5 Components for Successful Organizational Analytics
Earlier this year, I had the opportunity to attend the Society for Human Resource Management (SHRM) People Analytics Conference. During one of the sessions, speaker Jack Phillips shared five components for successful organizational analytics, and I wanted to share those with you.
I really liked this model because I felt like it was something the entire organization could support. I also thought that the acronym for the model – DELTA – was particularly catchy. Some people like using the world delta as a synonym for change. So, think of the DELTA model as a guide for transforming the organization to an analytics culture. Here are the five components:
DATA: This is the obvious one. If you want to have any type of analytics program or culture, then you need data. The organization will want to assess its access to both internal and external data. They will also want to commit to things like data quality and data security. It’s a perfect time to remember the technology phrase “garbage in, garbage out”. Good data helps organizations make good decisions and a commitment to quality data is essential.
ENTERPRISE: I think of the enterprise component from two angles. First that the organization has the right enterprise technology in place to retrieve internal data. And second that their technology systems “speak” to each other, meaning that there’s an ability to look at the relationships. This doesn’t mean that organizations need to replace all of their systems, but it does mean they need to look at the capabilities of their systems. It’s possible that some areas will need to be addressed.
LEADERSHIP: It almost goes without saying that leadership needs to be on board with using analytics in their decision making. I don’t want to take anything away from “gut feelings” and “vibes” but let’s face it, they can be wrong. We need to remember that it’s not only that leadership is on board with using data and analytics, but that they’re willing to dedicate resources toward analytics projects. And by resources, I mean headcount and budget dollars.
TARGETS: Think of targets as goals or areas of the business that could stand some improvement. The idea is that organizations identify targets to begin analytics projects. I’ve always said that one of the worst things that organizations can do is ask for employee feedback and do nothing with it. Same applies here. There’s no reason to identify a target for an analytics project if the organization isn’t willing to react to the data.
ANALYSTS: This ties into the second and third points about enterprise and leadership. Not only does the organization need to have the technology infrastructure in place but they need to have individuals who are capable of interpreting the data and following the rigor to make good decisions. In some cases, organizations may want to hire data scientists. And organizations can also invest in developing employees to take on these roles as the organization expands their analytics capabilities.
Now more than ever, organizations will be looking for ways to improve performance and do it quickly. I’m not suggesting they will do it at the expense of employees. That’s exactly why using data and analytics can be a valuable piece of their economic recovery.
It takes time to develop an analytics culture. This is something that organizations want to think about now, so it becomes a part of their short- and long-term strategic plans. Looks for ways to benefit from this shift right now and in the future.
11