Hier erfahren Sie, wie Sie sich innerhalb eines Unternehmens nachdrücklich für Best Practices für die Datenarchitektur einsetzen können.

Action: Provide clear distinctions between Data Architecture and Technology Architecture. Emphasize that technology is an enabler of data. Tools: Use frameworks like TOGAF, ISOs, DCAM, and DAMA to illustrate structured approaches to data management. Example: "Conduct workshops explaining the principles of TOGAF and how it supports a robust Data Architecture."

In the industry, most of the times, Stakeholders are focused on outcomes and not the internal workings of an application. However, without a robust and industry standard data architecture foundation, it is the stakeholders that will be at loss. As a data architect, one must foresee the maturity of the application and translate these concerns into "Use case based discussion points". This is because the language of use cases is more palatable to stakeholders and it's incumbent on the architect to communicate these Data-integrity and data-quality risk assessments with foresight on user workflows that can evolve from smaller to potentially large scale inconsistencies which will impact the Business to Cost ratio if not handled preemptively.

Highlight quick wins and immediate benefits of adopting best practices, such as improved data quality, faster decision-making, and cost savings.

Action: Highlight the tangible benefits of implementing strong data architecture practices, such as improved data quality, better decision-making, and regulatory compliance. Example: "Demonstrate how data governance frameworks like DAMA improve data accuracy and business outcomes."

5 benefits of a robust data architecture that can be highlighted are 1. Data consistency 2. Data quality 3. Golden records 4. Data cleansing and lack of it and non-necessity of it. 5. Business to Cost Ratio (Of having it) First 4 points are really intuitive to a data architect and can be established with proper scenario based presentations to stakeholders The 5th point is intuitive to the Business and Stakeholders and if substantiated by the data architect with numbers associated to cost potentially incurred in future, due to application evolution and trends that will only show up in the future, the stakeholders most of the times synergize with the architecture team to implement a robust well architected solution.

Action: Acknowledge common challenges such as data silos, integration issues, and resistance to change. Offer solutions based on industry standards. Example: "Explain how DCAM can help streamline data integration processes and reduce silos."

Development squads and the business need to understand the challenges such as a well architected data migration and architecture plan is a comprehensive endeavor that takes resources and time. The complete picture although elusive at a few times to teams is something that can be broken down into segments. The wholistic picture of a well functioning benchmark tested application being the goal presented by the architect, the first few steps of extreme detail should be laid out and the course of action is the secondary important aspect. 1. Wholistic picture 2. Immediate goals 3. Next immediate steps that runs the team towards that goal.

A case study is a great leverage to gaining acceptance for a proposed implementation. Research various teams in the firm or outside and establish a use case and showcase the implementation with important KPIs that have been reached by these teams and their milestones. Doing the above would give a clear picture to the development squads and the business teams a sense of trust and a path that's well tread to envision the future state of a similar implementation to the case study. One must keep in mind that there should always be a degree of freedom to pivot and to take calculated risks to have a healthy design and development team that is exuberant and curious to utilize their skillset to implement a well designed data architecture plan.

Action: Share case studies and success stories from other organizations that have successfully implemented data architecture best practices. Example: "Present a case study on how implementing ISO standards helped a company achieve data compliance and efficiency."

Action: Develop a clear, actionable plan for implementing data architecture best practices within your organization. Include milestones, timelines, and responsible parties. Example: "Draft a roadmap outlining the adoption of TOGAF principles over the next six months."

Action: Encourage collaboration between data architects, technology architects, and other stakeholders. Ensure everyone understands their role in supporting data architecture. Example: "Set up regular meetings between data and technology teams to align on goals and progress."

Bottom line: An effective data architecture should empower decision-making, drive innovation, and support overall business growth.

Action: Stay updated with the latest trends and best practices in data architecture. Attend relevant conferences, participate in webinars, and engage with professional communities. Example: "Join industry groups like DAMA International to stay informed about emerging data management trends and practices."

Start by educating stakeholders about the benefits of effective data architecture, such as improved data quality, enhanced decision-making, and reduced operational costs. Use data-driven insights and case studies to illustrate the impact of adopting best practices on organisational efficiency and competitive advantage. Engage in proactive discussions with cross-functional teams, highlighting how implementing standardised data models, robust security measures, and scalable infrastructure can mitigate risks and support long-term growth. Foster collaboration by seeking input from colleagues and addressing concerns constructively, emphasising the importance of aligning data strategies with business goals.