Kick-start data-driven engineering in your organization

Scattered, incomplete, and messy data can plague the efficiency of software development cycles. Without proper data management, your team will likely struggle with bottlenecks and redundancies, slowing development and wasting resources. A lack of suitable data handling can also lead to a lack of visibility, making problem-solving more challenging for you and your team.

Data-driven engineering may be the solution your team needs. Leveraging data collection and analysis strategies can help leaders gain valuable insights into their development lifecycles. This process empowers team members to identify issues, prioritize features that improve customer experience, and make data-driven decisions to enrich performance.

Pluralsight Flow is not just a platform, it's a tool that empowers your team. It equips them with the ability to measure output, evaluate productivity, and use data more efficiently to overcome development challenges. Let's delve into the benefits of data-driven engineering and the seven tenets that will give your team the confidence to get started.

• What is data-driven engineering?
• 5 benefits of data-driven engineering leadership
• 7 tenets of data-driven engineering
• Key metrics to track
• FAQ

Data-driven engineering uses collected data to make more informed decisions about the software development cycle rather than relying on intuition or guesswork. This approach allows for a more accurate, measurable way of operating and eliminates potentially inaccurate predictions.

The data-driven engineering process contains vital stages that allow your team’s development to move toward a more objective-based approach. The process begins with data collection and prompt analysis, followed by data-driven decision-making that teams can tailor to their unique needs. Modern data engineering can involve moving the entire process to the cloud.

Data-driven engineering can improve SecDevOps, allowing for the streamlined development of security testing tools using data to measure security threats and vulnerabilities. Systems engineering also employs data-driven engineering to gauge system performance and proactively prevent system failures.

Data-driven engineering isn't just about collecting data; it's about leveraging insights to make informed decisions and optimize your software development process. As an engineering leader, here are five key ways data-driven engineering empowers you to lead your team more effectively:

Data-driven engineering goes beyond one-time optimizations; it fosters a culture of continuous improvement by providing real-time insights into team performance. You can use these insights to identify areas for improvement and experiment with new strategies to boost team velocity and developer satisfaction.

For example, by tracking developer experience metrics (link to our post), like lead time for changes, you can identify opportunities for streamlining processes and potentially increase your team's development velocity.

Data-driven engineering empowers you to analyze historical usage patterns and predict future needs with greater accuracy. After all, the best way to predict the future is with accurate data, not guesswork.

Data-driven engineering equips you with the tools to identify these bottlenecks by analyzing metrics like cycle time and code churn. Armed with this knowledge, you can work with your team to streamline workflows and improve overall efficiency as you continue to grow.

Leveraging real-time data, your developers can stay informed of critical events as they happen, allowing for immediate resolution and reduced data collection delays. By utilizing stream processing solutions such as Apache Flink or Kafka Streams and real-time data storage, your team can react to situations quickly and make decisions before disaster strikes.

Data-driven engineering empowers you to identify and mitigate security and compliance risks proactively. By analyzing data from various sources, you can detect potential vulnerabilities and ensure your development processes adhere to industry standards. It can also highlight practices that need to be improved or allow for effortless automated reporting and auditing for ongoing compliance.

Data-driven engineering provides insights into systems and processes, helping you scale performance, improve resource allocation, and avoid paying for what your team doesn't need. Using this data, you can make data-driven decisions about how to allocate your budget and resources in the most efficient ways.

To improve the implementation of data-driven engineering practices in organizations, Pluralsight's Developer Success Lab has released the 7 Tenets of Transformation, a white paper highlighting the key attributes that lead to a successful engineering transformation. We recommend reading the white paper for all the juicy details, but here are highlights of the seven tenets:

Invalidation and discouragement don't create success; an organization must empower its workforce to foster a successful transformation. Top-down approaches don't typically consider the realities of those who need to implement the changes; you must empower yourself and your team members.

In the early phases, focus on strengthening your data-driven leadership skills for your team’s benefit. Communicate the reasons behind the changes and align members around similar goals. And speak openly and frequently with all employees to address concerns as they arise.

Our research shows that over 80% of reorganizations fail to deliver expected benefits, and many harm companies. Worst of all, reorgs can be stressful for your team and demotivate employees—remember, you want to keep all team members empowered and motivated through a transformation.

When implementing a reorganization, keep individuals together for longer to create durable teams. These teams have time to experiment and learn from failures, becoming more efficient in the long run. Along the way, invest in team training to showcase how you value their contributions.

Not everything has to be a significant accomplishment; achieving small wins early in the transformation process is critical. Acknowledging small wins with your team can create a sense of momentum, keeping developers engaged.

Begin by identifying the smaller parts of more significant problems that developers need to solve. Using a data-driven approach backed by historical metrics, teams can identify changes that are more likely to result in minimal organizational disruption. Collaborate with your team on goals and, as your process advances, make necessary changes to test the efficiency of your approach.

