You're facing a complex data migration to ERP platforms. How do you ensure accuracy throughout the process?

Con los años de experiencia, los datos son la base, pero todos los datos desde codigos, hasta historicos. Definir exactamente como se subiran los saldos y si se optara por historia, esta debe ser muy precisa y concisa y solo la necesaria por tema de comisiones o similares. Se consolidaran datos duplicados, se haran inventarios fisicos antes de la migraciòn, se auditaran informacion de modulos y contabilidad Los datos deben estar coherentes en el origen antes de la migraciòn y se requieren responsables de dichos datos por si es necesario ajustar la informaciòn

Ensuring accuracy in a complex data migration to ERP platforms requires careful planning, proper data gathering, Cleansing of data, data migration tools to validate and reconcile the data, deep testing after migration, and ongoing monitoring and support for post migration challenges.

With experience over the years, data is king. It's also important to know how the uploading of balances will be done to make sure historical data is accurate and only what we need-for example, commissions. We will aggregate the duplicated data in every system, taking physical inventories before migration. Comprehensive audits of all module and accounting information will be done. It is very important that data become consistent at their source before migration and controllers are in place that can make whatever adjustments may be necessary.

A successful ERP migration requires a thorough focus on data quality. This involves data cleansing to identify and remove duplicates, correct errors, standardize formats, and ensure consistency across systems. Data mapping is essential for defining the relationships between fields in the old and new systems. Validation is another critical step, ensuring data accuracy by comparing it with the source data. Continuous monitoring of data quality throughout the migration process is also crucial.

It is imperative to make both a wide and granular assessment of the data, understand the meaning of your information and guarantee that key users are invested in the process in order to help provide information, knowledge and insight on how to best handle each part of the migration (as is, needs tweaking, needs adaptation, needs additional information, needs sanitizing...). It is intuitive to see that cohesive and clean data will, in the very least, provide a sturdy foundation onto which to build your enterprise workflows. The lack of planning and correct assessment of your data will most likely lead to inconsistencies not only on data but also on how workflows were intended to function in the first place, generating constant rework.

From my experience, data cleaning is an often underestimated step in the data migration process, yet it's one of the most critical. Before moving any data to a new ERP system, I believe it’s essential to thoroughly analyze and clean the existing data. The process of removing duplicates, correcting errors, and standardizing formats might seem tedious, but it’s a foundational task that can make or break the success of the migration. In my view, investing ample time in the data cleaning phase isn’t just advisable—it’s essential. Clean data not only ensures the accuracy of the migrated data but also helps in reducing the overall data volume, making the entire migration process smoother and faster. #syniti #datafirst #businessbenefits

Nomeie uma célula e um líder de dados, ele será responsável por valida-los junto as áreas. Cada área é responsável por fornecer e entregar os dados estratégico e master data integro ao líder de dados. Lembre-se dados são um dos fatores de sucesso de uma implantação, se entrar sujeira, não funcionará direito e o retrabalho será muito maior e doloroso. Identifique e classifique os dados, como; Usuários, permissões, acessos,SOD Dados mestre (produtos,clientes, fornecedores, plano de contas, centro de custo, natureza financeira, bancos,etc.) Dados históricos (movimentos, saldos, qtd de estoque, lotes,etc.) Em cada fase do projeto, valide movimentos e dados, utilize uma ferramenta de mercado ou uma estratégia de carga massiva.

In order to execute a data migration you will have to aligned several departments and stakeholders to review analyze and work on this task sometimes is tedious but the more focus you apply to cleaning process the better result you will get . Talk and design scripts or documentation to help you tracking this will give accountability to parties involved. Review with them periodically and provide green light when done.

Ensuring accuracy during a complex data migration to ERP platforms, particularly NetSuite, involves a combination of meticulous planning, robust validation processes, and leveraging my extensive experience. Here’s how I approach the Data Cleansing phase: I prioritize data cleansing to ensure that only accurate and relevant data is migrated. This involves removing duplicates, correcting errors, and standardizing formats. For example, NetSuite has different CSV templates for different types of data. We can leverage those templates to input the data as per the requirements.

