Need to migrate your current ERP database to the cloud or a totally new ERP platform?

If you need to migrate your current ERP database to the cloud or a totally new ERP platform, all kinds of challenges can come into play and wreak havoc on the accuracy and completeness of your transferred data. Time, cost, data redundancy and integrity issues, stakeholder support and potential regulatory concerns are just some of these hurdles.

While Fostering a Continuous Reporting Environment for Users

How can you and your company ensure that data isn't being leaked to competitors?

Did your ERP pass the recovery environment test?

If you're splitting your ERP workflow into layers, be careful with a single ERP solution provider!

On the positive side, though, a new ERP implementation or move to the cloud provides an opportunity for you to sort out the data you really need to keep.  Leveraging a data hub can help you do so by storing and managing historical data like AP/AR, purchasing and sales history while the implementation is taking place.  The beauty of incorporating a data hub as part of a data migration strategy is that it can dramatically reduce the volume of data that you will ultimately need to load into your new ERP.  Plus, with your history in the data hub, business analysts and others can drive their analytics and reports in an uninterrupted manner while the new ERP implementation is underway and long afterwards.

But even if you don’t have a data hub now, many of the considerations you’ll need to make during your ERP migration are the same as the ones you would need to address when putting an enterprise data hub and reporting platform into place. 

This article covers all of these considerations and provides some proven best practices to help guide you in a successful migration of data from your current to new ERP system.

Why Is Data Migration So Important to a New ERP Implementation or Move to the Cloud?

The data migration process is critical to ensuring that the data in a new ERP system is accurate and complete.  This is vital because many people across the business will rely on that historical data.

A well-planned data migration strategy can help keep the entire ERP implementation project on time and on budget while addressing challenges like these:

Data Duplication

Multiple departments may store their own copies of information about the same customers or products in an ERP system, but their data may not be identical. For example, customer names and addresses may be stored in different formats or with varying addresses for the same customer .  And for multiple ERP instances, different customer or product numbers for the same customers and products may exist.  The list goes on!  If you simply import every record from each business unit into the ERP database, you could end up with tons of duplications and inaccuracies.  A migration strategy that includes a data hub for consolidating and storing this historical data can help overcome such redundancy and integrity obstacles.

Migration Costs

The cost of extracting, cleansing and restructuring data represents a significant percentage of the overall ERP migration budget. Such costs are a big reason why we are seeing more businesses invest in the use corporate data hubs as part of their migration projects.  This is particularly true for organizations that face very high migration costs due to the overwhelming volumes of data they need to manage.

Stakeholder Buy-in

As previously mentioned, various business units may use their own disparate systems to support their specific needs. That’s why it is necessary to involve management to ensure that everyone cooperates to produce a single, consistent set of data.

I can’t underscore enough how important it is to have proper executive buy-in during a data migration project – both at the beginning of it and throughout the process.  

Successful clients, for example, include regular executive involvement in some form or another such as regularly scheduled status meetings, budget reviews, or general exposure to the migration process by C- and V-level users across the organization.

 

Data Migration Best Practices

To guide the organization through the often-complex process of moving data to a new ERP platform or cloud-based deployment, it’s important to create an ERP data migration strategy that encompasses several key elements.

Create a migration team

To ensure a successful data migration, the process should start early to avoid delaying the ERP deployment. We recommend to our clients that they dedicate a team to analyzing the data, performing the migration and validating the results. The planning team can decide on what to include vs. exclude and the timing of what data should be migrated to the new ERP (and what should be stored in the data hub until the actual ERP implementation takes place if that’s the route they wish to take).

The data migration team is typically part of the overall ERP implementation team. Representatives from different business groups who can provide insights into how data is used by their respective business units or departments should be included on the team.

A good planning team like this can make a huge impact on a data migration project’s success. The team should include stakeholders – not just top/upper management.  

You also need individuals that understand the data, the processes in which the data is used, and the reports your company will need following the migration.  We recommend to our clients that they create an inventory of reports used on a monthly, weekly, daily basis to assist in this planning segment of their migration journeys.  By doing so, the data used in those reports can be identified and included in the ERP migration plan much more easily.

