General business | Specialist | Expert

4 pillars of ethics & trust in big data Workplace Analytics

The rise of Workplace Analytics software has revolutionized the way organizations measure team collaboration and engagement levels. However, these sorts of solutions can’t operate ethically without the right safety & privacy measures in place.

In this post, we’re going to take a look at the 4 pillars of Workplace Analytics ethics – bias, people impact, privacy, and security, and how you can abide by them.

4 pillars of ethical Workplace Analytics software

# 1: Privacy

A few years ago, The Daily Telegraph decided to abandon the use of devices that monitored if their employees were at their desks. The solution was heavily criticized by the National Union of Journalists and perceived as surveillance. This is just one example, and the issue of privacy is often the subject of heated discussions.

To gain a deeper understanding of privacy, and know how to protect it, ask yourself the following questions:

Is my information private?
Am I aware of the information gathered?
Can I view the information?
Am I able to edit or fix information that is incorrect?
Can I be forgotten?
What will the consequences be if the information is leaked?

Here are a few recommendations that will help you better ensure data privacy:

Be transparent about what you do with employee data
Don’t analyze data on a per-employee basis, focus on teams
Run a Data Protection Impact Assessment to better understand and foresee potential threats and come up with a plan to prevent them.

At Network Perspective, we pay a lot of attention to privacy. Before we add any new data tracking options, we cooperate with GDPR lawyers and update DPIA for standard app implementation.

#2: Security

In order to understand the role of security in PA software and whether you follow it at your organization, it’s essential to ask yourself the following questions:

Is your data encrypted?
Who is authorized to view Workplace Analytics data?
What types of cybersecurity measures do you follow?
Does the organization hold itself accountable for damages or security breaches?

To protect your employees’ data security, seek to get the most output of the least data input (i.e., using the smallest needed amount of information required to achieve your goals).

We also recommend hashing employee data to protect it from both internal and external access attempts (hashing will change names and surnames into numerical values).

So, how do we secure PA data at Network Perspective? While accessing employee interaction data (such as email, calendar, etc.), we only access metadata that describes traces of interactions. Interaction information is not downloaded, and the metadata we extract is encrypted / anonymized. Finally, we don’t display data on a per-employee level, but present data in an aggregated, team-level tier (of groups of at least 5 people). As a result, the team and their leaders receive access to crucial insights, all the while protecting employee privacy.

#3: Bias

Do you recall the time when Amazon experimented with an AI recruiting tool that scored candidates on a scale from one to five to find the best talent? Unfortunately, it turned out that the tool wasn’t gender-neutral and it put women at a disadvantage, so the company has decided to scrap it. This is a great example of how bias can take place in recruitment.

As part of Workplace Analytics team, ask yourself the following questions to make sure that you look after the privacy of your employees and that there is no room for bias:

How will my age, gender, race, education, and other demographic information be used?
What safeguards and processes are there to remove bias as regards all diversity wheel dimensions?
Is the AI auditable?

To protect your processes from bias, put mechanisms in place that will allow employees to question decisions based solely on automated information processing. At Network Perspective each person who sees their team results can challenge them with one click!

#4: People impact

As part of the Workplace Analytics team, you have an impact over which projects are carried out. It’s vital to only proceed with those which have a positive impact on the employees. Refrain from using data in a way that puts your employees at a disadvantage, for instance, to identify the weakest players.

To make it a little easier for you to ensure that the data you collect is used in an appropriate manner, answer the following questions:

Is this information used to save money or reduce costs?
How will this make people’s lives or the workplace better?
How will this improve customer satisfaction?

We use data at Network Perspective to assess the environment that people work in, and to improve it. However, our analytics isn’t really about the employees themselves, it’s about the workspace, workplace, work habits, etc. We try to influence the situation that people are in and optimize it to make it better.

Closing remarks

If you’re considering implementing Workplace Analytics software at your business (or are looking for a more ethical way to process employee data), it’s important to check whether it abides by the four pillars mentioned above. Your PA platform needs to respect privacy, secure data to prevent breaches, eliminate biases, and genuinely impact your people in a positive way in the first place.

Network Perspective ticks all four points off the list – if you’re interested in seeing our tool in action, reach out! We’d love to discuss your team collaboration objectives.