Radical transparency is often misunderstood. For most people, what comes to mind first is the sharing of sensitive financial and/or people data, but this isn’t the case.
Early on in my career, I felt a strong pull to keep information compartmentalized on a need to know basis. I believed that it was easier to manage a team when they didn’t have to worry about all of the existential risks coming around the corner. I couldn’t have been more wrong.
Our journey to radical transparency emerged naturally — through a process of trial and error. The more we shared, the better the outcomes we were able to deliver.
To me, radical transparency is about providing access to data that helps teams at all levels make the best decisions possible.
I’m going to break down two easy examples of how you can implement radical transparency in your organization. These ideas are specific to the context of a technology company, but could be applied with a little bit of effort to other verticals too.
The Personal ReadMe document describes who you are and how you work. It’s a small step toward transparency, but a significant one. People are often intimidated by the exercise and take longer to write the document than you’d expect. When team members and outside parties start reading them, they pickup on critical new information that helps them optimize how they work with a member of your team.
This is a truncated version, I’m happy to share full examples if you’re interested
As individuals see the results, we see them revise their ReadMes to be more useful. They realize no one is upset at them for being imperfect and that their coworkers are, in fact, happy to have guidance on how best to engage with them. Daily interactions at work became easier and more frictionless, and everyone is happier.
Backlogs are a simple but effective transparency tool. Everyone in the organization should make their todo lists public — from ICs to the CEO.
When we have transparency into what our colleagues are working on now (and next), the organization as a whole can interact with its constituent parts better and can course correct on misalignments in real time.
Many people are terrified of openly sharing their backlog. Some people feel that keeping their backlog private provides an internal proprietary advantage and allows them to demonstrate more value. Or that it provides job security. In reality, sharing backlogs makes work more efficient and critically exposes mis-alignment of priority and mission.
It also leads to important dialogue about the prioritization of different efforts across the team. Other team members will ask why you chose this priority over that one, and the answers you give will provide important context about your job and how those around you can support your efforts.
One of the classic datasets that organizations on their journey towards radical transparency wring hands over is compensation data. There are many good reasons why you may not want to make this information public. But there are plenty of examples of organizations that made compensation data transparent benefiting from tremendous upside. Some of the upside worth pondering: employee motivation, efficiency, and creating clear guideposts for growth.
There’s something motivating about knowing where you stand in comparison to your colleagues. If you’re in the same job function as someone else and they make 50% more than you, that naturally begs the question of “Why?”
Sharing compensation data is just the start. It incentivizes the creation of many systems that help employees grow along their individual journeys.
Transparency takes the frustration and mystery out of “I want to make 400K a year” by giving a clear path of role, job functions, and work ethic that others can see and emulate.
There is an expectation that compensation is inherently unfair. It may be, but most of the time what’s unfair is the lack of structure and justification that goes into a given compensation system. Bringing transparency to it starts important discussions around how this transpires throughout the organization.
Sales teams are often protective of their relationship with the customer. Getting product decision makers close to the actual end user is the most important aspect of transparency when it comes to building a great product, but this is often stymied by fears about damage to the relationship if communication isn’t micromanaged by the relationship owner.
It’s very easy for product teams to become isolated through proxies like analytics data, and user flows, while not actually having a direct relationship with the customer.
Essentially, they’re making a lot of guesses. That’s where radical transparency really shines. If you can make the product team part of the client relationship, that’s where real value is unlocked.
The product team should always be in the room when making promises to the client. This doesn’t need to happen during the early pursuit stage, but during SOW development, scoping, and when close to the finish line, it’s critical to get the product team’s insight into timeline, features, roadmap, etc. and even more important for them to be a party to the promises you are making to the client.
Give product teams access to sales call recordings, CRM data, and communications/emails from the sales team to the client. Radical transparency is all about providing as much data as possible to enable informed decisions, and this is an easy (yet often overlooked) way to reduce guesswork for product teams.
Please keep in mind you have to create rituals that force stakeholders to actually consume the information in order to make working this way a habit (shared inbox for sales/support, call recordings and summaries, etc.)
What are the smallest decisions being made in the organization? What information is necessary to make them well? Every organization will have different answers to these questions. In some cases, team members will need access to more information on customers. In other cases, transparency around costs is critical. Yet another organization will benefit from something more radical like sharing compensation data. It’s up to you to experiment and determine what works best for you, your team, and your organization.