Consider how you want to use your business data

Before starting the migration, spend time assessing your existing data, thinking about how it will be used within the ERP system, mapping it to the new ERP database, and setting up rules for translating the data to the new database structure during migration.

An ERP implementation is an opportunity to gain better insights into the business in real time by analyzing its data. So, when migrating, think about how data will be used for decision-making across the whole business, as well as by each department.  Many of Silvon’s customers generally want to attack their worst business pains first and the corresponding data sets are the ones we help store and manage for them within our data hub environment from the very beginning of their ERP migration projects.

There are many approaches for considering the broader use of business data outside of the main ERP database, too. 

How much history should be carried over?  

What should be migrated first?  

A data hub platform can offer up proven best practices and techniques for importing and managing history from a single ERP, multiple instances of an ERP, and different ERPs that may be deployed across an enterprise.

Assign data governance responsibilities

Determine who owns which data and assign roles to your team. For example, the team will have to determine which version of redundant customer information is correct and should be incorporated into the ERP system and corporate data hub if you use one. Now is also a good time to appoint someone with overall responsibility for compliance with any regulations that affect your business. Data governance also works well on the front end when building and managing analytics and reports based on your ERP data.

Less is More – Be selective with the data you migrate

You may be tempted to import every piece of data from your old ERP into your new system, but not all historical data is useful or needs to be immediately accessible. In fact, importing every historical data item can slow system performance and make it harder for users to find the information they need.

Having a smaller quantity of trusted data makes migration faster and less risky. This approach also allows you to deliver critical information in a very tight schedule before go-live. As a fallback, you may elect to store historical data that won’t be migrated in a separate system like a data hub should you need it for analysis or other uses later.

Strongly consider a data hub to streamline your migration efforts

As mentioned earlier, taking advantage of a new or existing data hub during the migration to a new ERP can streamline your migration effort while ensuring that your reporting initiatives continue smoothly as the new implementation takes place.

Keep in mind that while the volume of data you’ll need to load into your new ERP will be reduced using a data hub, you will still need to convert old codes to new codes in the new ERP – such as customer, product, product categories, etc. based on the ERP’s requirements for the data types and format of the codes.  Fortunately, you can use the Master Data in your data hub to maintain your old codes and to “seed” Master Data in your new ERP.

If possible, consider acquiring and bringing a data hub solution on-line BEFORE you begin your ERP implementation.  You’ll be in a much better position to tackle your data migration with far fewer headaches by doing so!

Consolidating your data may be beneficial

To understand what transformation is required to prepare legacy data for upload, you should have a clear vision of the results you’re expecting. 

Which data is needed for critical reports? How many details must be there?

Will summary be good enough?

ERP systems allow you to bring consolidated information through journals that group legacy transactions based on certain criteria. You may want to consider this approach if you wish to minimize the amount of information you’ll need to upload to the new system to support your operations and reporting requirements.

Silvon has worked with several clients that consolidate their data. For these businesses, we typically globalize customer and product numbers as needed while also making the numbers unique to avoid any data summarization issues.  This ensures that the ERP data they bring into our data hub for planning, analysis and reporting provides the amount of detail they need.

Be sure to reconcile the data

How can you check that all information is correct when you migrate your transactional data to the new ERP? 

After all the transformations, you need to make sure that results can be trusted. For that reason, we recommend that a reconciliation stage be included in the migration plan.

Business users should be provided with reports of summarized data for their review. While reports may not match exactly because of new account structures, etc. in the new ERP system, the totals should match. The data migration team can then map your new accounts back to legacy ones to verify line details.

If you consolidate your data, you’ll find it easier to review and fix issues on the line level because once a successful reconciliation of one journal has been completed, most issues of data conversion can be discovered and the next journals will take a fraction of the time to migrate and verify.

If you decide prior to your new ERP implementation to employ a data hub to store your historical data during the migration, data balancing (or reconciliation) will play an important and integral part of the hub’s implementation, too.  Our clients typically find that the reconciliation process is much more successful when there’s involvement from migration team analysts as well.

