You're struggling to align IT and business teams on data standards. How can you bridge the gap effectively?

Bridging this gap starts with clear communication and shared objectives. Convey mutual benefits of robust data standards, such as improved efficiency and better decision making. Use straightforward language and relatable examples to ensure everyone understands the importance. Hold regular cross functional meetings and provide collaborative tools for team alignment. By enabling a unified approach and highlighting common goals, you can create a cohesive strategy that benefits both IT and business teams.

To align IT and business teams on data standards: Understand both perspectives. Foster open communication and collaboration. Define clear objectives and develop standards together. Provide education and use Agile practices. Implement a Data Governance framework. Measure success, resolve conflicts promptly, and foster continuous improvement.

To effectively align IT and business teams on data standards, establish clear communication channels and a shared understanding of objectives, supported by a robust governance framework & executive support. Appointing data stewards, providing relevant training, and fostering a collaborative culture should encourage mutual cooperation. Its also important to obtain regular feedback to iterate and improve the process, ensuring data standards remain relevant and effective.

Aligning IT and business teams on data standards can be challenging, but finding common ground is key. Organizing joint workshops where both teams share their perspectives and focus on shared goals like data quality and compliance fosters mutual understanding and collaboration. Highlighting how data standards solve specific business problems makes the benefits tangible for business stakeholders, paving the way for a united approach.

The best way to achieve common ground and enable different teams (Data, IT, and Business) to work together in a seamless manner is to establish independent teams with shared goals. A viable approach is to create #DataDomains oriented teams composed of mixed profiles (IT+Data+Business) working towards common objectives. The goal of these teams should be to generate #DataProducts that allow the organization to maximize the #InformationValue, breaking down traditional departmental business structures.

Effective alignment between IT and business teams hinges on clear communication. Regular meetings, clear documentation, and an open-door policy for feedback are essential. Use accessible language, avoiding technical jargon to ensure everyone is on the same page. Encourage IT experts to explain data standards in terms of business outcomes, making the implications relevant and understandable.

It's not just about reorganizing teams into new organizational units (Data Domains). If communication flows are not properly established, implementation will fail. This is where agile methodologies come into play, facilitating and improving these communication flows. The challenge lies in applying these methodologies to large teams. Frameworks like Scrum of Scrums, which allow us to manage these communications so that, even though each domain team works independently, the different teams work in a coordinated manner following the common objectives of the data initiative, come into play to facilitate the initiative. #DataDomains #AgileMethodologies #ScrumOfScums #CommunicationFlows

Promote joint ownership of data standards by ensuring both IT and business teams are equally responsible for their development, implementation, and adherence. Establish a cross-functional team with representatives from both sides to oversee data governance policies. This shared responsibility makes data standards more practical and beneficial, leading to smoother implementation and greater compliance.

Models like #DataMesh favor these co-ownership figures. The idea is, in some way, to eliminate (or at least minimize) the limitations of the monolithic and centrally managed approaches that have traditionally been used in #DataPlatformArchitectures, in #DataManagement, and in data teams. #DataMesh proposes the adoption of a decentralized model based on a distributed architecture and the responsibility of the business areas (domains) over their data (decentralization of governance roles). Basically, what is proposed is to break down the data we manage into smaller and decentralized parts (#DataDomains).

Implement comprehensive training programs to educate stakeholders about data standards and governance. Tailor the programs to address the specific roles of IT and business team members, covering both technical aspects and business implications. An informed team is more likely to appreciate the value of data governance and collaborate effectively to uphold standards.