1.4 Data-Driven Development

Every year it becomes more important for companies to engage in data-driven decision making. The use of a defined suite of key metrics and measurable outcomes is an indispensable way for teams to evaluate the effectiveness of their high-level strategies and assess the extent to which their business model remains aligned with their long-term goals.

1.4.1 Data-Driven Decision Making

Level: Basic

Your team’s decision-making process is data-driven and effective. The team can also communicate data insights clearly to senior leadership, and that leadership makes effective use of those insights.

Effective use of analytics should be a part of every major decision-making process. This means that the team should be strong at using data internally, and should also be able to communicate its data insights to other teams and leaders as-needed.

1.4.2 Goal Cadence

Level: Basic

The team creates and revises measurable goals—such as product performance and team productivity—at a regular cadence (e.g., quarterly). All team members are involved in goal-setting.

A strong goal-setting cadence will include both short and long-term goals. Progress towards goals should be measured through the use of relevant KPIs; however, KPI are not goals themselves. A KPI is only valuable to the extent that it helps to illustrate the team’s progress towards achieving a desired outcome (goal).

1.4.3 Team-Level Metrics

Level: Basic

Team performance is measured by KPIs tied to customer or business value. These enable the team to make decisions with more autonomy than it would otherwise be able to.

Clear outcome metrics provide teams with a sense of purpose and act as “guardrails” that enable teams to act autonomously without the risk of deviation from your company’s core goals. Metrics should be chosen based on what outcomes will maximize customer and business value.

1.4.4 Intra-Company Alignment

Level: Intermediate

Your team develops its key performance targets in-coordination with other teams (technical and non-technical) in order to maintain strategic alignment with your organization’s high-level goals.

It is common for the development process to result in products that do not fully represent the business case for the product, even when developers have been briefed on what the business case is. Effective use of shared targets makes it easier for teams to monitor their alignment more closely, so that requirements drift can be recognized and corrected early.

1.4.5 Product Data Monitoring

Level: Intermediate

Your team’s use of product/service data is guided by a monitoring strategy that covers both functional and non-functional metrics. This strategy has allowed your team to create significant value from its data.

Teams that continually monitor product data and make use of data insights are better at discovering new opportunities to increase product value. Strong use of product data, such as functional and performance data, testing data, and customer use metrics, is key to your team’s ability to maximize your product’s ability to meet its core business objectives.

1.4.6 Workflow Activity Review

Level: Advanced

Your team uses activity data to identify workflow issues and other topics that would be useful to address during daily stand-ups, retrospectives, and other inspect-and-adapt activities.

Teams can use workflow data (e.g., commits, ticket activity) to better understand and optimize both team-level and individual work patterns. Workflow data makes it possible to assess the effectiveness of team collaboration, identify potential performance issues (e.g., excessive code churn), and to discover and publicize high-performance work patterns.