Test early and often

Testing early and many times over the course of your ERP migration project can help to ensure its success.

• Start testing your new system with small amounts of your migrated data as early as possible and gradually build up to more comprehensive testing over time. This strategy also works well if you decide to create a data hub for your analysts and other business users as part of your ERP migration strategy.
• Start with representative subsets of customers and orders, and then gradually expand to cover all data, applications and uses.
• Go through your checklist and run tests in which users go through their entire day-to-day processes on the new system.  This can expose problems that might otherwise be missed.
• If you use a data hub to store your ERP reporting data, setting up a nightly load process will help you expose and resolve issues as well.

Dangerous Narrative AI

 The dominant discourse around AIG, particularly LLMs, is tainted by a dangerous narrative, which is the misattribution of human capabilities such as “reasoning,” “thinking,” and “interpretability” to systems that, in essence, operate in radically different ways. The article below, in a lucid analysis, based on strong evidence, demonstrates that this tendency is misguided.

A closer understanding of the inner workings of these models reveals that the so-called “intermediate tokens,” the steps in information processing, are not manifestations of thought or reasoning. They are better explained by complex mathematical and statistical structures, visualized in elegant graphs, but fundamentally non-anthropomorphic. What is observed is, in reality, a process of prompt augmentation, highly dependent on formal checkers. These checkers are external mechanisms that guide and validate the process within specific and limited settings, not an intrinsic capability of the model.

The term “interpretability” has acquired a worrisome status. When applied to the analysis of these intermediate tokens without a robust causal basis and real verifiability, it is nothing more than an attempt to extract meaning from what is merely a complex statistical correlation. Just as the protrusions on a person’s skull do not reveal a person’s future or health status, the “interpretation” of an AI model’s internal signals lacks scientific foundation.

The anthropomorphization of terms like reasoning and interpretability is, in fact, a misappropriation of language to lend illusory credibility, attract investment, or mask technical limitations. And when adopted by consumers and enthusiasts, this same anthropomorphization often reflects a lack of access to technical criticism. It is an understandable mistake, but no less damaging for perpetuating myths.

The tendency to anthropomorphize sophisticated statistical systems is not benign. It obscures the true nature of the technology, opens the door to excessive hype, and diverts resources and attention from AI research beyond AIG. Recognizing that prompt augmentation guided by formal verifiers is at the core of current operation, and not a simulacrum of thought, is essential for sound and ethical AI development. The future of AI depends on the ability to distinguish between mathematical elegance and the illusion of consciousness.

* AIG - Artificial Inteligence Generative

Privacy ...A word that is almost extinct

Privacy A word that is almost extinct... The end of privacy in the digital world is the result of the increasing erosion of the barriers that once protected personal life in the context of technology. With the expansion of the internet, social networks and connected devices, the amount of data generated by individuals has skyrocketed. This data, from consumption habits to real-time locations, is collected, stored and often monetized by companies and governments, often without the explicit consent or full understanding of users. 

The emergence of generative AI and its hunger for data to train models has made this scenario even more explicit.

All of our online steps leave permanent traces. On average, a person connected to the internet generates an impressive amount of information daily. For example, studies show that, in 2023, a typical smartphone user produced around 1.5 GB of data per month in network traffic alone. This includes browsing, streaming and applications. But the digital trail goes far beyond that, as it encompasses everything you click, search, post, like or even hesitate before deciding.

Social media amplifies this volume. An active user on X, for example, can generate dozens or hundreds of interactions per day (posts, likes, retweets), each recorded with metadata such as time, location and device. Google, for its part, processes around 99,000 searches per second globally, and each search contributes to a profile that can already contain thousands of data points about a person, from what you search for to the time you spend reading results.

In addition, digital culture has changed expectations of privacy. People voluntarily share intimate details on social media, while devices such as virtual assistants and home security cameras capture audio and video in spaces previously considered inviolable. The combination of voluntary exposure and involuntary collection creates a scenario where the concept of the “private sphere” is increasingly blurred.

