"Software is eating the world."
"You can't manage what you don't measure."
Organizations from all industries are embracing software as a way of delivering value to their customers, and we are seeing software drive innovation and competitiveness from outside of the traditional tech sector.
For example, banks are no longer known for hiding gold bars in safes: instead, companies in the financial industry are harnessing software in a race to capture market share. Using innovative apps, banks are making it possible for their customers to do most of their daily banking in a few swipes, from depositing checks to transferring money securely between bank accounts. Moreover, the banks themselves can improve their service in a number of ways, such as using predictive analytics to detect fraudulent transactions. Other industries are seeing similar changes: cars are now computers on wheels, and even the U.S. Postal Service is in the middle of a massive DevOps transformation. Software is everywhere.
Leaders must embrace this new world or step aside. Gartner Inc. predicts that by 2020, half of the CIOs who have not transformed their teams' capabilities will be displaced from their organizations' leadership teams. And as every good leader knows, you cannot improve what you do not measure, so measuring the software development process and DevOps transformations is more important than ever.
Delivering value to the business through software requires processes and coordination that often span multiple teams across complex systems, and involves developing and delivering software with both quality and resiliency. As practitioners and professionals, we know that software development and delivery is an increasingly difficult art and practice, and that managing and improving any process or system requires insights into that system. Therefore, measurement is paramount to creating an effective software value stream. Yet accurate measurement is no easy feat.
Measuring DevOps. Collecting measurements that can provide insights across the software delivery pipeline is difficult. Data must be complete, comprehensive, and correct so that teams can correlate data to drive business decisions. For many organizations, adoption of the latest best-of-breed agile and DevOps tools has made the task even more difficult because of the proliferation of multiple systems of recordkeeping within the organization.
One of the leading sources of cross-organization software delivery data is the annual State of DevOps Report (found at https://devops-research.com/research.html).2 This industry-wide survey provides evidence that software delivery plays an important role in high-performing technology-driven organizations. The report outlines key capabilities in technology, process, and cultural areas that contribute to software-delivery performance and how this, in turn, contributes to key outcomes such as employee well-being, product quality, and organizational performance.
Bolstered by this survey-based research, organizations are starting to measure their own DevOps "readiness" or "maturity" using survey data. While this type of data can provide a useful view of the potential role that DevOps can play in teams and organizations, the danger is that organizations may blindly apply the results of surveys without understanding the limitations of this methodology.
On the flip side, some organizations criticize survey-based data wholesale and instead attempt to measure or assess their DevOps readiness or maturity using system data alone. These organizations, which are creating metrics based on the system data stored in their repositories, may not understand the limitations of that methodology, either.
By understanding these limitations, practitioners and leaders can better leverage the benefits of each methodology. This article summarizes the two separate but complementary approaches to measuring the software value stream and shares some pitfalls of conflating the two. The two approaches are defined as follows:
Neither system data nor survey data alone can measure the effectiveness of a modern software delivery pipeline. Both are needed. A complementary approach to measurement can arm organizations with a more complete picture of their development and operations environment, address the key gaps of each approach, and provide organizations with the information they need to develop and deliver software competitively.
As an analogy, consider how a manufacturer may track the effectiveness of a complex assembly line. Instrumentation at each step provides data on rates of flow and defects within each phase and across the end-to-end system. Augmenting that with survey data of the assembly line staff can prove invaluablefor example, discovering that a newly deployed cooperative robot is putting more physical strain on employees than was promised by the robot vendor.
Capturing that information before higher defect rates, lower employee survey scores, or even lawsuits arise can prove invaluable. In this example, the survey data provides leading indicators to system data, or provides insights that system data might not disclose at all. Whereas assembly line manufacturing is extremely mature in terms of metrics and data collection, there is a severe lack of industry consensus on how to measure software delivery. This implies that this practice is still in its in infancy. (Note that this is likely related to the relative maturity of the fields themselves: the manufacturing discipline has been around for a long time, so those who study and measure it have had several decades to perfect their craft; in contrast, software engineering is a relatively young field, making its measurement study much less mature.) As such, it is critical for organizations to understand what they can and cannot measure with which approach, and what steps they must take to gain visibility into their software delivery value streams.
Using the authors' collective decades of research and experience in collecting both survey and system dataconfirmed by in-depth discussions with hundreds of experts at dozens of global organizations who make software value-stream measurement a key part of their digital transformationthis article outlines the measures necessary for understanding your ability to develop and deliver software.
There are several reasons why both system and survey data should be used to measure the value streams that define your software-delivery processes. One of the most important is that most organizations seem to have almost no visibility or reliable measurement of their software-delivery practices.
