What data should we collect? Part 2: It depends…

Hopefully you are convinced that there is virtue in collecting data as a means to aid decision making, especially if you have read the first part of our blog series ‘What data should we collect?’. Now it’s time to think about the core question: what data is it that you should collect and how can you make the most of it? Our answer? It depends.


“It depends” is a fantastic reply to almost any relevant question that goes beyond yes or no. It was also the theme of a discussion I’ve recently enjoyed with like-minded professionals, business leaders, consultants and freelancers, brought together to discuss the concept of the ‘Minimum Viable Workplace‘ (i.e. what is the minimum that needs to be provided for people to be able to do their jobs). The suggestion that there should be a hashtag on twitter for questions to which the answer is #ItDepends got my little group discussion going on the day. But I’ve used “It depends” many times before in discussions with people, specifically about data collection strategies.

“It depends” as an answer could be seen as lazy or indecisive. But what a good “It depends” answer actually does, is to open up further debate. The conversation could go like this:

 

What this shows, is that data collection depends on the decision to be made. Too often, especially in a workplace context, we find that people get excited about a particular technology that collects some data without reflecting on what the business needs to decide and act on. In this case the technology (such as a sensor, or a mobile phone app, or a survey tool) dictates what data is collected rather than a thought through business strategy aiding decision making.

Let’s look at some examples:

  • An organisation might be interested in reducing their energy costs. Sensors collecting data on energy consumption over time, which could then be analysed and aggregated by floors in the building, by teams or possibly by devices using energy (lights in unoccupied rooms, computers left on over night) can highlight where the biggest savings could be made.
  • A growing business might be thinking about how to accommodate an increasing number of staff in its existing premises. Collecting data on occupancy of desks, meeting rooms and break out areas can put a spotlight on utilisation rates of specific areas, show remaining capacities of spaces and pinpoint potentially under-utilised areas. Low levels of desk occupancy could lead to implementing an activity-based working strategy.
  • A Sales Director could be concerned about the time the sales force is spending with customers. Again, gathering insights into the numbers of times desks of sales staff are occupied, could provide baseline figures to understand how actual patterns of mobility play out over time, and whether different strategies to get the sales team out on the road are required.
  • Retention of staff could be high up on the agenda of a business suffering from increasing staff turnover. An employee engagement survey could highlight issues of motivation, leadership or satisfaction. Different data collection strategies could be employed, too, as discussed in a recent study by McKinsey, which used personality tests and a sensor-based data collection of interaction behaviours to solve the same issue and bust long-held myths of the senior management team on what works and what doesn’t.
  • A co-working space might want to know which shared facilities to offer and where to place them in order to attract new members and keep existing ones. Collecting data on how well facilities are used, possibly combined with a survey of its members (or an app that rates facilities) can give clues on preferences of co-working users. A spatial analysis of where facilities are placed and how easily reachable they are to what group of users could be another data collection strategy to aid this decision on the provision and placement of facilities.

What decision makers do with the data once it is collected is a crucial part of the question about what data to collect. You should always ask: How does my data inform decisions? How does it guide actions? The long answer to “What data should we be collecting?” is therefore “When you know the decisions that need to be taken and you understand the range of possible actions that might be decided on, you will know what data to collect”.

What is also clear, is that there is not a single answer to the question which data helps with which decision. In the examples above, seat occupancy data is useful in two different scenarios (accommodating growth and encouraging the sales force to spend time with customers). It also works the other way, since a single problem such as staff retention could be addressed with different data collection strategies. Which one works best is then a question that depends again, this time on what is feasible, practical, available and manageable. (We will talk about this one in more detail in our next blog, so watch this space).

So when someone answers your request for advice on workplace, or in fact any other data collection with a thoughtful “It depends…”, ask back “On what?”, then lean in and learn from the discussion that unfolds.

Read part 3 of this series of blogs here

3 thoughts on “What data should we collect? Part 2: It depends…

  1. Pingback: What data should we collect? Part 1: Why bother? | brainybirdz

  2. Pingback: What data should we collect? Part 3: Big data vs small data | brainybirdz

  3. Pingback: What data should we collect? Part 4: The organisational reality | brainybirdz

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