The prevailing trend is towards a world where privacy, as we once knew it, seems like a luxury of the past, replaced by convenience, connectivity and an inescapable sense of enforced transparency.

What ? What Is AI and What Is Not AI?

So, what exactly is AI? 

In simple words, AI refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. 

An AI solution has five key building blocks.

1. Data: Data means numbers, characters, images, audio, video, symbols, or any repository on which operations can be performed by a computer.
2. Algorithm: An algorithm is a sequence of calculations and rules used to solve a problem using data that is optimized in terms of time and space.
3. Model: A model is a combination of data and algorithms used to generate the response. Once you have a model, you can constantly provide it with new data and algorithms and continue its refinement. 
4. Response: The responses are the results or outputs from the models. The outputs are based on the specific objectives that could be related to automating tasks, providing insights, aiding decision-making processes, and more.
5. Ethics: Ethics refers to the moral principles and guidelines governing the collection, processing, analysis, interpretation, and application of data and insights in AI. Ethical considerations are crucial in ensuring that data-driven outputs contribute to the positive social, economic, and environmental impacts of the organization and the community.

has three key characteristics:

1. Learning: The ability to learn from data and improve over time without explicit programming.
2. Adaptability: The capability to adapt to new situations and use cases beyond their initial or original purpose. An AI system should have the capability to reason or think and address the objectives through logical deduction.
3. Autonomy: The AI system should perform tasks independently with minimal or even zero human intervention.

There are details in implementations, to have integrated or applied AI... that we will not explain here yet...

Other than that, no AI, simple!

 houve um aumento sem precedentes no interesse em torno da inteligência artificial (IA). Essa atenção aumentada abrange uma infinidade de setores, incluindo empresas comerciais, empresas de tecnologia, empresas de capital de risco, universidades, governos, veículos de mídia e muito mais. À medida que o interesse em IA se intensifica, algumas empresas até renomearam suas soluções de software existentes como produtos de "IA", um fenômeno frequentemente chamado de "lavagem de IA". Além disso, há também um crescente senso de "FOMO" (medo de ficar de fora) entre as corporações em relação à adoção de IA.

Então, o que exatamente é IA? Em palavras simples, IA se refere à simulação da inteligência humana em máquinas que são programadas para pensar, aprender e tomar decisões.

**Uma solução de IA tem cinco blocos de construção principais.**

**Dados:** Dados significam números, caracteres, imagens, áudio, vídeo, símbolos ou qualquer repositório no qual operações podem ser executadas por um computador.

**Algoritmo:** Um algoritmo é uma sequência de cálculos e regras usadas para resolver um problema usando dados que são otimizados em termos de tempo e espaço.

**Modelo:** Um modelo é uma combinação de dados e algoritmos usados ​​para gerar a resposta. Depois de ter um modelo, você pode constantemente fornecer novos dados e algoritmos e continuar seu refinamento.

**Resposta:** As respostas são os resultados ou saídas dos modelos. As saídas são baseadas nos objetivos específicos que podem estar relacionados à automação de tarefas, fornecimento de insights, auxílio em processos de tomada de decisão e muito mais.

**Ética:** Ética se refere aos princípios e diretrizes morais que regem a coleta, processamento, análise, interpretação e aplicação de dados e insights em IA. Considerações éticas são cruciais para garantir que as saídas baseadas em dados contribuam para os impactos sociais, econômicos e ambientais positivos da organização e da comunidade.

No entanto, o termo “IA” frequentemente causa confusão devido ao seu uso amplo e às vezes vago. Um sistema de “IA verdadeira”, composto por um agente que executa a tarefa no ambiente, tem três características principais:

**Aprendizado:** A capacidade de aprender com dados e melhorar ao longo do tempo sem programação explícita.

**Adaptabilidade:** A capacidade de se adaptar a novas situações e casos de uso além de seu propósito inicial ou original. Um sistema de IA deve ter a capacidade de raciocinar ou pensar e abordar os objetivos por meio de dedução lógica.

