Last updated on 2024年9月7日

What are the key skills and competencies for master data integration professionals?

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Master data integration is the process of combining, transforming, and validating data from different sources to create a single, consistent, and accurate view of the most important entities for a business, such as customers, products, suppliers, or assets. Master data integration professionals are responsible for designing, implementing, and maintaining the solutions and tools that enable this process, as well as ensuring the quality, security, and governance of the master data. In this article, we will explore some of the key skills and competencies that master data integration professionals need to succeed in their role.

本文章的要点总结

Data quality management:

Ensuring accurate, complete, and consistent data is crucial. Use tools for data profiling and cleansing to maintain high-quality master data that your business can rely on.
Leadership support for data governance:

When leaders emphasize the importance of good data practices, it encourages a culture of responsibility and excellence across all teams.

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1 Data Modeling and Mapping

One of the core skills for master data integration professionals is data modeling and mapping. This involves defining the structure, relationships, and attributes of the master data entities, as well as the rules and transformations that apply to them. Data modeling and mapping require a good understanding of the business requirements, the data sources, and the target systems or platforms. Master data integration professionals need to use data modeling and mapping tools, such as ER diagrams, UML diagrams, XML schemas, or ETL tools, to create and document the master data model and the data integration logic.

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Ashish Saini

MDM | JAVA | IDQ | CDI Certified Developer at Informatica
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In Master Data Management systems, data typically takes on a relational form and is stored within RDBMSs. For efficient storage, the development of a data model becomes crucial. Understanding key concepts like data types, ER Diagrams, various notations, and normalization techniques is essential. Additionally, it's important to customize data models according to the specific MDM tool in use, as each tool may require data to be structured in a particular manner to fully utilize its functionalities.

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2 Data Quality and Validation

Another essential skill for master data integration professionals is data quality and validation. This involves ensuring that the master data is accurate, complete, consistent, and compliant with the business rules and standards. Data quality and validation require a good knowledge of the data domains, the data sources, and the data quality dimensions, such as accuracy, completeness, consistency, timeliness, or uniqueness. Master data integration professionals need to use data quality and validation tools, such as data profiling, data cleansing, data matching, or data auditing, to measure, monitor, and improve the quality of the master data.

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Kadhirvelu Ratnasabapathi

VP at Seacoast Bank, LinkedIn Top Voice: | Data Governance | Data Quality | Data Management | Data Modeling | Business Analysis | Business Intelligence | Data Warehousing | Data Architecture | Master Data Management
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Data Quality Management is one of the skills needed for building Master Data solution. While considering data from multiple sources and integrating, the data can be of different standards, incomplete, inconsistent, and invalid. In another level, duplicates need focus on Match and Merge effort. Master Data integration professionals need to be well-versed and experienced in Data Quality Management.

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Ashish Saini

MDM | JAVA | IDQ | CDI Certified Developer at Informatica
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It is really important to know the domain data inside out, where it's from, how is it related, and how is it in terms of quality, like being accurate, complete, consistent, valid, and all that. To accomplish this very often you would have to rely on profiling tools such as Informatica Data Quality to get understandable and actionable insights into your data.

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3 Data Governance and Security

A third key skill for master data integration professionals is data governance and security. This involves establishing and enforcing the policies, procedures, roles, and responsibilities that govern the access, use, and modification of the master data. Data governance and security require a good awareness of the legal, ethical, and regulatory aspects of the data, as well as the business objectives and stakeholders. Master data integration professionals need to use data governance and security tools, such as data catalogs, data lineage, data stewardship, or data encryption, to manage, protect, and document the master data.

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Diane Pappas

Senior Manager Strategic Customer Advisory, Blackline
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In my experience, for data governance to be successful, you need support from leadership in addition to the items listed above. All organizations have an impact on data governance and hearing from their leaders that it is a priority will help ensure good data practices across all teams.

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JL Suarez, MBA

?? 50 x LinkedIn Top Voice: ??Senior Manager of Corporate Insights, Analytics, and Data at Holiday Inn Club Vacations??: Inspiring Innovation & Leadership in Business Intelligence??
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I can't stress enough the importance of a solid game plan. It's like coaching a winning team - you need to know your players (data) and have a playbook (policies and procedures) that everyone follows. And let's not forget about defense (data encryption) to keep the bad guys out! Keep your data game strong and your master data will be scoring points for your business objectives. ?? #DataGovernance #DataSecurity #WinningWithData

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4 Data Communication and Collaboration

A fourth important skill for master data integration professionals is data communication and collaboration. This involves communicating and collaborating with the various stakeholders involved in the master data integration process, such as business users, data owners, data analysts, data engineers, or data architects. Data communication and collaboration require a good ability to listen, understand, and explain the master data concepts, issues, and solutions. Master data integration professionals need to use data communication and collaboration tools, such as data visualization, data storytelling, or data literacy, to present, share, and educate the master data insights and outcomes.

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5 Data Analysis and Problem-Solving

A fifth essential skill for master data integration professionals is data analysis and problem-solving. This involves analyzing and solving the various challenges and opportunities that arise in the master data integration process, such as data conflicts, data gaps, data errors, or data enhancements. Data analysis and problem-solving require a good aptitude for logic, creativity, and critical thinking. Master data integration professionals need to use data analysis and problem-solving tools, such as SQL, Python, or R, to query, manipulate, and explore the master data.

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Surya Santosh Kotikalapudi

Technology Manager
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Mastering the master data requires complete understanding of the data. One should have an understanding of the life cycle of the data from its birth to termination, and the ways the data gets maintained to solve the inconsistencies and improve quality.

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6 Data Learning and Adaptation

A sixth key skill for master data integration professionals is data learning and adaptation. This involves learning and adapting to the changing needs and expectations of the master data integration process, such as new data sources, new business requirements, new technologies, or new best practices. Data learning and adaptation require a good attitude for curiosity, innovation, and continuous improvement. Master data integration professionals need to use data learning and adaptation tools, such as online courses, blogs, podcasts, or webinars, to stay updated and informed about the latest trends and developments in the master data field.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the key skills and competencies for master data integration professionals?

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1 Data Modeling and Mapping

2 Data Quality and Validation

3 Data Governance and Security

4 Data Communication and Collaboration

5 Data Analysis and Problem-Solving

6 Data Learning and Adaptation

7 Here’s what else to consider

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