Perfect Tempo: How to Deliver Business Value Through Agile Architecture Principles?

Data teams face a persistent challenge in keeping up with the rapid pace of innovation and evolving business needs. In the race to maintain data quality, reliability, and architectural robustness, they can fall behind in implementing cutting-edge technologies and methodologies. And this can hinder their ability to quickly deliver value to the business.

The constant pressure to balance the expectations of business teams with the demands of managing complex data ecosystems can widen the gap between what the business needs and what the data team can deliver. Forward-thinking teams prioritize agile architecture principles and invest in continuous improvement initiatives that enable them to adapt and respond to the ever-changing business landscape.

However, getting that investment can feel like an impossible task. One approach is to tie all capabilities to the prioritized business outcomes and then realize incremental value for both the business and the data teams with quick, agile iterations.

A Proven Approach

A large healthcare organization that wanted to build a modern technical stack secured business buy-in with a similar approach. In the past, the company relied on third-party providers to deliver customized software. Although solutions were tailored to the company’s specific use cases, this also meant vendors could offer the same solutions to the competition.

To address this, the organization prioritized the application’s business value by breaking it down into specific use cases and categorizing them based on the most important analytical capabilities—in short, developing a plan to create a homegrown solution.

Each phase of the program was tied to a specific business value, while the technology architecture supporting it was enhanced with every phase. Linking each step to specific outcomes ensured that the value delivered was proportionate to the overall investment. This strategy enabled the data teams to execute a three-year roadmap. Today, the architecture is evolving in tandem with the business, with the team carefully considering and managing risks along the way.

Befriending the MVP

One standard method leveraged to achieve a similar outcome is creating a minimal viable product (MVP) for any business use case. The focus is on limiting the scope of the final deliverable while starting to lay foundational architectural capabilities along the data pipeline.

For example, a data team working on a customer segmentation project can work with the business to identify the most important customers and then build a model for those segments first. Then, the team can deliver the model to the company and gather feedback, making changes before moving on to the next set of segments. This allows the business to see value much sooner while also ensuring that the segmentation model reflects the company’s needs before adding more data sources or model complexities.

Additional agility can be created by layering in the ability to work through the architecture components. For example, consider building basic data-cleansing logic during the first phase and adding more complexity later. Extend this approach to each deliverable iteration, ensuring that you are delivering incremental business value while also building a robust solution.

Putting It into Practice

Although our healthcare example sounds whimsical, it is a challenging feat. Striking a balance between delivering value and achieving technical excellence can be achieved only when business and technical counterparts both understand each other’s goals and priorities. Achieving such a state includes the following moves:

• Establish a cross-functional team. Assemble a diverse team that includes members from key business domains, data teams, and technology departments. This team will be responsible for identifying key business objectives, defining the project’s scope, and ensuring that the technical solutions align with these goals.

• Define shared goals and priorities. Collaboratively determine the most pressing business needs and the desired outcomes for the project. Helping the team prioritize which components to develop first ensures that the solution’s most impactful aspects are delivered quickly.

• Identify key milestones and value delivered. Align each phase of the project with the specific, measurable value that is being delivered. Break the project down into small, manageable phases. For each phase, outline the deliverables and the milestones to demonstrate incremental progress and business value.

• Embrace an asset-driven approach. Promote a culture that stimulates the creation and governance of reusable data assets across the organization, to increase efficiencies and accelerate time to value. Define and adopt common standards for every project.

• Develop an iterative architectural plan. Create an adaptable plan for the project’s technical components, allowing the team to adjust based on any new information or changing business needs. This plan should include strategies for incorporating architectural improvements and additional features throughout the project’s life cycle and referenced within the previously defined phases.

• Foster open communication and collaboration. Encourage regular communication among team members to share updates, address challenges, and celebrate successes. This will help maintain momentum and ensure that all stakeholders stay engaged and invested in the project’s success. Partnering with business stakeholders is critical to managing and communicating value on an ongoing basis.

• Monitor progress and adjust as needed. Continuously evaluate the project’s progress against its goals and milestones, adjusting as necessary to stay on track. Along the way, provide room to refine the technical approach, reprioritize deliverables, or reallocate resources to meet the needs of the business.

Business and technical data teams need to collaborate now more than ever. Building bridges to cover this gap requires a new approach focused on creating incremental value through agile architecture improvements. This involves breaking down complex projects into smaller, more manageable tasks that can be completed in short sprints, with regular feedback and collaboration between business and technical teams.

With this approach, businesses can make incremental improvements in their data architecture, leading to more effective data analysis, better decision-making, and ultimately, improved business outcomes. This also helps foster a culture of collaboration and innovation, with business and technical teams working together toward a shared vision.

About the Author

Sandy Estrada is a data and analytics advisor focused on helping executives set strategies, align operations, and mobilize technology to solve their most pressing data challenges. With a 20-year track record of leading data and digital transformations, she understands the value of creating frameworks that focus on material business outcomes.

Sandy is a Vice President as well as an Analytics and Information Management Consulting Practice Lead with Cervello, a Kearney company, where she is focused on nurturing and growing their global clients, teams, and partnerships.

She also serves as a mentor for various organizations, helping women and those in underrepresented communities grow their careers in data. She’s passionate about empowering the next generation of data professionals and ensuring that the field becomes more diverse and inclusive.