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Last updated on 2024年9月29日

You're facing conflicting data quality standards in a merger. How can you ensure seamless integration?

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In the midst of a merger, you're hit with a critical task: aligning disparate data quality standards. This challenge is not just about technical integration but ensuring that the data you rely on is consistent, accurate, and actionable. Data governance plays a pivotal role in this process, providing the framework and policies needed to harmonize standards and maintain data integrity. As you navigate through this complex terrain, understanding and addressing the differences in data quality standards will be key to a successful merger and the future reliability of your data assets.

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Sneha Chennamaraja

Data Solutions Architect | Cloud & Data Enthusiast | Virtuoso in Data Architect, Data Engineering and Data Analytics |…
Vincenzo Audino

Senior Manager at KPMG Italy
Abhishek Ray

Enterprise Data Strategy Practice @ Capgemini Invent

1 Assess Needs

Begin by thoroughly assessing the data quality needs of both companies involved in the merger. This means understanding the unique business processes, regulatory requirements, and customer expectations each entity brings to the table. By identifying the most critical data elements and evaluating existing data governance policies, you can pinpoint where conflicts may arise. This initial step lays the groundwork for creating a unified data quality framework that serves the newly merged entity without compromising on the specific needs that each company has developed over time.

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Sneha Chennamaraja

Data Solutions Architect | Cloud & Data Enthusiast | Virtuoso in Data Architect, Data Engineering and Data Analytics | I help in delivering scalable, data-driven solutions.
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To tackle conflicting data quality standards during a merger, start by assessing the needs of both the sources. Look at their data structures, governance rules, and quality standards. Focus on key data points, such as customer records, financial transactions, and product inventories, that need to be aligned. For example, if a company uses different formats for customer IDs, assess the need to standardize those fields to avoid duplication. Use tools like Collibra to audit and monitor data quality. By understanding the needs for consistency and accuracy, you can develop a tailored data governance framework that ensures compliance and smooth integration.

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Vincenzo Audino

Senior Manager at KPMG Italy
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Integration progressively of the framework by identifying the most critical data elements and evaluating existing data governance policies

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2 Define Metrics

Once you've assessed the needs, establish clear data quality metrics that are agreed upon by all stakeholders. These metrics should include aspects like accuracy, completeness, consistency, and timeliness. Having a shared understanding of what constitutes 'high-quality data' is essential for seamless integration. This step involves not just agreeing on definitions but also on the methods for measuring and reporting on these metrics. This ensures that when you talk about data quality post-merger, everyone is on the same page.

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Vincenzo Audino

Senior Manager at KPMG Italy
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To ensure consistency in merged datasets, follow these steps: Standardize Data Formats: Align formats for dates, numbers, and text fields. Data Mapping: Create a document to align field names and data types. Data Cleansing: Remove duplicates, correct errors, and fill missing values. Unique Identifiers: Use unique IDs to avoid duplication and match data accurately. Validation Rules: Set rules to check for inconsistencies during merging. Automated Tools: Use tools for data cleansing, transformation, and validation. Regular Monitoring: Continuously monitor and audit data for consistency.

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3 Integrate Systems

The technical side of integration is where you'll combine the actual data systems. Prioritize interoperability and the ability to handle data from both companies efficiently. This may involve adopting new software or customizing existing systems to ensure they can communicate effectively. It's crucial to maintain data quality during this process, so regular checks and balances should be put in place. This might include data profiling or using data quality tools to monitor and resolve any issues that arise as systems are integrated.

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Abhishek Ray

Enterprise Data Strategy Practice @ Capgemini Invent
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? ?????????????????????? ???????????? ????????: Develop an MDM strategy to create a single, authoritative source of truth for key data entities such as customers, products, and vendors. ? ?????????????????? ????????????????????: Implement automated data quality monitoring tools to continuously assess data against the newly defined standards. ? ?????????? ???????????????? ?????? ?????????????????????? ??????????????????????: Implement the integration in phases, starting with less critical systems, and gradually move towards more complex and critical systems.

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Vincenzo Audino

Senior Manager at KPMG Italy
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To ensure smooth communication between integrated systems: Communication Plan: Develop a plan detailing who communicates what and how. Roles and Responsibilities: Clearly define roles to avoid confusion. Open Communication: Encourage transparency and regular check-ins. Collaboration Tools: Use project management software, instant messaging, and video conferencing. Regular Updates: Keep everyone informed with consistent updates. Conflict Resolution: Address issues promptly to prevent escalation. APIs and Middleware: Utilize custom APIs and middleware for seamless system communication.

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4 Train Staff

Training your staff is an essential step in ensuring that your data quality standards are upheld. Employees from both companies need to understand the new protocols and systems to maintain data integrity. This training should cover not only the technical aspects of any new software or systems but also the importance of data governance and their role in it. By empowering your staff with knowledge and best practices, you're more likely to have a workforce that is vigilant about maintaining high data quality standards.

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5 Monitor Progress

After implementing your data quality standards, it's important to continuously monitor progress. This involves setting up a system for tracking the agreed-upon metrics and regularly reviewing them. By doing this, you can quickly identify and address any discrepancies or issues that arise, ensuring that data quality remains high throughout the integration process. It also allows for ongoing optimization of processes and systems as you learn more about how the merged entity operates.

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6 Adjust Policies

Finally, be prepared to adjust your data quality policies as needed. A merger is a dynamic process, and what works at the outset may need refinement over time. By staying flexible and responsive to new information and changing business needs, you can ensure that your data governance framework supports high-quality data standards in the long term. This might involve revisiting metrics, retraining staff, or even overhauling systems as your understanding of the merged entity's needs evolves.

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You're facing conflicting data quality standards in a merger. How can you ensure seamless integration?

1

2

3

4

5

6

1 Assess Needs

2 Define Metrics

3 Integrate Systems

4 Train Staff

5 Monitor Progress

6 Adjust Policies

Data Governance

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You're facing conflicting data quality standards in a merger. How can you ensure seamless integration?

1

2

3

4

5

6

1 Assess Needs

2 Define Metrics

3 Integrate Systems

4 Train Staff

5 Monitor Progress

6 Adjust Policies

Data Governance

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