Todo projeto deve ter uma área de TST e uma base de desenvolvimento segregada da PRD. Ter definido e claro o plano de testes e execução é fundamental. Utilize ferramentas de mercado para gestão dos testes ou o velho e bom Excel para controlá-los. Alem do conceito de UAT, recomendo seguir os abaixo; Plano de teste técnico, Plano de teste unitário, Plano de teste integrado, ( fase 1, fase2, e quantas forem necessários) Plano de homologação, (UAT) Plano de war room “stress test” Por fim, Valide os documentos e gere evidências, controle o plano de evolução dos testes e não esqueça de aplicar o bug fix entre uma etapa e outra. A quantidade de plano de testes estará ligada diretamente a satisfação e eficiência dos mesmos Boa sorte e sucesso

Here’s how I approach it through test migrations, drawing from my three years of experience as a 5x Oracle NetSuite Certified ERP Consultant: 1. Sample Data Selection: For the initial test migrations, I select a representative sample of the data. This helps in validating the migration process without overwhelming the system. The sample includes different data types and edge cases to ensure comprehensive testing. 2. Test Environment (Sandbox) Setup: Setting up a dedicated test environment that mirrors the production environment is essential. This environment allows for safe testing without impacting live operations. I ensure that the test environment is regularly updated to reflect any changes in the production environment.

Sem dúvidas os testes são cruciais para uma migração de dados bem sucedida. A validação dos usuários finais também não pode ficar de fora desta etapa.

First first first ! Agreed with internal or external clients amount of data , which data , which users, how many will be executing and testing remember this is a test but the more specifics you get and provide the better test will be shown, assign tasks for validations and also dont scare but be prepared if a roll back is needed.

As migrações de teste são uma etapa essencial na estratégia de migração de dados do ERP. Realizar várias migrações simuladas permite identificar e corrigir problemas sem comprometer os dados reais. Use uma amostra representativa de seus dados para criar um ambiente de teste realista. Analise os resultados com cuidado, ajuste o plano de migração conforme necessário e documente todas as mudanças. Repetir esses testes refina o processo, garantindo maior precisão quando for migrar o conjunto de dados completo.

Compatibility: Ensure the tool is compatible with both your legacy system and the new ERP. Data Mapping Capabilities: Evaluate the tool's ability to handle complex data transformations and mappings. Data Cleansing Features: Look for tools with built-in data cleansing capabilities. Error Handling: Assess the tool's ability to identify and address data errors during the migration process. Data Validation: Evaluate the tool's capacity to validate data integrity and consistency. Performance: Consider the tool's performance, especially for large datasets. Cost-Benefit Analysis: Weigh the cost of the tool against the potential benefits and return on investment.

Choosing a tool is not easy and depends on budget, knowledge, vendor or a bunch of suggestions from internal engineers, make sure they are aligned and agreed from my perspective. Compatible: make sure is compatible with legacy systems and new ERP. Scalable: Think on future needs and size of Data and processes to be managed. Friendly Use : Even manuals are provided or technical team is aware of functionality make sure managed services will be available. Support : As I said above make sure vendor guarantee 24/7 troubleshooting and best practices available. Cost: Keep an eye on licensing, training, and implementation and any other benefit vendor can provide sometimes cheapest one is expensive in the future term.

Com certeza a ferramenta conta muito neste momento. Quanto maior a facilidade, quanto mais intuitiva a ferramenta, melhor. No ERP NextOne temos o importador nativo do sistema, geramos as planilhas para o cliente preencher, auxiliamos na validação das informações e depois realizamos todos os testes necessários junto ao cliente, para assim executar a migração final.

Escolher as ferramentas certas é crucial para o sucesso da migração de dados no ERP. Elas devem ser compatíveis com os sistemas legados e com a nova plataforma, garantindo uma transferência de dados sem falhas. Priorize ferramentas com recursos sólidos de mapeamento de dados, registro de erros e opções de recuperação. Algumas plataformas ERP oferecem ferramentas de migração integradas, mas soluções de terceiros podem proporcionar mais flexibilidade. Independentemente da escolha, as ferramentas devem garantir um processo de migração preciso e eficiente.

Recomendaría de igual manera, revisar las condiciones de sus equipos, como son la memoria RAM, memoria de la máquina, servidores y en general todas las condiciones que el proveedor recomiende para que el ERP corra correctamente.