While change can be exciting from a leadership perspective, people will inevitably experience fear or discomfort during big organizational shifts. If you notice resistance during the transformation process, consider other team members' perspectives. Many individuals who value stability may have anxieties about potential losses or disruptions.

Reach out to team members who are hesitant about changes and hear them out—you can likely find value in their thoughts and concerns. Focusing on early wins is a great way to help persuade skeptical teams, and seeking feedback can help you better understand resistance.

Focus on creating a culture of learning within your organization. The heart of transformation is learning to adapt and grow for the better. Invest in growing developers to create a culture where team members understand they are valued, and you are continually invested in their future. 

To implant the concepts, work with your team to better understand skill gaps. For the best chances of success, focus on aligning development efforts with the long-term vision of your organization's transformation.

Aligning organizational incentives better can help guide teams toward a successful transformation. Ensure current incentives focus on transformation goals rather than outdated ones that are now out of alignment. Develop new incentives if needed.

As with any change, clearly explain why incentives are switching and how they align with the transformation process. Communicating upfront that incentives are evolving with the organization's changing goals can give your team a better view of the process and their roles in it.

Analyzing how the data-driven process changes during an organizational transformation is critical. Metrics should provide better insight into your current organizational positioning and outline goals that point your teams in the right direction. 

By tracking progress and promoting transparency within metrics, you'll be able to identify areas for improvement and keep your workforce engaged. Utilize a platform like Pluralsight Flow to help set organizational goals and track vital metrics. Compiling a source of knowledge, such as a data book, can also be a helpful step in aligning teams. 

Creating goals and robustly collecting necessary metrics is critical—this will help determine how well your organization can react and adapt to change over time.

If you’re wondering what key metrics your team should track for data-driven engineering, don’t fret. We're here to recommend critical metrics you should examine at the start of your journey. 

Remember that the data-driven engineering process is a science, and you may need to adapt and select metrics more applicable to your organization as you advance. But first, understand these metrics and how they relate to your data-driven engineering transformation:

• Coding metrics are data points related to the coding process; they facilitate a better understanding of developers and contributed code. Metrics include coding days, rework time, commits per day, and impact.

• Submit metrics are data points related to how pull request (PR) submitters respond to and incorporate feedback from the code review process. Metrics include responsiveness, unreviewed PRs, iterated PRs, and iteration time.

• Review metrics are data points related to the code review process and help guide healthy developer teamwork. Metrics include reaction time, involvement, influence, and review coverage.

• Team collaboration metrics are data points designed to give leaders an overview of how teams interact during code review. Metrics include time to merge, number of follow-on commits, sharing index, and submitters.

• Developer experience metrics are data points that help organizations better understand the interaction between tools, processes, technology, and culture. Metrics include lead time for changes, cycle time, and velocity.

• DORA metrics are four data points DevOps teams use to evaluate performance. Metrics include deployment frequency, mean lead time for changes, mean time to recover, and change failure rate.

Becoming proficient in key metrics is a critical element in data-driven transformation. By learning how to become a data-driven engineering leader, you can better set and track goals to align and guide your teams.

Tackling data-driven engineering and pushing for transformation within your organization can be daunting. With Pluralsight Flow, you can gather data across various development tools, including code repositories and ticketing systems, for more informed decision-making.

For example, the team at Thomson Reuters needed to embark on a data-driven engineering transformation with DevOps at its core. With over 500,000 customers, Thomas Reuters leveraged Pluralsight to increase average coding days from 2.3 to 3 per developer and make more data-driven decisions.

Stop operating in the dark—use up-to-date data and generate metrics to focus on improvement. Illuminate your path with Pluralsight's 7 Tenets of Transformation, and learn the attributes we use to empower organizations of all sizes.

The data-driven engineering process may leave you with questions. Here are answers to frequently asked questions about data-driven engineering.

When collecting data for analysis, we recommend focusing on common coding metrics that provide a well-rounded overview of code development, pull requests, code review, and team collaboration. We also suggest obtaining a solid understanding of DORA and SPACE metrics.

Data-driven requirements engineering is similar to data-driven engineering in that it follows a data-centric approach to decision-making to improve outcomes. However, DDRE focuses on the requirements-gathering stage rather than the entire development lifecycle, collecting data that reflects a user's behavior and preferences.

Data-driven engineering's primary goal is to leverage data throughout the development lifecycle to identify areas of potential optimization and improve the overall process. ETL's primary goal is to prepare data for analysis, pulling it from sources and organizing it into a warehouse. In essence, ETL prepares data, while DDE uses said data to improve the development process.

No, data-driven engineering doesn't require object-oriented programming (OOP). Functional programming, such as R, and scripting languages, such as Python, can be used without issue. Engineers can apply the principles and processes of data-driven engineering across teams that are developing in any language.

No, machine learning knowledge is not required to implement data-driven engineering methodologies, but machine learning can help identify patterns and trends and automate tasks.