My Data Odyssey Through Agile Waters - Part 2

Greetings,

Welcome to the 12th edition of A Data Mind, where I,?Sune Selsb?k-Reitz, delve into the fascinating world of data across various aspects of life.

And welcome back to the second part of My Data Odyssey Through Agile Waters. In this series, I will explore the dynamic world of data within the context of the Scaled Agile Framework (SAFe). In the previous edition, we embarked on a journey through the challenges and complexities of integrating data initiatives into SAFe. Today, we delve deeper into this topic, exploring potential solutions that can help us navigate the agile waters of data.

Embracing Flexibility and Adaptability

As mentioned earlier, data initiatives often demand a higher degree of flexibility due to their ever-changing nature. While SAFe provides a structured approach, it may not always align perfectly with the rapid pace of data work. In this part, let's discuss how to embrace flexibility and adaptability effectively.

Agile Framework Tailoring

One way to make SAFe more accommodating to data initiatives is to tailor the framework to better meet the unique needs of data teams. This means modifying SAFe's processes to allow for shorter planning cycles and more frequent adjustments. For instance, consider adjusting the Program Increment (PI) planning process to allow for smaller, more frequent data releases instead of a strict quarterly schedule.

I suggest keeping the same Program Increment length but reducing the amount of PI-planned work to 25-35% to support large data-related projects or programs, and devoting 65-75% to smaller, insight-driven work to facilitate quick insights and exploratory efforts. We need to take into account both the big picture (25-35%) and the smaller (65-75%) but equally important insight work, as we need to get both segments of work prioritized by the business (by the SAFe Business Owners). This can be achieved every two to four weeks - depending on the length of iterations - with a more comprehensive planning session held each quarter, as we are familiar with from traditional SAFe.

Lean Portfolio Management for Data

Another important aspect of tailoring SAFe for data-centric projects involves applying Lean Portfolio Management (LPM) principles. LPM enables continuous prioritization and rapid decision-making, which is suited to the fast-paced nature of data initiatives. Through LPM, most organizations can guarantee that their data-centric efforts are continually evaluated, allowing resources to be reallocated to projects or programs that promise the greatest (data) value. This proactive approach mitigates the risk of investing in initiatives that are no longer aligned with evolving business objectives, thereby avoiding unnecessary expenses by giving a go or no-go at the corresponding stages of the SAFe Portfolio Kanban.

Cross-Functional Collaboration

Collaboration is the cornerstone of any successful agile effort, and working with data is no exception. To effectively harness the power of data within SAFe, it's essential to promote collaboration among different teams and stakeholders. Regular communication channels and feedback loops must be established to ensure that data initiatives remain closely aligned with ever-evolving business objectives. Encouraging cross-functional teams to collaborate, demolish silos, and share knowledge can result in a more agile and responsive strategy for data-driven decision-making.

Continuous Improvement and Adaptation

Flexibility and adaptability should be integrated into the culture of data teams working within SAFe, in order to promote a mindset of continuous improvement and adaptation. In my mind, the teams should regularly review and reflect on their processes, looking for ways to optimize their planning cycles, improve data quality, and streamline workflows. The Inspect and Adapt and Retrospective events are highly valued practices within the agile community, which also applies to data teams without exception. This method enables individuals and teams to identify bottlenecks and inefficiencies and take immediate corrective action.

You Need to Measure Your Success - and Final Words

Finally, it's essential to establish specific metrics to measure the success of your tailored SAFe framework for data initiatives. We must define key performance indicators (KPIs) to evaluate the effectiveness of shorter planning cycles, flexible data releases, and improved collaboration. In addition, we need to consistently evaluate these metrics to gain insight into the impact of our agile data approach and drive further improvements.

By embracing flexibility and adaptability within SAFe, we are not simply altering a framework, but rather cultivating a mindset that flourishes through change and innovation. The capability to respond quickly to emerging data opportunities and challenges is a defining characteristic of modern organizations. By tailoring SAFe, implementing Lean Portfolio Management, promoting cross-functional collaboration, fostering a culture of continuous improvement, and evaluating success using relevant KPIs, we are equipping ourselves to navigate the ever-changing landscape of data-driven decision making with confidence and agility.

So, thank you for embarking on this journey through data with me. Please stay tuned for more insights and adventures in the world of data. Until then, keep exploring the data universe and remember that the journey is as valuable as the destination.