**Autonomia:* O sistema de IA deve executar tarefas de forma independente com intervenção humana mínima ou mesmo zero.

Na prática, a IA pode funcionar em qualquer situação em que se possa derivar padrões de dados e formular regras para processamento. Em outras palavras, os sistemas de IA têm um desempenho ruim em ambientes imprevisíveis e não estruturados, onde há falta de objetivos claros, dados de qualidade e regras predefinidas. Embora a IA possa analisar grandes quantidades de dados, identificar padrões e derivar regras, ela não pode gerar hipóteses verdadeiramente novas. A verdadeira inovação geralmente requer intuição e uma compreensão sólida de princípios e práticas de inovação mais amplos. Por último, mas não menos importante, a IA pode lutar com dilemas éticos e tomar decisões que exigem raciocínio moral, empatia e compreensão da cultura e dos valores humanos.

Então, quais são os casos de uso reais da IA? 

Onde as três características da IA ​​discutidas acima são aplicadas ou usadas? 

Um veículo autônomo é um exemplo clássico de uma solução de IA que aplica as três características da IA, ou seja, aprendizado, raciocínio e tomada de decisão, em tempo real, para criar um veículo capaz de dirigir sem intervenção humana. 

Os veículos autônomos da Waymo são equipados com um conjunto de sensores, recursos de LiDAR e radar e câmeras de alta definição para coletar grandes quantidades de dados sobre a navegação do veículo e seus arredores. Algoritmos avançados de aprendizado de máquina são usados ​​para processar e interpretar esses dados. Esses modelos são constantemente treinados em vastos conjuntos de dados, permitindo que o carro reconheça e categorize novos objetos e situações, preveja as ações de outros usuários da estrada e tome decisões de direção em tempo real para garantir uma direção segura e eficiente.

Outro caso de uso em que as três características da IA ​​— aprendizado, raciocínio e tomada de decisão — são usadas é na escrita de um livro. Sistemas de IA como o ChatGPT são treinados em extensos conjuntos de dados, como livros, artigos e outros tipos de conteúdo. Esse treinamento permite que a IA entenda padrões de linguagem, estruturas narrativas e elementos estilísticos. A IA pode obter conhecimento para entender os componentes de uma história e raciocinar sobre as motivações dos personagens para criar enredos envolventes. 

À medida que a história avança, a IA pode tomar decisões sobre reviravoltas na trama e ações dos personagens. 

Aqui está um exemplo de como o ChatGPT, que usa a arquitetura GPT-4, escreveu uma história sobre mim (mesmo que algumas das respostas sejam imprecisas ainda ou talvez sempre imprecisas ).

IA é o seu Ás!

IA é nada ou mundo de novo para alguns, ou até tudo para alguns, mas sem falsas modéstias mas quem vive, cresceu na área de programação de computadores e está ligado as evoluções desde BigData, Deep Leraning ... e ai ..ai vem IA !  Como usar ?  Como requerer ?  Como fazer ser estratégico... 

Se você jogar suas cartas corretamente, 

AI é o seu ás!

O que está realmente acontecendo:

1. Mudança de potência

- Do monopólio aos indivíduos

- Da permissão à criação

- De guardiões a sonhadores

2. Super ferramentas, democracia!

- IA não é a história

- Sua visão é

- Os resultados falam mais alto

3. Nova Sociedade Criativa

- Não há mais espera

- Não mais perguntar

- Sem mais limites

4. Porque aqui está a verdade:

O público não se importa com suas ferramentas

Eles se importam com o que você faz com que eles se sintam

Eles se importam com o que você cria

Eles se importam com sua história

Os portões estão fechados.

As ferramentas são acessíveis.

O mundo está pronto!

Sua jogada.

Recorde : Eles vão se lembrar o que você criou

Não é o que você criou.

Which way data security...

In a scenario where technology and the internet permeate all aspects of business, cybersecurity has become a key strategic issue for companies of all sizes.

Organizations are increasingly aware that failure to protect sensitive data and internal systems can have serious consequences.

Data leaks, breaches and other security incidents don’t just affect financially and operationally; they also directly impact the reputation and trust that customers, partners and the market place in the brand.

