What do you do if your data infrastructure and systems are not aligned with your data architects?

In my view, some strategies can be used to achieve alignment, such as: Start by initiating open and honest communication with your data architects, Work together to identify specific areas where data infrastructure and systems diverge from the architecture planned, Ensure both parties have a clear understanding of the organization's data requirements, including data sources, data formats, data governance policies, and compliance standards, Obtain buy-in from relevant stakeholders, including data engineers, IT teams, business users, and senior management and Continuously monitor the alignment between data infrastructure and data architecture and make adjustments as necessary.

Firstly, it is important to have open discussion with the Infra team so that every party gets equally heard. Then, based on the discussions work together to identify the root causes of misalignment and explore potential solutions that bridge the gap between infrastructure and architectural goals. This may involve reassessing current data infrastructure components, revisiting architectural designs, or implementing new technologies and methodologies to better align with the overarching data architecture strategy. Sometimes, cost management can also be a blocker, which needs to be addressed collectively.

If infrastructure and systems are not aligned with data architects, it's important to understand the need by ... Work together: Work closely with your data architects and other relevant stakeholders (DevOps, etc.) to understand their vision and goals for the data infrastructure. Review: Assess current systems to identify gaps and areas for improvement, e.g. scalability, performance, governance, data quality and integration capabilities. Desired State: Define the ideal state of the data infrastructure (+ AI) in alignment with the goals of the data architects and other stakeholders (data scientists, etc.). Roadmap: Create a roadmap outlining the steps to realign the data infrastructure with the vision outlined by the data architects.

Begin by conducting a comprehensive needs assessment to understand the gap between the current data infrastructure and the architects' vision. This involves evaluating the existing systems, data flows, storage solutions, and processing capabilities against the future requirements outlined by your data architects. Consider scalability, performance, security, and compliance needs. This step provides a clear foundation for identifying specific areas of misalignment and prioritizing adjustments to bridge these gaps effectively.

When your data infrastructure and systems are not aligned with your data architects, it can lead to inefficiencies, miscommunications, and challenges in achieving data-related goals. Initiate open and transparent communication channels between data architects, data engineers, data scientists, and other stakeholders. Foster a collaborative environment where teams can discuss requirements, priorities, and challenges related to data infrastructure. Ensure that data architects and data infrastructure teams have a shared understanding of organizational goals, data strategy, and desired outcomes.

To encourage open dialog between all relevant teams, consider ... Communication: Encourage regular communication between data architects, relevant stakeholders and infrastructure teams to address discrepancies and understand constraints. Goals: Ensure that the goals of data architects, relevant stakeholders and infrastructure maintenance teams are aligned. Collaboration: Facilitate joint problem-solving sessions to develop and implement strategies for realignment. Improvement: Establish a culture of continuous improvement where feedback is valued and adjustments are made iteratively to improve alignment.

Facilitate an open dialogue between data architects, engineers, and key stakeholders to discuss findings from the needs assessment. This conversation should focus on understanding the rationale behind the architects' vision, the limitations of the current infrastructure, and the impact on ongoing and future projects. Encouraging cross-functional collaboration at this stage ensures that all parties have a shared understanding of the objectives and constraints, fostering a cohesive approach to planning and implementation. Adding multi-disciplinary guilds that can meet on a fixed cadency to discuss the innovation bricks, share issues or ask for consultation enhances this open dialogue too.

Open dialogue helps align data infrastructure and systems with data architecture by fostering collaboration and understanding among stakeholders. It allows teams responsible for infrastructure, systems, and architecture to openly discuss challenges, share insights, and propose solutions. Through open dialogue, different perspectives can be considered, leading to more informed decisions and better alignment between infrastructure and architecture. Additionally, open dialog promotes transparency and encourages feedback, enabling teams to address issues promptly and adjust plans as needed to ensure alignment with the overall goals and objectives.

Maintaining an open dialog between the data teams is essential to manage each others dependencies and timelines when onboarding new changes to the platform. As a Databricks Solution Architect I have to take action to ensure that the DevOps Engineering team is on page and aligned with the Data Engineers, and vice versa when Databricks needs to be updated.

Developing and integrating a plan helps align data infrastructure and systems with data architecture by assessing current setups, setting clear goals, designing a roadmap, allocating resources, establishing timelines, fostering communication, conducting testing, providing training, and ensuring continuous monitoring for optimization. This ensures better coordination and efficiency in managing data systems.