The earlier an organization starts measurement, the earlier a baseline is established and can be used for gauging relative improvement. For a small organization, applying system metrics as the initial baseline can be easy. For example, a 20-person startup can measure MTTR (mean time to repair) using just an issue tracker such as Jira. A large organization, however, will need to include service desks and potentially other planning systems in order to identify that baseline and may not have implemented a tool that provides cross-system visibility. We recommend getting started with baseline collection immediately, and for many organizations that will mean collecting survey data while efforts to capture and correlate system data are under way.
In the absence of complete system measurements, comprehensive surveys can provide a holistic view of your system relatively quickly (such as, within several weeks). Contrast that with full visibility of your system provided by system-based metrics. Getting end-to-end system data can be a long journey as you first must deploy a measurement solution across systems, and then make sure that cross-system integration is in place so the data can be properly correlated. Modern value-stream metrics are making this easier, but for many organizations this has been a multiyear project.
While it is important to start as early as possible to get the benefits of system data, deploying survey data provides an almost immediate value and source of baseline information. This is valuable both for baselining current and future survey data, and for comparing survey with system data once in place. Therefore, it is best to capture a system baseline with survey measures now while continuing to build out system-based metrics.
What happens once you are fully instrumented with system-based metrics? You can continue using your survey-based metrics for both augmentation and capturing additional data that's uniquely suited for survey methods.
Leaders must embrace this new world or step aside. Gartner Inc. predicts that by 2020, half the CIOs who have not transformed their teams' capabilities will be displaced from their organizations' leadership teams.
There are still some measures that are important to software delivery, such as cultural measures, that survey-based measures will pick up and system-based metrics may miss. In addition, having both types of metrics provides opportunities for triangulation: if your survey measures provide data that is drastically different from the data coming from your systems, this can highlight gaps in the system.
Some might say such a gap is just an area where "people lie," but if all of the people working closely with the system are lying, you might want to consider their experience as a true data point. If your engineers consistently report long build times and the system data reports short build times, could it be a configuration error in the API? Or could it be that the system-based measure is capturing only a portion of the data? Without consistently collecting insights from the professionals working with your systems, you will miss opportunities to see the full picture. The rest of this article outlines the pros and cons of each measurement type.
System-based metrics generally refer to data that comes from the various systems of record that make up an end-to-end software delivery value stream. Important aspects of this data include:
To use an analogy to illustrate: when building a house, a contractor may use concrete for the foundation; wood/nails/screws/drywall for the walls; wiring and plumbing; brick for the exterior; paint/carpet for the finish; plus any materials for the kitchen and bath. In order to track and monitor progress, you build in monitoring to track each piece of the construction and install it as the house is built. Once installed, each and every piece of this infrastructure (specific data) can continually provide reporting and metrics (continuous data) at subsecond intervals (precision). You can then combine and correlate (volume and scale) these to create a full picture of what is happening in your house.
System-based metrics are useful, but they cannot paint a complete picture of what is happening in your software-delivery work. Therefore, it is strongly recommended that you augment your metrics with complementary survey measures.
Survey-based metrics generally refer to data about systems and people (such as culture) that comes from surveys. Ideally, these surveys are sent to the people who are working on the systems themselves and who are intimately familiar with the software-development and delivery systemthat is, the doers. It is better for teams to avoid surveying management and executives, because, as a recent study by Forrester shows, executives tend to overestimate the maturity of their organizations.3
Important aspects of this data include:
Let's return to the house analogy. When using system data, you can get detailed information from each piece of the system that is reporting. This level of detail isn't possible (or realistic) when asking people through survey questionsbut you can very quickly and easily get a holistic understanding of what your system or its components are doing. For example, you can reliably ascertain if the house is in a good state: anyone can report if the house is on fire, if a room is dirty or smoky, or if an event has caused damage. This data can be gathered much faster than the time needed to instrument and then correlate and synthesize hundreds or thousands of data points. If your survey and system measures disagree, you have great cause to start debugging the system.
Software is driving value in organizations across all industries and around the world. To help deliver value, quality, and sustainability more quickly, companies are undergoing DevOps transformations. To help guide these difficult transformations, leaders must understand the technology process.
This process can be illuminated through a good measurement program, which allows team members, leaders, and executives to understand technology and process work, plan initiatives, and track progress so the organization can demonstrate the value of investments to key stakeholders. System-based metrics and survey-based metrics each have inherent limitations, but by leveraging both types of metrics in a complementary measurement program, organizations can gain a better view of their software-delivery value chain and DevOps transformation work.
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1. Azzarello, D., Debruyne, F. and Mottura, L. The chemistry of enthusiasm. Bain and Co., 2012; http://www.bain.com/publications/articles/the-chemistry-of-enthusiasm.aspx.
2. DevOps Research and Assessment. 2014, 2015, 2016, and 2017 State of DevOps Reports; https://devops-research.com/research.html.
3. Stroud, R., Klavens, E., Oehrlich, E., Kinch, A. and Lynch, D. A dangerous disconnect: executives overestimate DevOps maturity. Forrester, 2017; http://bit.ly/2Fs6Wjo
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