If you’re in the majority of organizations, digital transformation is likely a sore subject.
Although 97% of companies reported accelerated efforts during the pandemic, only about 1 in 3 succeed overall.
Consider Revlon: The premature rollout of a new ERP system disrupted production and fulfillment in 22 countries, resulting in a $64 million loss, a 7% dip in stock price, and a class-action lawsuit from investors.
While such large-scale failures are relatively uncommon, many companies struggle to meet targets and achieve sustainable change. After a series of failed initiatives, wasted resources, and missed opportunities, businesses often revert to familiar practices. This organizational inertia drags digital initiatives out for years instead of months.
The businesses that do succeed are those that choose the right priorities — and can reasonably measure progress against those priorities. If “what gets measured gets managed,” measuring digital transformation success can turn a quagmire into an achievable roadmap forward.
The problem: what to measure is different for every company.
The idea of measuring progress with standardized DT metrics is a little like measuring health by weight or BMI. (The Rock weighs 260 pounds and his BMI would categorize him as “morbidly obese,” which we know is not the truth.) It paints an incomplete or inaccurate picture in a situation in which context matters.
“The hard thing is that digital transformation means different things to different people,” says Amy Carillo Cotten, a consultant working with enterprise engineering organizations.
She explains that there is a lot of context that makes measuring success difficult:
One size metrics do not fit all. What success looks like is unique to your organization and depends on a number of factors like core competencies, architectures, components, and teams involved. Transforming traditional organizations will have different measures of success than digitally native tech companies as they often have more inertia to overcome.
Sustainable transformation goes beyond technology. It’s a cultural shift in ways of working and adopting modern development practices that support cross-functional collaboration, transparency, agility, and value delivery. Many companies underestimate the investment these changes require and struggle to measure it.
Enterprise systems are complex. Companies often underestimate the dependencies of their technical systems, hiring multiple consultants and vendors to advise and execute. These siloed efforts create inefficiencies that work against the goals of the transformation and harm visibility of progress as a whole.
Digital transformation is never (really) done. The purpose of the transformation is to be agile and responsive to market needs and changes and to continually optimize people, systems, and processes. How do you define and measure success in this context?
Because successful digital transformation is not just an evolution in technology but also in organizational structure and culture, there is often a misconception that it’s all nebulous and not able to be measured quantitatively.
In fact, the opposite is true.
Starting to establish (and measure) better development practices before embarking on a digital transformation will make it more successful. Having the right engineering metrics can help you identify areas for improvement and monitor the adoption of those practices. Digital transformation is more about a state change than completing a list of projects, and the longitudinal data can help measure that progress.
The sooner you can establish best practices to ensure that developers have enough time to do their work and that they are working on the right things, the sooner you are able to identify the major bottlenecks that are the biggest impediments to progress. These bottlenecks are likely going to be a key component of your transformation.
Digital transformation in engineering often suffers from trying to do too much at once, leading to inefficiency and lack of direction. While there’s no one way to measure success, it begins by identifying the biggest opportunities and prioritizing them. What technical priorities will help meet business objectives?
Matt Swann explains that “for digital businesses, technical priorities should be mapped clearly to key business priorities. It's critical to change the language from technical to business to ensure the business understands and cares about technical needs.”
Pre-transformation, technology priorities and business priorities might not have aligned. When the role of technology is supportive and not strategic, OKRs might look like achieving 99.999% server uptime or reducing IT support ticket resolution time — important goals, to be sure, but they don’t move the needle on a P&L.
Digital transformation changes that. Priorities now tie much more closely to what the business needs to achieve, and the metrics that become most important are the ones that reveal the sustainable progress of those priorities:
| Business Priority | Technology Priority | Engineering Metrics |
| Create a seamless omnichannel customer experience | Deliver frequent value in ecommerce and omnichannel features | Time spent vs. allocated to innovation and high-value initiatives |
| Quick response to market shifts | Replatform for scale and agility | Time spent vs. allocated to technical debt/KTLO work |
| Deliver X features this year without raising overhead | Adopt modern development practices | Delivery and quality (DORA) metrics |
Some metrics will be lagging indicators, but others can be early signs of where progress is happening (or not). While the data isn’t perfect, understanding actual vs. predicted performance allows engineering leaders to measure progress, adjust benchmarks, budget resources, and communicate with executive teams as to why.
No framework or set of engineering metrics will be perfect, but the right metrics will reflect a) the priorities of your transformation and b) the directionality and velocity of the change you are trying to make.
Successful digital transformations require engineering metrics, even if they’re too complex or time-consuming to pull on your own. As headcount is an engineering organization’s biggest budget item, tracking your team’s effectiveness (in terms of time allocation, efficiency, and sustainable ways of working) requires telemetry, just as any DevOps or Cloud monitoring platform would.
The right engineering intelligence platform enables you to align engineering efforts directly to key business outcomes. Combining standard productivity metrics with hard-to-surface indicators of how developers actually spend their time and work collaboratively, you’ll get a holistic picture of the metrics that truly matter for your transformation.