Tracking and analysing data
Tracking and analysing data
Organisations often have a wealth of data that can be used to help improve employee well-being. Using employee perception and objective organisational data to monitor impact and outcomes can help you make strategic decisions and give you a competitive advantage.
There are a number of ways to track your organisation’s progress by analysing collected data. You may choose to
- compare groups at one point in time, or
- examine one or more trends across time.
Collecting the same data for multiple groups allows you to compare the groups on their scores.
For example, if engagement scores are collected for each department in the organisation you can then graph the scores by group to examine similarities and differences. In the example below, a quick look at the bar chat highlights that the marketing team has much lower engagement scores than other organisational departments.
There are a number of ways here we can try and find out why the marketing team has lower scores, such as
- examining the marketing team’s engagement data in more detail (e.g. scores by item or sub-category, as examined below),
- selecting a number of marketing team employees to conduct interviews with,
- run a marketing team focus group to find out common themes that may be impacting on engagement levels and then using that information to make strategic decisions.
Given we already have engagement data for the marketing team, we may choose to more closely examine that first, by looking at individual item scores. In the example below we have graphed the highest and lowest item scores for the marketing team. Immediately, we can see that while the marketing team feels they have a good work-life balance and a sense of belonging, they do not feel as if they have much autonomy or gain much enjoyment from their roles. We still can’t say specifically what is causing the low scores, but now that we have an area to focus on, we may choose to follow-up with a focus group to help inform future changes.
Tracking data over time
By measuring the same things on a regular basis (for example bi-annually, annually, or biennially) and looking at the data together, you can start to identify trends over time.
In the example below we have graphed organisational engagement scores for each year from 2010 to 2019. By looking at this data across time we can see that from 2010 to 2015 engagement scores were similar, varying from 4 to 5 out of 10. However, in 2016 engagement scores increased from 4.5 to 7 out of 10, and haven’t changed much since. Overall, this data shows us that compared to previous years this organisation is showing a good pattern of improvement, but there is still some room for improvement.
Tracking data over time is also useful for monitoring the effectiveness of activities, as changes after the implementation of activities can be monitored.
For example, if you decide to implement flexible work arrangements you may notice differences in the level of employee engagement, compared to previous years where no flexible work was not available to employees. While you cannot guarantee that the introduction of flexible work arrangements is the reason for the boost in employee engagement, you may conclude that it is likely the program is having a positive impact and is worth continuing. Alternatively, if employee engagement decreased following the introduction of flexible work arrangements, you may conclude that it is likely the functions are not having a beneficial impact and require readjusting or replacing.
To explore this further you can gather employee perceptions data specific to how they felt about the introduction of flexible work arrangements. By doing this you can more accurately assess whether changes in employee engagement were likely because of the flexible work arrangements, or may be due to something else that is unrelated.
If your organisation already has multiple sources of data it is useful to combine data from numerous sources. Having a more holistic approach to evaluation may provide you with more insights into how your organisation is positioned.
For example, data collected by Human Resources may be kept separate to data collected by Health and Safety. However, each piece of data alone only shows a part of the whole organisational story and combining data sources will give a whole of organisation view.
In the example used above, employee engagement scores may have increased following the introduction of flexible work arrangements. You may be interested to see whether this increase in engagement corresponds with other organisational outcomes of interest, such as safety incidents (e.g., lost time injury frequency rates (LTIFR)).
If we put those two sources of data together in the same graph, we find that engagement and safety improved after the introduction of flexible work arrangements. Thus, combining data can be a more strategic way to see if activities impact a number of outcomes to give more holistic insights.
While we are unable to determine if flexible work arrangements caused an increase in engagement and reduction in safety incidents, we can track this further to see if the improved engagement and safety outcomes is sustained and perhaps investigate further the relationship between these variables. It is likely that the introduction of flexible work arrangements positively influenced engagement scores, and improved engagement had positive flow on effects to safety. However, we would need to test this using more sophisticated analyses.
Privacy and Confidentiality
When collecting, analysing and reporting data it is important that you maintain the privacy and confidentiality of your employees.
Not only is it a legal requirement, but you will have higher participation rates and more genuine feedback if you maintain confidentiality and consistently demonstrate respect for employee’s privacy. You can ensure privacy and confidentiality by following the below guidelines during collection, reporting and retention phases.
Avoid asking for identifiable data and/or linking identifiable information to actual employee responses. For example, sometimes you may need to collect email addresses to send out and receive a survey, but it is important to make sure the email address and/or participant name is not recorded against the survey responses, in a way that can be used to identify individuals.
- Always inform employees on the purposes of collecting data.
- Only collect data which is necessary.
- Employees should have access to and knowledge of the organisations policies relating to collection, reporting and retention of data.
- Apart from where collection is required by legislation, employees should always have the option to opt-out of taking part in data collection.
- Always seek consent for the collection, use and disclosure of employee perception data.
- Try not to report data for small groups of participants due to the possibility of jeopardising anonymity. If you need to report data for groups under 10 participants, make sure that it is presented at a high enough level to ensure no one individual’s results can be linked to them.
- Do not report names, or any other identifiable characteristics.
- Reported data should be as accurate, complete and up-to-date as possible.
- Personal information should only be retained for as long as is necessary to fulfill its collection purpose.
- Personal or sensitive information should be protected by security safeguards (such as passwords, or stored in locked files).