How can ERP skills lead to a successful data science career?

ERP (Enterprise Resource Planning) skills can enhance a data science career by providing a strong foundation in understanding organizational data, processes, and business objectives. Proficiency in ERP systems allows data scientists to effectively extract, clean, and analyze enterprise data, making their insights more business-relevant. Additionally, ERP expertise can lead to specialized roles in industries where these systems are prevalent, such as manufacturing or finance. Combining data science with ERP skills creates a unique skill set valued by organizations seeking data-driven decision-making and process optimization.

Vamos a ser sinceros. Con 37 años de experiencia en el sector, creo, que los ERP's actuales se han convertido en aglutinadores de datos que los usuarios no saben interpretar. Lean, WorkFlow, MRP, BI, MES, CNC, automatización y más automatización ...... ¿Qué nos encontramos los profesionales ?. Excels; a miles. Es lo que entienden y saben utillizar la mayoría de usuarios. Luego, hay que integrar estos excels en el ERP para que el ERP de turno los "gestione". El ERP "digiere" estos datos (a veces con indigestión) y los vuelca en alguna herramienta de la que se extraen cientos de informes que no se saben interpretar si es que acaso se los miran. Formar a los usuarios, formar a los lideres, formar a la empresa y los datos cobraran sentido !!

Having all in one place enhances unification, better planning, and synergistic optimization across the organization. With just the click of a button, all you need is brought to your doorstep.

It has been two years since McKinsey coined the term "analytics translator" and identified it as the 'new essential.'Among these analytics translators,we have Type A who transitioned from the analytics domain to the business domain.There are Type B who made the shift from the business domain to analytics.I believe that those who possess a strong understanding of ERP are better suited for the role of Type B.Their ERP experience has undoubtedly equipped them with valuable domain knowledge,enabling them to function effectively as data engineers.This is because they already comprehend the genuine business requirements and the specific areas that need to be connected in order to present visual data to business users facilitating decision-making.

Data science is the study of data to extract meaningful insights for business and having proficiency in the data structure and the business processes that are configured within the ERP systems allows data scientists to effectively extract the right data, clean and analyze it effectively, making their insights more business-relevant.

Yes. Ensuring that data complies with ethical, privacy and security standards is made a lot easier with ERP. ERPs typically come with audit functionalities, which record every change made to the data. I found this feature to be particularly important for financial and healthcare industries where stringent compliance standards exist.

ERP needs qualitative master data. Be it Vendor, Customer, or Material data. A separate Master Data Hub connecting to ERP will help ERP get clean data. Data cleansing tools and data governance policies will enhance data quality.

Uma contribuição valiosa para o texto seria enfatizar como a implementação de sistemas ERP pode melhorar a qualidade e consistência dos dados em toda a organização. Além disso, destacar a importância da qualidade dos dados para análises de ciência de dados, ressaltando como o conhecimento em acessar, extrair, transformar e carregar dados de várias fontes, lidar com problemas de dados e aplicar padrões de governança e segurança é fundamental para garantir a eficácia das operações e análises empresariais.

There is no such thing as pristine quality data coming from the ERP. Not in your company, not in mine, not in anyone's. We must acknowledge that ERP data reflects the company's 'metabolism' or 'behavior,' and we should treat it that way. Every data point inserted into the ERP was carefully designed in that way for some reason. The data scientist's job is to go deeper into the processes and ERP interfaces with professionals and other systems to understand why the data looks the way it does. Complaining about data quality is not a strategy.

The quality of the data is key to AI or any other data science fields. Being an ERP consultant, puts you on the front row of of both the systems. It's because you know the ERP, so you know "the semantics of data"

With inbuilt tools for data analysis, ERP users can drill down and segment data in various ways, such as by department, product line, region, or any other criteria. This segmentation facilitates targeted analysis and decision-making, departmentally and organizationally.

ERP systems are key for process management. Analysis directly in the ERP system can be not so easy and less out of the box and so BI is a better tool for the anaysis.

I come from SAP background. Details about the Spend analysis, accounts receivable, and manufacturing delays can be easily staged into Dataiku or Snowflake which can be used by Python, R, Tableau, etc to detail out analysis in a graphical way.

Data Analysis is auxiliary to ERP Systems. As a main source and consumer of Data, many tools exists to manipulate, prepare and present Data sets for Analysis, mostly based on SQL language. This is the role of Dataviz tools to facilitate analysis and comprehension of Information pointed out.

While ERP involves the [supposedly] smooth integration of the various functions within an organization; those using these software find it difficult to take advantage of the vast trove of data produced by such ERP Systems. It is so, because business users (from executives to end users) do not know the types of data available to them for decision-making. Data Stored in ERP Systems still model the way departments are physically setup (siloed/isolated by functions) and therefore, Analytics tools use similar designs, using architecture such as data marts another canned concept) for reporting. This makes it difficult to do Analytics and getting quality insight because these designs are obstacles in gaining Enterprise-wide view of the data.

