Your team is divided on the value of data governance. How can you bridge the gap for business success?

First listen to the concerns and understand it completely, involve all the key stake holders and get their buy in, collaborative approach will address the gap.Identify the tangible business benefits of DG by aligning the business objectives with DG and clearly articulate and communicate to all the stake holders.Start a small project and demonstrate the value/ benefits, clearly define the data ownership, data steward responsibilities and roles for all relevant stake holders. Continuous education and communication on the DG policies ,objectives and collaboration with all the stake holders is important.

A poorly communicated DG program can easily create the perception of being a bureaucratic burden. To avoid this, it is essential to establish clear and transparent communication channels that allow everyone involved to understand the program's objectives, benefits, and responsibilities. Actively involving all stakeholders in the design, implementation, and evolution of the data governance program is crucial to generate a sense of ownership and commitment. In this sense, the work of the Data Governance Committees is essential, which should be made up of representatives from different departments and hierarchical levels (both from technology teams and business areas). #DataGovernance #Communication #Transparency #StakeholderEngagement

As soon as data starts getting shared between systems, data governance is an absolute necessity. You can avoid the "bureaucratic obstacle" concern by (1) keeping the length of forms down to a single page, and (2) walking the main actors through their steps yourself, so they never hit a spot where they aren't sure what to do. Also, it's important to create activity diagrams for each action that's going to be governed: for instance, publishing a new shared entity, requesting access to a shared entity, etc. That way all stakeholders know what is expected of them (and hopefully will see that it isn't much beyond what they already do) and everyone has a unified view of what the DG processes are.

When your team questions the value of data governance, you should regard it as a warning sign. You have to ask - Is this effort aligned with business priorities? Are you aligned with the overall data strategy? Is the effort tied to an important project? Are you delivering value for the business? Is the business able to leverage your results? Are stakeholders taking action based on governance decisions and products? You may need to conduct a data governance alignment check. Then check to ensure your deliverables are useful to and understood by stakeholders. Have you been too involved in DG tools? Do you need to evangelize and train your team and stakeholders more? The answers should resolve the split.

In this type of situation, I believe it is crucial to identify and establish rapport with the appropriate stakeholders within the team to comprehend the overarching objective and the role of data governance. Additionally, securing the support of an executive sponsor for the data governance proposal is essential. This endorsement typically encourages alignment and compliance among other team members.

Aligning on common goals is indeed the most critical part when the program involves multiple teams and it eradicates the common "what do I get" mentality. It also establishes the connectivity between teams so that they can know how the other team does it.

I have used a stakeholder workshop where each dept demonstrated their top issues and problems. Identify the objectives associated and show how multiple goals can be defined with a shared outcome.

Aligning on common goals is indeed crucial to overcoming skepticism about data governance. Here’s a detailed approach to engaging your team and demonstrating the strategic value of data governance Identify Shared Objectives Demonstrate the Role of Data Governance Showcase Strategic Support Engage in Collaborative Efforts Communicate Benefits and Success Stories Address Concerns Proactively

Training and communication are important parts of success in gaining Allie’s. If you can’t define it you can’t explain it and why it’s needed.

Educating them on how strategies related to data governance can enhance the decision-making process for business teams. Assisting them in comprehending the policies and standards associated with data governance. Show them real-life examples or case studies where data governance has led to business success. This could include instances of improved data security, compliance with regulations, or increased customer satisfaction due to better data management.

Created Example: Data Governance in a Healthcare Organization Organization: HealthPlus Clinics Challenge: HealthPlus Clinics experienced difficulties with patient data management, leading to discrepancies in patient records, billing errors, and challenges in complying with healthcare regulations. Data Governance Initiative: HealthPlus Clinics implemented a data governance framework that included: Standardizing patient data entry procedures across all clinics. Introducing a centralized electronic health record (EHR) system. Training staff on data governance principles and data protection practices. Regularly reviewing and updating data governance policies.

Implementing data governance can seem daunting, but breaking it down into manageable tasks can make the process more tangible and approachable for your team. Here’s a step-by-step guide to implementing data governance, highlighting how each step contributes to the overall strategy and demonstrates its value Define Data Governance Goals and Objective Establish a Data Governance Framework Develop Data Policies and Standards Implement Data Management Tools and Technologies Train and Educate Staff Establish Data Governance Processes Monitor and Review Data Governance Practices

You need to start with the development of the DG frameworks. Identify what they are and what value they bring. Then prioritize their value and create a strategy and Gantt chart for implementation.

Creating a 3-step (or 30, 60, 90-day) plan can make data governance more tangible and manageable. In the first step, identify key stakeholders like business leaders and information security & compliance personnel, and understand the main data sources. It’s impractical to identify all data sources early on; this is ongoing. In the second step, select a specific data domain to document how data is transferred from source to target and enhance data quality. In the final step, establish a data governance board with potential data owners and stakeholders, showcasing initial improvements. This phase also includes planning for future data set enhancements and data governance projects.

A collaborative approach to developing and implementing a data governance strategy involves the team in every step, from planning to execution. By encouraging contributions, addressing concerns, and fostering a sense of ownership, you can bridge divides and ensure that data governance initiatives are supported and successful. This inclusivity not only improves the quality of the strategy but also enhances team engagement, commitment, and accountability

When reporting the value of data governance and trying to achieve buy-in from your team, consider tracking metrics other than business performance, conversions, or outputs. Data governance not only improves the quality of your data but also, in doing so, makes the work easier for team members who don't have to chase down missing data, make sense of ambiguous entries, or write off datasets as unusable. Reporting metrics about analyst satisfaction, task/ticket completion times, or ease of collaboration can help foster buy-in from your team.

To illustrate the value of data governance, consider some additional aspects... Business: Emphasize how effective data governance improves decision making, reduces risk and aligns with business objectives. Case studies can show how data governance has led to tangible business improvements. Stakeholders: Involve stakeholders from different departments to provide their perspective on data governance. This helps to understand different viewpoints and fosters a sense of ownership and collaboration. Regulations: Point out the importance of complying with new regulations such as the EU AI Act. These regulations impact generative AI projects and emphasize the need for sound data and AI governance to build trust with customers and partners.