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How can you balance technical and business requirements as an MDM professional?

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As an MDM professional, you are responsible for managing the quality, consistency, and governance of master data across different systems and domains. Master data is the core information that defines the entities and relationships of an organization, such as customers, products, suppliers, locations, and hierarchies. Master data management (MDM) is the process of creating, updating, and maintaining a single source of truth for master data, ensuring its accuracy, completeness, and timeliness.

However, MDM is not only a technical challenge, but also a business one. You need to balance the technical and business requirements of various stakeholders, such as data owners, data consumers, data stewards, and data architects. How can you achieve this balance and deliver value to your organization? Here are some tips to help you.

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1 Understand the business context

Before you design and implement an MDM solution, you need to understand the business context and objectives of your organization. What are the key drivers and pain points for MDM? What are the expected benefits and outcomes? Who are the main users and decision-makers of master data? How do they use and interact with master data? What are their expectations and preferences? By answering these questions, you can align your MDM strategy and scope with the business vision and priorities.

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Jayant Swamy

CTO | Machine Learning | Deep Learning | Artificial Intelligence | Data Engineering | Technologist | Strategy | ex-Accenture
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Understand first that Data is a business Asset - technology/ solution etc need to drive to that. With that in mind , Mdm needs to owned by the business - Unfortunately given that so Much starts revolving around the tool and implementation- the business owner steps aside , is overwhelmed, sidelined for long, becomes passive participant, take your pick - is where MDM start going astray. The implementation has to reflect the business process need and not vice versa. Make the org alignment properly - and systems will follow.

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Amir K.

Customer Exprience Ambssador | PYTHON , R ,SQL | AZURE CERTIFIED | ASPIRING AI ENG
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Before deploying an MDM solution, assess your organization's needs. Identify drivers like compliance, data quality, and efficiency, and the expected gains such as improved decision-making and operational savings. Understand users like data stewards, IT, and executives—how they use master data and their expectations. Align your MDM strategy with the company's goals, focusing on pain points like data consistency and process streamlining. Set clear objectives and KPIs to measure the MDM's success and ensure it supports business growth. Engage with stakeholders to capture their needs and ensure the MDM solution delivers tangible benefits.

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Cleber Pires

Senior Client Partner | Digital Strategist na act digital
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é muito comum, empresas procurarem MDM como uma solu??o sem compreender explicitamente as dores e problemas de negócio a resolver. E compreender, significa aprofundar a ponto de conseguir decompor os problemas em conquistas e indicadores tangíveis e do tamanho que possam ser materializados em uma solu??o.

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2 Involve the stakeholders

One of the most critical factors for a successful MDM project is stakeholder engagement and collaboration. You need to involve the stakeholders from the beginning and throughout the MDM lifecycle, from planning and design to implementation and maintenance. You need to communicate clearly and regularly with them, solicit their feedback and input, and address their concerns and issues. You also need to establish roles and responsibilities for each stakeholder, such as who owns, creates, updates, approves, and consumes master data. By involving the stakeholders, you can ensure that your MDM solution meets their needs and expectations, and that they adopt and support it.

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Amir K.

Customer Exprience Ambssador | PYTHON , R ,SQL | AZURE CERTIFIED | ASPIRING AI ENG
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Involving stakeholders is vital for MDM success. Engage them early and continuously, from planning to maintenance. Communicate often, seeking their feedback, and resolving their concerns. Define clear roles, like data ownership and approval, ensuring the MDM meets needs and gains support.

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Cleber Pires

Senior Client Partner | Digital Strategist na act digital
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Além do engajamento das partes interessadas, é crucial discutir a importancia da governan?a de dados no MDM. Estabelecer políticas claras e procedimentos de governan?a ajuda a manter a qualidade, a seguran?a e a integridade dos dados mestres ao longo do tempo

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3 Define the standards and rules

Another essential aspect of MDM is defining the standards and rules for master data quality, consistency, and governance. You need to define what constitutes master data, how it is structured and classified, what are the sources and targets of master data, how it is validated and cleansed, how it is integrated and synchronized, how it is secured and audited, and how it is measured and monitored. You need to document these standards and rules, and enforce them through policies and procedures. You also need to review and update them periodically, to reflect changes in the business environment and requirements.

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Amir K.

Customer Exprience Ambssador | PYTHON , R ,SQL | AZURE CERTIFIED | ASPIRING AI ENG
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For effective MDM, set clear standards for data quality and governance. Establish what qualifies as master data, its structure, and classification. Define its sources, validation, integration, security, and auditing processes. Document and enforce these rules with policies and regularly update them to stay current with business changes.

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Cleber Pires

Senior Client Partner | Digital Strategist na act digital
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é importante criar padr?es de dados mestre que n?o sejam apenas rígidos e bem definidos, mas também flexíveis e escaláveis. Isso permitirá que a organiza??o se adapte rapidamente a mudan?as no ambiente de negócios ou em suas necessidades de dados.

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4 Choose the right tools and technologies

Finally, you need to choose the right tools and technologies to support your MDM solution. You need to consider the technical capabilities and limitations of your existing systems and platforms, as well as the future scalability and flexibility of your MDM architecture. You need to evaluate the features and functionalities of different MDM tools and technologies, such as data modeling, data integration, data quality, data governance, data security, data analytics, and data visualization. You need to select the ones that best fit your technical and business requirements, and that can deliver the desired performance and results.

MDM is a complex and challenging discipline, but also a rewarding and valuable one. By balancing the technical and business requirements of your MDM solution, you can optimize your master data management and enhance your data engineering skills.

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Cleber Pires

Senior Client Partner | Digital Strategist na act digital
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Algumas perspectivas sobre ferramentas: IA/ML Podem ser usados para automatizar processos de limpeza, valida??o e enriquecimento de dados. Por exemplo, algoritmos podem identificar e corrigir erros, inconsistências ou duplicatas em dados mestre. IA pode ajudar na classifica??o e categoriza??o de dados mestre, aprendendo com padr?es e exemplos passados para aplicar categorias corretas a novos conjuntos de dados. Algoritmos de ML s?o excelentes em detectar anomalias, para manter a integridade dos dados mestre. Eles podem identificar padr?es anormais ou atividades suspeitas que podem indicar problemas nos dados ou tentativas de viola??o de seguran?a.

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5 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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Cleber Pires

Senior Client Partner | Digital Strategist na act digital
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Utilizando análise preditiva, as organiza??es podem antecipar futuras tendências e necessidades de dados mestre, permitindo um planejamento mais proativo. Algoritmos podem sugerir a??es para melhorar a qualidade dos dados mestre ou para alinhar a gest?o de dados com as estratégias de negócios.

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How can you balance technical and business requirements as an MDM professional?

1

2

3

4

5

1 Understand the business context

2 Involve the stakeholders

3 Define the standards and rules

4 Choose the right tools and technologies

5 Here’s what else to consider

Data Engineering

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How can you balance technical and business requirements as an MDM professional?

1

2

3

4

5

1 Understand the business context

2 Involve the stakeholders

3 Define the standards and rules

4 Choose the right tools and technologies

5 Here’s what else to consider

Data Engineering

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