ERPs are a well-educated mask for users and other systems to fill relational database tables. The core value proposition of an ERP system is not to model business data; that's the role of data science and BI teams. Ask a salesman about the differences and similarities between two different products, and you hear a lot of different aspects, buying personas, pricing, sales pitches, use cases, etc. But what does the ERP or the DB know about two different products? They can only compare them based on the attributes inserted. Data modeling is something you have to work actively on and not simply query.

It falls back to the very basic of ERP, the business processes. As the selection of algorithms depends heavily on the context and meaning of the data, it becomes rather easier for the ERP consultants to choose the right algorithm for the modeling phase.

ERP systems facilitate data communication within organizations through centralized data repositories, data integration, real-time updates, automated workflows and notifications, reporting and analytics capabilities, collaboration tools, and access control mechanisms. By leveraging ERP systems, organizations can enhance communication efficiency, improve data transparency, foster collaboration, and support data-driven decision-making across various departments and functions

4. Business Acumen: ERP professionals typically have a strong understanding of business processes. This knowledge helps data scientists in framing problems and solutions in a business context, making their work more relevant and impactful. 5. Project Management: ERP implementations often involve project management skills. Data scientists often work on complex projects, and project management skills gained from ERP can be transferable. 6. Problem-Solving: Troubleshooting and problem-solving skills developed while managing and maintaining ERP systems can be applied to data science projects when encountering challenges in data analysis or model building.

In thise phase we complete the circle. We started with business data and in the end, we are providing again business insights (which are again business data) to the business people. Knowing ERP makes this task much easier.

Os sistemas ERP desempenham um papel fundamental ao facilitar o compartilhamento e a integração de dados em toda a organização, promovendo uma colaboração mais eficaz entre cientistas de dados e analistas. Eles também permitem colaborações em plataformas e ferramentas comuns, fortalecendo a coleta e análise de dados de maneira mais integrada e colaborativa.

When you work in a complex environment wherein data is being received or transmitted to various 3rd party systems to-and-fro from your ERP system, you come across various challenges while defining integration strategy spread over different platforms. You will have to spend time in decoding file formats, data sources and accordingly finalize the overall architecture of integrations. This skill of collaborating with various teams owning other applications and developing an overall knowledge of business processes and transactional data plays a crucial role in your Data science journey. You get to understand the holistic impact of upstream and downstream systems which further help you in better representation of data.

My day to day functions are all about collaboration. ERP creates that platform for ease of knowledge sharing and problem solving across teams. Rarely is something an isolated issue. ERP helps to test theories and fill in the “why’s”and “how comes” of complex questions.

ERP systems implementations often involve collaboration with cross-functional teams, communication skills, and the ability to translate business requirements into technical solutions. These soft skills are valuable in data science, especially when you need to work closely with business teams.

Being an ERP professional I am familiar with data management, including databases and flows, crucial for handling large datasets. ERP integration of diverse business department data fosters skills in consolidating heterogeneous data sources, vital in data science. ERP has helped me understand business processes, aligning analyses with organizational goals effectively. Analytical skills honed through ERP reporting tools directly apply to data science tasks like generating insights. ERP skills equip data scientists with the ability to manage, analyze, and derive insights from data within a business context, laying a solid foundation for a successful data science career.

ERP, Most difficult if concepts of digitilization to digitization not realized at the sametime very simple if following is clearly understood and practiced. 1 - Data Collection Engine 2 - Business processes fully integrated. 3 - Reporting - trace and followup for all exceptions as well as all results with required comparatives. May it be a flat report, Data Analytics, Dash Boards. Often ERPs are misrepresented but reasons are ignored. Meaning the business responsible when they just use the term Digitilazation to Digitization its a buzz word, once they understand, they will realise the benefits of ERP in use.

ERP skills are a valuable asset for data scientists and analysts, as they provide a deep understanding of business workflows, problem-solving skills, experience with massive datasets, and data analysis and report building skills. Furthermore, ERP skills can help data scientists and analysts to develop more specialised skills, such as ERP data analysis, ERP predictive modelling, and ERP process optimisation. Overall, ERP skills can give data scientists and analysts a competitive edge in the job market and help them to advance their careers.

To leverage your ERP skills for a successful data science career, consider further developing your technical skills in areas like machine learning, and data visualization.

Process knowledge is top important to understand the business and deliver real value as a Data Scientist. If you know your ERP, you understand data and process flow throughout the company.

For a career in data science, you will need to understand in depth how and why database tables are designed and structured in the way they are. For example, how does a RDBMS structure data to provide a multi-level bill of materials with common sub assembly parts - and importantly prevent BOM structure loops from data entry errors. Then, understand what data / tables etc are needed to perform key MRP/ERP functions; e.g. how does MRP know how to sequence its supply/demand calculations. Try modelling an apparently simple business process like receiving some raw material. What tables are updated and what business flows are triggered.