Integration plans help identify and mitigate risks associated with the integration process. By proactively addressing potential issues such as data loss, security vulnerabilities, or compliance violations, the plan reduces the likelihood of costly setbacks or failures.

Develop a detailed integration plan that outlines the steps needed to align the data infrastructure with the architects' vision. This plan should include timelines, resource allocations, risk assessments, and fallback strategies. It's essential to balance technical improvements with business continuity, ensuring that the integration process minimally disrupts ongoing operations. Prioritize actions based on their impact on achieving alignment and their feasibility within the given constraints.

To test changes, consider ..... Controlled testing: Perform in-depth testingof all planned changes in a controlled environment to uncover potential problems. Detect problems early: Use the testing phase to identify any issues between the new design and existing systems. Business impact: During the testing phase, assess the potential impact of the proposed changes on the business. Stakeholder consultation: Ensure that you coordinate with stakeholders throughout the testing process to gather feedback and effectively address concerns and business impact.

Implement controls in accordance to the vision that could be tested both scripted and unscripted as part of a release in a testing environment. Implement these controls and test as part of your workflow in ADO making sure they are both visible and documented throughout the development / project lifecycle.

Before fully implementing the new changes, conduct thorough testing to ensure they meet the required specifications without adversely affecting existing systems. This could involve setting up a parallel environment to simulate the integration of new technologies or architectural changes. Testing helps identify potential issues early, allowing for adjustments before full-scale deployment, thereby minimizing risks and ensuring a smoother transition.

With a well-tested plan in place, proceed to execute the strategy to align the data infrastructure with the architects' vision. This phase should be managed carefully, with continuous communication among all parties involved to ensure clarity and coordination. Implement changes in phased rollouts when possible to reduce impact on existing systems and allow for monitoring and quick adjustments as needed.

Implement quality assurance processes to ensure that changes are rolled out accurately and effectively. Test any new configurations, systems, or processes thoroughly before deployment to minimize the risk of errors or disruptions.

To monitor the results, it is important to ... Set KPIs: Define key performance indicators (KPIs) to measure the efficiency, reliability and scalability of the modified data infrastructure. Reporting: Implement regular reporting mechanisms to track system performance and identify deviations from the desired state early. Business Impact: Assess the business impact of any performance deviations and take the necessary actions to address them promptly. Stakeholders: Keep stakeholders informed about system performance and all necessary adjustments to ensure alignment with stakeholders' and business objectives.

After executing the strategy, continuously monitor the results to ensure that the changes have effectively aligned the data infrastructure with the architects' vision. Use predefined metrics to evaluate performance, scalability, and other critical factors. Monitoring allows for the early detection of any unforeseen issues and provides valuable feedback for further refinement and optimization of the data systems.

Other aspects to consider ... Root causes: Get to the root cause to understand why the mismatch occurred, whether it was due to miscommunication, insufficient resources or changing requirements. Collaboration: Encourage collaboration between the infrastructure team and data architects to effectively close the gaps and ensure a shared understanding and commitment to the desired outcomes. Agility: Adopt agile practices to facilitate flexibility and adaptability in data infrastructure alignment and enable iterative improvements and adjustments based on evolving requirements. Training: Provide training and professional development for all team members involved in managing and maintaining the data infrastructure.

In addition to the steps outlined, consider the long-term maintenance and adaptability of the aligned infrastructure. The data landscape is continuously evolving, so it's crucial to implement solutions that are not only aligned today but also flexible enough to adapt to future needs and technologies. Regularly revisit the alignment strategy to incorporate new insights, technologies, and business objectives, ensuring that your data infrastructure remains robust, agile, and forward-looking.

Las infraestructura y sistemas de datos estan basados en una idea logica primero para abordar requerimientos funcionales y no funcionales de arquitectos previos Esto lleva a 2 puntos: 1) Debe evolucionar la infraestructura y sistemas para mantener el ritmo. 2) Estan mal abordatos los desarrollo en la infraestructura y sistema de datos. En ambos casos los requerimientos funcionales y no funcionales deben guiar el alineamiento para que los arquitectos de datos puedan dar un resultado esperado con lo que se tiene actualmente implementado.

Bring architects and the infrastructure team together for open discussion to clarify gaps. - Conduct a thorough assessment - Revisit objectives and priorities - Develop a unified strategy - Implement incremental changes - Foster continuous collaboration - Invest in training - Monitor progress - Cultivate flexibility and innovation - Document and communicate changes - Realise a cohesive data environment Follow these steps to align infrastructure with the architects' vision effectively.