Why Axonius chose to work with Acumen

A month ago, the R&D organization that I lead at Axonius became a customer of Acumen.io: the engineering intelligence platform that helps dev teams go from firefighting to shipping on time. 

We did not make this decision overnight. As engineers ourselves, we scrutinize every investment that we make in development technologies. And we constantly ask ourselves: are we solving the right problem? Do we truly need this? Is it something we could build ourselves?

But I believe that our partnership with Acumen will transform our R&D operations and help fulfill my vision of a fully scaled, data-driven engineering organization. 

Here’s why. 

Growing pains

Engineering leaders in hyper-growth organizations often describe a turning point in the growth of their teams – when the old ways of transferring knowledge and keeping the team aligned start breaking down. 

This happens in the way Hemmingway described going bankrupt: “Gradually, then suddenly.”

Teams get out of sync. Leaders feel out of the loop. Velocity and quality plummet. 

In my role as an R&D leader, success means being able to see around corners. And even before the pandemic, I sensed that Axonius was approaching this inflection point. It was getting harder to keep a pulse on everything that was going on across the R&D organization – much less to identify blockers and risks ahead of time. 

From what I hear from my peers, this is a universal challenge. But historically, there haven’t been many tools available to promote visibility and data-driven decision-making as R&D teams grow. Git analytics or JQL are a jumping-off point, but they tend to offer a flat, one-dimensional view of data from a single system. 

A new software category

By the time I crossed paths with Acumen, I had become aware of a new category of software: engineering intelligence platforms.

This category of technology exists to solve challenges like the ones that I saw on the horizon at Axonius. Generally speaking, these technologies:

• Bring together data housed in multiple engineering data systems (in contrast with pure git analytics tools)
• Provide teams and leaders with access to that data in the form of queries and data visualization
• In some cases, provide guidance in interpreting the data – by using machine learning to boost signal or explain what’s driving results (more on this later)

The category is still in its early days. But it doesn’t seem like an exaggeration to say that this type of technology will fundamentally reshape how software is built. 

You see, even as other parts of the organization become more data-driven (think of how Gong.io transformed sales), key elements of R&D – from planning and daily prioritization to iteration reviews – have remained largely driven by intuition. Forward-looking R&D teams that can harness the power of data will be able to unlock massive additional output and impact. 

But I was also skeptical. 

After all, it’s not difficult to imagine startups fueled by investor lust for the fast-growing dev tools market, churning out features that sound good to the investor class – but don’t actually drive usage or value in practice. Snazzy dashboards that get pulled out only during board meetings. Vanity metrics that don’t move the needle. 

Before investing in the category, I needed to be certain that Acumen would work for us. 

R&D leadership first principles

The deeper I researched engineering intelligence platforms, the more I became convinced that the only way to truly crack this product category is to start from three “first principles.” 

These are three essential tenets in the decision-making process of any R&D leader. And the more I learned about Acumen, the more convinced I became that they had architected their platform with these principles in mind: 

• Context: Context is what helps an R&D leader make meaning out of information – what turns data into insights. Most fundamentally, it requires understanding how metrics are trending and how they compare across individuals and teams. It also includes the ability to group, smooth, and extrapolate data to reduce noise and boost signal. One of the things that I value most about Acumen is that every KPI is embedded within a rich, multidimensional context: I can see at a glance how a metric is trending, slice into it for more granularity, and see how it correlates with other metrics. 
• Flexibility: Two points here: 1) Each engineering organization is unique and has a different point of view of what success means for them; and 2) It’s impossible to know in advance everything that an engineering leader might want to learn about his or her team. That’s why it’s helpful to have the ability to ask questions flexibly of the data, rather than being hemmed in by a set of predefined metrics. While still providing a default option, Acumen offers tremendous customization of metrics, timeframes, and querying options. The platform is built to grow with leaders and their teams as analytical maturity deepens. 
• Guidance: Knowing that something is wrong is like half a fork: it’s definitely part of what you need, but it’s fairly useless on its own. R&D leaders are overworked and need guidance on where to turn when there’s a problem. Creating alerts like “5 tickets are overdue!” or “your cycle time is getting worse” without providing a clear path to mitigation options is a classic example of a software dead end. Acumen started by tackling the immensely daunting task of training machine learning algorithms to mine through data and identify the root cause of issues – so that it can proactively surface the why behind every metric, and provide proactive recommendations on mitigation. 

Ultimately, it became clear to me that Acumen hadn’t started with a slick fundraising pitch. Rather, they decided to tackle the challenge bottom-up: furnishing teams with the context, flexibility, and guidance to accelerate them to first value – and drive stickiness and utilization over the long term. 

Parting thoughts

The question of how to make R&D teams more data-driven is one of the most important of my lifetime. What’s at stake is nothing short of engineering teams’ ability to deliver world-changing innovation.   

That’s why I feel privileged to be working with a brilliant and inquisitive team as they continue evolving their software platform. 

Acumen began with deep empathy for the actual challenges of leading an R&D team. Its leaders saw firsthand how hard it is to find signals in messy engineering data – so they pioneered machine learning to identify hidden patterns across datasets. They built customizable, context-rich alerts to facilitate better daily standups, and root cause pinpointing to make retros more data-driven. From estimation and planning through iteration review, their platform is built to mesh with and enhance the operating rhythm of an agile team. 

Most importantly: at every step of the way, Acumen has built a platform that doesn’t just look good on a marketing website, but drives genuine and sustained value for engineering teams.   

Solving the challenge of preserving visibility and alignment as engineering teams scale won’t be easy. But I’m confident that Acumen is poised to unlock the promise of this transformational new software category.