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You're balancing data quality and project timelines. How do you ensure both are met effectively?

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Balancing data quality with project timelines is a common challenge in the realm of data governance. Data governance involves overseeing the availability, usability, integrity, and security of the data employed in an organization. It's crucial to understand that both high-quality data and adherence to timelines are fundamental to the success of any project. Ensuring data quality means verifying that data is accurate, complete, and reliable. Meanwhile, project timelines refer to the schedule of deadlines and milestones that guide the completion of a project. Striking a balance between these two can be tricky, but with a strategic approach, you can meet both effectively.

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Dr. Anne-Marie Smith

Enterprise Data Management Expert | Data Governance | Metadata Management | Consultant | Doctoral Faculty Mentor |…
Peter M Henderson

Senior Data and Change Leader | Transforming Data, Analytics & AI Culture, Capability, and Controls.

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John Wernfeldt

I help Leaders get more Value from Data & AI?? | Top Data Management Voice | Managing Director p? Northridge Analytics…

1 Define Goals

Before diving into any project, clearly define what 'quality' means in the context of your data and understand the project's timeline constraints. Establishing clear goals for data quality and project deadlines sets the stage for success. This involves setting specific, measurable, achievable, relevant, and time-bound (SMART) objectives that align with the overall strategy of your organization. By having these targets in place, you can create a roadmap that balances the need for high-quality data with the urgency of meeting project milestones.

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Peter M Henderson

Senior Data and Change Leader | Transforming Data, Analytics & AI Culture, Capability, and Controls.
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Remember that data governance is not a project. It doesn’t stop when the project stops. The most important thing is to establish the culture, roles, responsibilities, processes, tools, and controls needed to perform data governance successfully on an ongoing basis. Beyond that, focus on data that most easily demonstrates the value of data governance so that the business buys in to the work needed to achieve good quality data.

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John Wernfeldt

I help Leaders get more Value from Data & AI?? | Top Data Management Voice | Managing Director p? Northridge Analytics | Data Governance Advocate | D&A Strategist | Data Architecture
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First, define data quality and what good data quality looks like. It differs between different data assets. Also understand what data is the most business critical and prioritise to improve the quality of those assets first.

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2 Plan Ahead

Effective planning is the backbone of achieving both data quality and timely project completion. Start by creating a detailed project plan that includes data governance tasks, such as data profiling, cleansing, and validation, alongside other project activities. Allocate sufficient time for these tasks within the project schedule. This ensures that data governance is not an afterthought but an integral part of the project lifecycle. Remember to also plan for contingencies, as unexpected data issues can arise and impact your timeline.

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Dr. Anne-Marie Smith

Enterprise Data Management Expert | Data Governance | Metadata Management | Consultant | Doctoral Faculty Mentor | Curriculum Development | Ph.D.
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Having a comprehensive project plan that includes sufficient (emphasis) time for data quality activities at the right points and allowing teams to iterate those activities as necessary is critical. Know what the current DQ levels are for the data involved in the project, and explore how to address those issues while planning the project - not halfway through the project when problems are evident. Taking time at the start - during the planning process - can help to avoid longer project stage cycles and deliver on time to satisfied users.

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3 Prioritize Tasks

Prioritization is key when resources are limited and deadlines loom. Evaluate the criticality of different datasets and focus on ensuring the quality of the most impactful ones first. This doesn't mean neglecting other datasets, but rather understanding the hierarchy of importance and allocating your efforts accordingly. By doing so, you can prevent bottlenecks and ensure that the most crucial data meets quality standards without derailing the project timeline.

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Jeremy Coone

Data Enthusiast | Enterprise AE
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Determine what assets are going to be prioritized using a simple framework like: High Priority: Focus on tasks that have a significant impact on achieving the project's goals and are time-sensitive. -Addressing critical data quality issues. -Implementing essential data quality policies. -Deploying key data quality tools. Medium Priority: Important but less urgent tasks, such as: Completing data enrichment processes. Low Priority: Tasks that are necessary but can be addressed later, such as: Enhancing tool functionalities. Conducting advanced training sessions.

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4 Monitor Progress

Continuous monitoring helps in identifying issues early and keeping both data quality and project timelines on track. Implement a system that provides real-time insights into the status of data governance activities and project milestones. Regular check-ins allow for adjustments to be made proactively, ensuring that neither data quality nor project deadlines are compromised. This dynamic approach to monitoring keeps all team members informed and responsive to changes.

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Dr. Anne-Marie Smith

Enterprise Data Management Expert | Data Governance | Metadata Management | Consultant | Doctoral Faculty Mentor | Curriculum Development | Ph.D.
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Know what measurements are important for this project's success before starting - and quantify those measures. Different projects in an organization consider different metrics to be important - know which ones apply in this initiative. Prepare to capture the measurement's results and report on them honestly and regularly and incorporate the measurements and reporting into the project's lifecycle.

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5 Collaborate Effectively

Collaboration among team members is vital for balancing data quality with project timelines. Encourage open communication between data professionals and project managers to ensure everyone understands the importance of both aspects. Use collaborative tools to share updates, document processes, and maintain transparency. This collective effort not only improves data governance but also fosters a sense of shared responsibility towards meeting project deadlines.

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6 Leverage Technology

Lastly, utilizing the right technology can significantly streamline the process of maintaining data quality while adhering to project timelines. Automated tools can assist in tasks like data cleansing and validation, reducing manual effort and the potential for human error. By integrating these technologies into your workflow, you can expedite data governance tasks without compromising on quality, helping keep your project on schedule.

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You're balancing data quality and project timelines. How do you ensure both are met effectively?

1

2

3

4

5

6

1 Define Goals

2 Plan Ahead

3 Prioritize Tasks

4 Monitor Progress

5 Collaborate Effectively

6 Leverage Technology

Data Governance

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You're balancing data quality and project timelines. How do you ensure both are met effectively?

1

2

3

4

5

6

1 Define Goals

2 Plan Ahead

3 Prioritize Tasks

4 Monitor Progress

5 Collaborate Effectively

6 Leverage Technology

Data Governance

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