive performance management decisions we need to do the hard (and good) work involved in yielding that data.
Yes, right? Is this too much common sense to dedicate 400(ish) words to? You tell me in the comments section below.