How can you resolve a conflict when departments have different data governance policies?

If each department has its own (and different) data governance policy, your first question should be if that makes sense. Is there a reason why each department needs a separate policy and you can't have a company-wide data governance policy? The problem may be that the policy goes too far into the details and is way more than just a policy. Putting that aside, it is important to recognize that different departments can have different ways in which they use the data. What one department needs is not what another department needs. What one department does to the data impacts other departments. The departments need to collaborate through data governance for the benefit of the company.

In my experience, different policies arise when departments have different objectives with data - it is understandable. It is not about hierarchies with data governance, not everything is defined and adopted centrally, neither the policies are set in stone nor are they followed uniformly. What matters is that these policies itself are not conflicting and deviating the organization away from its objectives. In my experience, creating data governance guiding principles is essential to allow data governance policies be aligned across departments. Having the data governance operating model fit to the operating model of the organization helps to ensure that the data governance efforts do not happen in silo.

Don’t shy away from conflict - if you do - u are are in the wrong team. Data will cause conflict - two groups will never see it insimilar manner we and will like to exercise control . Unless you are really to debate based on fact - this exercise is usually not very productive. Be careful not to not make this hierarchy driven and built on corporate politics .. if it is - it is a good indicator to step away..

Harmonizing data governance rules becomes particularly important based on the business's location. In regions like the Global South, there is a growing recognition of the General Data Protection Regulation (GDPR), with many countries aligning their laws accordingly. Despite this alignment, variations in implementation strategies, discordant elements like timelines, fines, and overseeing entities, and discrepancies in principle nomenclature may arise. Consequently, updating and reconciling knowledge of these rules is essential, bringing relevant departments in line with the requirements. The utilization of data governance sandboxes, as previously highlighted in my article, proves to be an effective means of achieving this harmonization.

Resolving conflicts arising from disparate data governance policies involves fostering collaboration and standardization. Initiate cross-departmental discussions to understand concerns and goals. Establish a unified framework by identifying common ground and key objectives. Develop a comprehensive data governance policy that integrates the strengths of individual policies. Ensure transparent communication and provide training to align teams with the new framework. Implement a phased transition, allowing for adaptation. Regularly review and update policies based on evolving needs, ensuring ongoing collaboration and a harmonized data governance approach across departments.

The goal is to foster a collaboration which is rooted in diversity of thought, perspective, and experience. This approach can help identify common and different needs across departments and build bridges of understanding as opposed to barriers that limit capacity and capability.

One should find the best solution which can benefit all , it should be a robust mechanism and should ensure such situations don't occur again . There can be ways where every stakeholder can agree on a term which can benefit them as well as the governance policy handlers and should try and find such aid for the benefit of all

A common ground might include trialling a policy for a period, then see how it goes and take from there. Depending on the policy, some sort of simple mechanism can be put in place, e.g. a structured process for escalating concerns. Knowing that there's a defined path for addressing issues can provide a sense of security for those who are apprehensive about the policy. Aother option might include benchmarking against Industry Standards.

Initiating the resolution process by identifying common ground is essential. For instance, a common scenario involves finding a balance between the control measures advocated by Risk Management and Customer-facing teams. Despite differing policies, both groups are aligned in their dedication to ensuring data accuracy, compliance with regulations, and the preservation of customer trust. Therefore, the most effective approach is to unite all stakeholders around these shared objectives and listen to their concerns, before progressing to find a collaborative solution.

Conflict negotiation between departments starts with establishing a common vision. A want for commonality is not going to generate action. Change will be driven by a need. Every department should be expected to have their own specific needs. Building an architectural framework towards a common vision will embed the flexibility to address the diversity of their requirements. This is key to the successful implementation of governance strategies. Trust that the ground will support these needs is critical in adoption of the common governance model. It’s in the post-adoption phase that the true benefits of this approach become evident, in a cultural adaptation to the common governance policies moving forward.

We try our best to share as many policies as we can but we know departments will have unique needs. These differences need to be governed and maintained through communication, transparency, and documentation. Every department should know the reasoning and logic behind their differences. Otherwise, inter-departmental meetings become battle grounds for "who's data is right" rather than making data-driven-decisions. Big or small, any company size will have different needs for their data and waiting for a quorum from a bunch of stakeholders isn't always feasible. Some will have PII with strict retention policies, others near real-time data, some batch load, some are risk adverse, others outcome focused. Document these differences and share.

Resolving conflicts arising from disparate data governance policies involves fostering collaboration and standardization. Initiate cross-departmental discussions to understand concerns and goals. Establish a unified framework by identifying common ground and key objectives. Develop a comprehensive data governance policy that integrates the strengths of individual policies. Ensure transparent communication and provide training to align teams with the new framework. Implement a phased transition, allowing for adaptation. Regularly review and update policies based on evolving needs, ensuring ongoing collaboration and a harmonized data governance approach across departments.

Monitor tech project resolutions by establishing key performance indicators (KPIs) aligned with project goals. Regularly assess progress through milestone tracking and feedback loops. Utilize project management tools for real-time visibility. Evaluate results by comparing achieved outcomes with initial objectives, considering factors like timeline adherence and budget compliance. Solicit stakeholder input to gauge user satisfaction and address potential issues promptly. Foster a culture of continuous improvement by learning from both successes and failures, adapting strategies accordingly. Adjust project parameters as needed, ensuring alignment with evolving technological landscapes and organizational priorities.

1. Data governance KPIs :Set specific KPIs to measure ex: data errors by % or achieving trust score KPI Tracking : Periodically track and report on progress 2. Evaluate success of projects by measuring project completion, adherence to timelines and achievement of project goals 3. Measure cost reductions achieved through the projects and KPIs measurement as above, increased data quality, lower compliance risks and decreased data management overhead. ROI : Calculate ROI of data governance by comparing the benefits(increased revenue, cost savings)to the cost of program. 4. Assess results of all internal and external compliance audits. 5. Train the data and model with evolving business priorities 6.Track Data governance roadmap progress

I understand that, when addressing data governance, the presence of federated data domains is crucial, meaning that each area maintains its autonomy and specific rules. In other words, it is understandable that each department has its own set of standards. However, if there is not a "higher" governance that establishes the main guidelines, I would venture to say that there is not a true governance in the company in question, but rather, only some "best practices" in each department. Data governance is not something established area by area, where each creates its own rules, but rather something defined at the company level, where there is a responsible and autonomous area for the care, maintenance, and definition of that information.

Imagine a corporate potluck where each department brings a unique dish, representing their data governance policies. Conflicts arise as flavors clash. Now, picture a "Data Fusion Feast." Departments collaborate to create a harmonious menu—blending policies into a cohesive strategy. Through open dialogue, compromise, and a dash of innovation, conflicts dissolve. The story illustrates that resolving conflicts in data governance is akin to crafting a delightful feast, where diverse policies contribute to a rich and flavorful strategy.

One policy. Makes no sense having 11 island republocs trying to work together in 1 organisation. Core business is core business and flourishes from a common standard upholded by all.