Fase de Hypercare ou Poolcare, Dependendo do tamanho, importância e complexidade do projeto, ambos se farão necessários. Recomendo usar o Hypercare ao menos durante os dois ou três próximos fechamentos, você pode iniciar com acompanhamento assistido full time nos primeiros dias e semanas e depois mudar para part time, trazendo os recursos do parceiro quando necessário. Durante as fases mais importantes, como fechamento de vendas, administrativo ou outros, sugiro acompanhamento full.. Tudo depende da evolução do projeto, adoção das áreas, , curva de aprendizagem, SKUs e complexidade. Prefira pecar pelo excesso do que pela falta, o risco de uma não entrega ou de uma parada não programada, não tem preço

Ongoing data quality checks are essential for maintaining the integrity of your ERP system. Here's how to ensure data accuracy: Regular Data Audits: Conduct routine audits to identify and correct data inconsistencies. Data Profiling: Analyze data to identify patterns, anomalies, and potential quality issues. Data Cleansing: Remove duplicates, errors, and inconsistencies from the system. Data Standardization: Enforce consistent data formats and definitions across the organization. Data Validation: Implement data validation rules to prevent incorrect data entry.

No item Planejamento, sugeri a formação de um grupo que possa participar desde o planejamento até o final do processo de implementação. Essa mesma equipe deve interagir com os usuários de cada setores, monitorando o funcionamento e feedback. Além desse monitoramento humano, pode-se criar um canal de comunicação digital (e-mail, chat, Whatsapp, telefone) para receber informações dos usuários.

Ensuring accuracy is detailed and rigorous. Use your experience to dip test the data by effective sampling. priortise the critical data. Use the available tools effectively to put attention and effort to the desired area and monitor all deviations in test phase. collaborating with users is important in view of task enormity.

Mesmo após a migração bem-sucedida, o monitoramento contínuo é fundamental para garantir a precisão dos dados. Acompanhe o sistema para identificar anomalias ou erros que possam surgir e tenha um plano para corrigi-los rapidamente. Realize auditorias regulares no ERP para detectar discrepâncias que possam impactar as operações. Também é importante coletar feedback dos usuários finais sobre a precisão dos dados e o desempenho do sistema, abordando imediatamente quaisquer preocupações levantadas.

Data migration to an ERP platform is a complex task that requires meticulous planning, execution, and validation to ensure accuracy. By following these best practices—pre-migration planning, careful execution, post-migration validation, thorough documentation, and clear communication—you can significantly reduce the risk of errors and ensure a successful transition to the new ERP system. The key to a successful data migration lies in attention to detail, robust testing, and a proactive approach to managing potential issues. With the right strategy and tools in place, you can achieve a smooth and accurate data migration that supports your organization’s goals and operations.

I begin by designing comprehensive training programs tailored to the specific needs of the organization. This includes both foundational training for new users and advanced sessions for those who will be handling more complex tasks. In my past projects, I have found that breaking down training into digestible modules helps users absorb information more effectively. Another key element of my training sessions is hands-on practice. By providing real-life scenarios and data, users can directly apply what they've learned in a controlled environment. For example, during a data migration project for a previous client, I created a sandbox environment where users could simulate data entry, validation, and correction processes.

training is often the activity which gets suqeezed when projects overrun so the best thing to do is start the training early in the timetable - rather than leave it to the end. Training can also be designed to complement the project work - like bringing end users in to assist in mapping, testing and creating documentation.

Important since continuity for any migration process these taks should be performed in order to succeed with train the trainers model: Training sessions should be real scenarios based and with differnt user roles. Sandbox environment is best so users can touch it anytime they want and test freely . Make sure creating a container to keep manuals , documentation,presentations and all that is needed to offer as future guidance. Understand that there is a natural human behavior to react on major changes . Guarantee that users will be able to call or email help desk or support or managed services after migration phase. Be open to receive feedback while training sessions occurs , best method to improve with future migration processes.

Start with data source 1. Plan and Assess: o Data Mapping: Align all old with the new system data fields. o Risk Identification: Spot potential issues early itself. 2. Clean Data: o Quality Check: Remove duplicates, identify junks data, special characters. o Standardize: Ensure consistent data formats and types. 3. Pilot Test: o Subset Testing: Migrate a small data sample. o Validation: Check for accuracy and completeness. 4. Execute Migration: o Automated Tools: Use reliable software to minimize errors. o Real-time Monitoring: Track progress and resolve issues immediately. 5. Post-Migration Validation: o Reconciliation: Count, Compare new and old system data. o User Testing: Have end-users verify data accuracy.