What are the challenges in analyzing data that organizations usually face?
The most traditional way to collect data has been a survey. Employee engagement survey, pulse survey, NPS surveys. This gives organizations point in time measures around a sample set to build their views on. Most survey tools are advanced enough to give insights into problem areas but suffer from a couple of downsides.
Challenges while analysing data
- The data tends to be point in time, typically after a significant event as an important announcement or right after periodic pay and performance cycles. These tend to bias and cloud the data
- The data in most circumstances is skewed, Its human nature to shy away from hard feedback or talk about issues so employees usually speak in neutrals or positives. The real grit and stuff is missing
These and the inability to analyze data real time limit the range and impact of such traditional practices leading to surprises.
The data per say is also challenging with few people understanding the correlations to build intelligence out of or experts that can help with visualizing it, thereby removing the noise from the message.
There is, however, hope
The biggest impact being felt on people and talent analytics is primarily been focussed on recruitment, engagement, retention and performance.
By the time the data is scrubbed, analyzed and interpreted the impact has already been felt typically in an exit, disengagement or disruptive fashion.
The potential however remains to get more interconnected insights to build intelligence from. With platforms like peopleHum that provide most of this first level intelligence out of the box now any organization, be it a size of a 100 or the size of 50,000 can now leverage the benefit of people analytics without investing in organic capability.