But companies still do not know how to hire cyber security services, in a planned way in alignment with the solutions that make the process of data inputs and outputs that the company has with platforms that it uses, mobile apps, access and access control of your employees and customer data as well.

They say a lot about the subject, and in the end you think that a security product is the solution and it’s not! And when they find out it was not effective, but the fault is the product or that the company has no strategy.

Which way is this theme so talked about, commented and feared, which way? 

Agilidade ou rapidez ??? - - Agility or speed ???

In our projects and searches for better production, I seek tools that can support me to design and implement projects in a more agile and productive way, with ballast from where, how it was done and knowing what reflects in the project I develop for customers, this I use the word agile and not fast. This because the speed can generate for the theme of implement, software engineering, programming a software instabilities, insecurity, the word agile reserves within it the speed of a method that practice became so agile that you or your team performs fast, this acquires with training, experience and failures to result in good practices, good results and agile deliveries are low, medium and high complexity projects. In my day to day with "builds", review, project design and tasks either as a programmer or project manager of three to thirty programmers, I see in the publications and checks of Google and Apple, but concerned about testing what I use in the project or what your project aims at, generating effort in my point of view unnecessary, cases to learning, but most in this topic specific unnecessary explain why used an acceleration framework that you master to a host that will be coupled to work on a platform "store", because and why to be explained the person without need to know, if you are following the rules of that platform " store".  

Look at how the dominant operating systems emerged, such as Android and iOS whatever versions they were derived from UNIX!  This of the old and good UNIX .... this is little talked about because we live a generation that seems not to like to understand why there is such a thing!  And the old, good and stable UNIX generated opportunity for existence of Android and iOS and ai who can and understands software engineering and computer programming, it is normal to reuse and use what has best practiced in an older project and bring to the world that you want functional and consequently market, programmers, good programmers do this time use stable accelerators...

Who is marketing, who is psychology, do not understand and will have different views... a few will use the benefit ...

<<portuguez-br>>

Em nossos projetos e buscas por melhor produção, busco ferramentas que podem me apoiar a desenhar e implementar projetos de forma mais ágil e produtiva, com lastro de onde, como foi feito e saber o que reflete no projeto que eu desenvolvo para os clientes, isto uso a palavra ágil e não rápido. Isto pois a rapidez pode gerar para o tema de implementar, engenharia de software, programação de um software instabilidades, insegurança, a palavra ágil reserva dentro dela a rapidez de um método que prática ficou tão ágil que você ou sua equipe executa rápido, isto adquire com treino, vivência e insucessos para resultar em práticas boas, de bons resultados e de entregas ágeis sejam projetos de baixa, média e ou alta complexidade. Em meu dia a dia com "builds", revisão, desenho de projeto e tarefas seja como programador ou gerente de projeto de uns três a trinta programadores, vejo na publicações e verificações da Google e da Apple, mas preocupada em testar o que uso no projeto ou o que o seu projeto tem como objetivo, gerando esforço em meu ponto de visão desnecessário, casos até aprendizado, mas a maioria neste tema especifico desnecessário explicar porque usou uma framework de aceleração que você domina para um hospedeiro do que estará acoplado a funcionar numa plataforma "store", porque e porquês a serem explicados a pessoa sem necessidade de saber, se você esta seguindo as regras daquela plataforma " store".  Olhemos como os sistemas operacionais dominantes surgiram, como o Android e o iOS sejam quais forem as versões, derivaram do UNIX !  Isto do velho e bom UNIX .... isto é pouco falado pois vivemos uma geração que parece que não gosta de entender o porque existe tal feito !  E o velho, bom e estável UNIX gerou oportunidade para existência do Android e do iOS e ai quem pode e entende de engenharia de software e programação de computadores, é normal reusar e usar o que tem de melhor praticado num projeto mais antigo e trazer ao mundo que você deseja funcional e consequentemente de mercado,  programadores , bons programadores fazem isso o tempo usam aceleradores estáveis...

Quem é de marketing , quem é da psicologia, não entendem e vão ter visões diferentes... alguns poucos vão usar o beneficio ...

<<portuguez-br>>