Moving the Engagement Data Glacier

We are seeing a trend towards a more constant streaming of data - we are looking at daily data or even hourly data wherever we can. Now, this is not to say that more regular data is always the best, but to my mind the trend is towards the finest temporal resolution that people can make sensible use of - which of course will be determined by a mix of subjective, pragmatic, and statistical (probably also somewhat subjective) factors.
Take house prices for example. Australians are obsessed with house prices and nearly as many are obsessed with arguing over the data and predictions. Recently we have seen the development of the RP Data-Rismark Daily Home Value Index which imputes a daily estimated house value for all Australian Capital cities. I won't be going into the methodology - the point I am making is that such is the hunger for frequent data that imputation methods are being developed so that we can keep an eye on what is happening daily. Interestingly, it appears some commentators have developed some fatigue in trying to comment on the results on a daily basis and are instead analysing the weekly figures. The important observation is that the data is allows people to make an informed choice as to what timeframe is appropriate.

Taking things to the extreme recently, Stephen Wolfram posted a mesmerisingly geeky/interesting blog article summarising hourly data for his email (ingoing, outgoing etc.). keystrokes, calendar events, phone calls and footsteps. The resulting dataset allows him to track various stages of his life and the overall patterns that have governed his working life since 1989. Some of the patterns are at the day level (more footsteps at lunchtime), some of them week level (writing a blog) and some of the patterns reflect multi-year trends (working on a major book). Again, Stephen's data collection allowed him to zoom in and out and decide the appropriate levels of analysis.

Now, where does this leave the average employee engagement data stream? With many companies asking their employees for culture feedback once a year - it seems engagement measurement is lagging nearly every other metric in the world. However, there are a growing number of companies who have realised that yearly culture and engagement tracking is not timely enough.

We regularly collect daily data from different employees and this allows leaders and managers to decide what the appropriate and meaningful timeframe for analysis is. We have observed interesting intra-day effects. However, for most companies, or business units undergoing change management, weekly data has been the smallest timeframe of value. Whereas other companies are settling into a rhythm of checking data on a monthly and quarterly basis. Since we have begun collecting this data we are yet to see an organisation who finds a yearly datastream adequate.

The verdict:

Hourly = academically interesting and fun to watch on your screen

Daily = see above

Weekly = beginning to become very useful in change management scenarios and large companies enacting initiatives - allows managers to try different things each week if they have large enough teams

Monthly = as above for weekly and great in change management, and larger companies, and also for linking to other monthly data such as customer satisfaction or Net Promoter tracking

Quarterly = commonly makes the most sense for many companies and ties in well with most other business metrics - many companies are gravitating to this as it also only requires each person to complete a 5-10 minute survey once every 3 months

Yearly = generally fine for global climate change data (sea levels, glacial melting etc.)

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