Conveying the importance of platform engineering (DevOps, SRE, Data, QA, etc) and IT is a challenge for not just non-technical individuals, but everyone. Additionally, with each team working in vastly different spaces, it can be a challenge to compare the value of different initiatives. It is this exact problem that led me to create TRM.
Since the value created by every team falls into the three TRM buckets of time, risk, and money: it made it easy to convey progress on a weekly/monthly/quarterly basis. Our quarterly reviews often looked something like the following (not actual data):
Time
When it came to time, the focus was on where we could save the company the most time. We displayed the time savings as a % of an FTE (full time employee) saved to give context. (Using hours, while impressive when there are over two thousand hours a year per FTE, doesn't convey the value to the company). In the above, this shows we saved the equivalent of more than two FTEs worth of time by the end of the year. We also provided the same data broken down to which teams/departments received the time savings.
Note: Not actual data
In the above, this shows we saved the equivalent of more than two FTEs worth of time by the end of the year.
Risk
We measured risk as impact and likelihood. This gave us a score for all risks that could be compared. The trick is conveying where we are, and where we came from.
This is my favorite visualization, a radar chart. If we think about risk, our goal is to reduce it as much as possible, but it will never be eliminated. The radar chart visually shows this, as the area shrinks as the company's exposure to risk also shrinks.
Note: Not actual data
In the above, the chart quickly conveys where the company started, where it currently stands, and where it's headed. For each of those points in time, we can quickly identify where we are doing well and where our opportunities are. Like time, this chart can be broken down into finer detail (by vendor for example).
Money
The most straight forward of the values, money. This was our area of focus, reducing vendor spend, so it was where our time was spent the most. I wish I could share the actual visualizations for our tracking, but here is roughly what it looked like.
Note: Not actual data
Teams
Each team is distinct in their focus. How does each line up with TRM? ...
Dev Ops (+43%)
DevOps teams are focused on Ci/Cd, deployments, production availability, and security. How many people did I confuse?
Value
DevOps serves three customers: product engineering, risk management, and finance.
For product engineering, they provide a structured, consistent environment so that teams can test code and release it to production, without focusing on the complexities of making that happen (time).
For risk management, DevOps (or DevSecOps) ensures the environments, particularly production, are following the best security and compliance practices.
Lastly, particularly for SaaS companies, often hold the keys to the most expensive vendor expenses, making them finance's best or worst friend.
Price
Like all teams, there is a financial cost with DevOps teams. DevOps engineers are above average in pay and often have a longer onboarding period (time).
Note: the % score will vary from company to company, and year to year. The bigger their customer base, the bigger value they can provide.
SRE (+38%)
SRE teams are focused on production monitoring, observability, alerting and availability. Clear?
Value
For SaaS, SRE has three primary customers: risk management, product engineering, and customer support.
For risk management, SRE's goal is to minimize the impact of a production incident. It does this by making sure incidents are less frequent, and incidents are handled quickly.
For product engineering (time), they focus on making it easy to determine when there is an incident (and when there is NOT) and providing the tools/training to resolve things quickly. Lastly, it provides processes to ensure incidents are not repeated.
Lastly, customer support (time) gains the ability to determine scope of incoming problems/bugs (just this user? people on the east coast? everyone?).
Price
Like any team, there are costs for people, but also a cost for the tools to enable observability.
Note: the % score will vary from company to company, and year to year. The bigger their customer base, the bigger value they can provide.
QE/QA (+29%)
QA/QE teams are focused on ensuring unit, manual, integration, regression, smoke, and/or load testing is performed before releasing. Obviously?
Value
QA's primary customers are risk management and product engineering.
For risk management, QA's role is to reduce the chance that a change to production creates an incident (it does it through all those types of testing).
For product engineering, the sooner a bug is found, the exponentially less time is spent fixing (so they can focus on delivering their value).
Price
Time is the biggest expense, where development is significantly slowed by the testing process. There is also a people cost, but QE engineers are typically less than other engineers.
Note: the % score will vary from company to company, and year to year. The bigger their customer base, the bigger value they can provide.
Platform (+32%)
I haven't seen this team elsewhere, but we had a team focused on the cross-functional areas of software development. Clear as mud?
Value
Snagajob's platform team's primary customer is the product engineering team, by reducing the time spent developing new products/features.
Price
Costs are mostly in people, but the tooling costs are substantially less than the other platform engineering groups.
Note: the % score will vary from company to company, and year to year. The bigger their customer base, the bigger value they can provide.
Data (+61%)
In SaaS companies, data is gold. OLTP data stores, message buses, data warehouses/lakes, ML/AI, data visualization, reporting and backups power every facet of the company.
Value
Data's primary customer is every employee, particularly in SaaS. Accurate, secure, and timely data access reduces company-wide communication, provides direction, and creates a competitive advantage.
Price
Data engineers are more expensive than most engineers. The tools to provide data storage, retrieval, and backups are also expensive.
Note: the % score will vary from company to company, and year to year. The bigger their customer base, the bigger value they can provide.