How can you ensure that your team meets the data needs of the business?

In 2024 we are living on competitive market not a corporate market, so everyone updating a knowledge day to day life,so you're working on Data Engineer, knowing on what's our data upstream and downstream, then data how will help business and stakeholders, so you can easily moving forward path on your career journey, so everyone know about your work and what's work help on your company business and stakeholders then finally always keep updating.

Tenha sempre em mente que uma entrega de valor para a organiza??o passa por um entendimento profundo do negócio para alcan?armos os melhores resultados e este é o primeiro e principal passo que deve ser realizado. Vis?o e estratégia s?o fundamentais para obter estes resultados e devem ser compartilhados de forma clara e objetiva com o time. é necessário conectar todos com o propósito da entrega e gera??o de valor para a organiza??o e áreas de negócio.

Ideally, business context gets extracted, chewed up and fed to data teams by BA or PO, but that is not always how it goes. One thing you should be cognizant of, is that business people have their own biases and previous experiences. Oftentimes, they will subconsciously try to "dumb it down" to tech people, putting their wants into the context of dashboards or reports, because this is what they know. It is in your best interest to extract the actual requirement. What was requested as a dashboard can may very well be an automated notification, and what was requested as an automated notification - could really be a data quality issue to be automatically raised to responsible parties. Be sure to get to the core of the request.

In addition to understanding business goals, consider conducting stakeholder interviews to gather insights and refine requirements. Collaborate closely with business analysts and domain experts to ensure a nuanced understanding of the data's significance. Document clear data requirements and establish key performance indicators (KPIs) that directly align with business objectives. Explore potential challenges and constraints related to data quality, privacy, and compliance. Establishing a strong rapport with end-users facilitates ongoing communication and feedback loops, fostering an iterative and adaptive approach to data engineering that consistently meets evolving business needs.

In organizations with a top-down culture, grasping business needs may be more straightforward. Conversely, discerning the business context can be challenging in a bottom-up culture, especially within larger companies. To enhance understanding in such cases, individual contributors should collaborate closely with their managers. This collaborative effort is vital for identifying and validating data issues within the company, ensuring the data produced aligns effectively with the business requirements.

Creating insightful data comes as a second pillar for delivering accurate data and information to the stakeholders. Accuracy depends on the clear idea of the deliverables. And hence, first pillar includes defining the clear roles and responsibilities of the team working on the deliverables. Choosing the right team and defining the right roles of each member helps delivering the best of everything. Communicating well on the idea and the process to be followed, will surely help the team on the timeline of deliverables, planning the capacity well and avoid any last minute conflicts. This will also help individuals work best on their hands on tools, whether it be data engineering or data analytics and deliver the best of their track.

Some times project's are failure in freelancing and startup even though MNC company because team members are facing some Miss conversation in project explain and what's our role and your role in project, so everyone at team clearly explain your role of work and deadline of project then what's problem on project.finally information is valuable.

Assigning specific responsibilities to each team member helps prevent the misuse of available data. Often, individuals inadvertently duplicate processes that have already been completed right after data retrieval. This not only leads to a significant drain on processing power in big data scenarios but also poses a risk of data loss. This ensures that the team stays aligned with the business's data requirements without realizing it. Detecting and resolving issues becomes challenging because each team member believes their actions align with the business objectives, making debugging a difficult task.

Tailor roles for efficiency. For instance, In oil and gas mechanical engineering, assign a data analyst to optimize equipment performance and a data engineer to integrate predictive maintenance systems. This ensures collaboration, with each member contributing uniquely to meet the specific data needs, enhancing overall operational excellence.

Define clear roles and responsibilities is the most important step in a project. It’s the key to know who will be mobilized on what task, based on each person's expertise. And to guarantee a perfect collaboration, a good communication and no overlap in a project or a team initiative. The best way to define them is to use a RACI matrix: with this matrix, it’s straightforward to identify what team/person is responsible, accountable, contributor and informed on what.

Many employees are working in company only role of work they didn't help your career journey because we are living on competitive market not a corporate market, so everyone knowing our work and what the contribution our work on project and how I can help my company and stakeholders, they only help your career journey, so everytime keep updating our life.

In addition to adopting all measures mentioned, fostering a collaborative culture within the data engineering team by promoting regular stand-up meetings, encouraging knowledge sharing, and facilitating cross-functional collaboration with data scientists and analysts is paramount. Continuous integration and deployment practices can streamline the testing and deployment of code changes, enhancing efficiency and reducing the time-to-value for data solutions. Embrace automation for routine tasks, enabling the team to focus on higher-value activities. Regularly assess and refine the project roadmap to adapt to evolving business priorities and technological advancements.

Embrace adaptability. In oil and gas, iterate rapidly based on real-time equipment performance data. Continuous feedback loops allow prompt adjustments, ensuring mechanical systems operate optimally, aligning with safety standards, and responding effectively to evolving efficiency requirements.

A picture says more than thousand words. In my experience visuals like mockups and process diagrams aren't just tools, they're communication lifelines. I keep revisiting these assets in the sprints. It's key to treat these visuals as evolving documents. Are your mockups reflecting current user stories? How frequently do you update mockups and process diagrams to mirror feedback? Engage in a constant 'ping-pong' with key users. Their satisfaction is a moving target – they evolve with the discussion. Are your visuals keeping pace?

The definition and the implementation of best practices and standards - and I would like to add common processes - is especially essential when a department has a decentralized organization and when there is a lack of maturity. Here implement them ensure every team defines and deploys common data solutions, guarantee of efficiency and scalability.

One should also consider regularly conducting code reviews to maintain high coding standards, identify potential issues, and promote knowledge sharing within the team. Integrate metadata management practices to enhance data lineage, traceability, and understanding across the entire data lifecycle. Foster a culture of continuous learning and professional development within the data engineering team, encouraging members to stay abreast of industry trends and advancements. Implement automated testing for data quality and performance, and regularly update best practices to align with evolving technologies and business requirements.

?? Documents are reflect of how data need of business was understood from technical view by data engineering team: ?? How structure of content. ?? Define the scope of purpose. ?? Fill it with right stakeholder. ?? How use it with business and IT team. The more standardized the documents are, the easier the above points are.

Choose industry-relevant tools. In oil and gas mechanical engineering, use advanced sensors and IoT devices for real-time data collection. Implement a robust SCADA system for monitoring and control. These technologies empower the team to proactively address issues, ensuring equipment reliability and safety while meeting dynamic data needs.

As Cloud Providers AWS, Azure e Google Cloud disponibilizam excelentes servi?os gerenciáveis como o AWS Glue, Azure Data Factory, Azure Synapse Pipelines e Google Cloud Data Fusion que auxiliam bastante todas as principais atividades do Engenheiro de Dados. Servi?os gerenciados, serverless, seguros e totalmente integrados. A escolha das ferramentas e solu??es devem passar pela análise criteriosa da institui??o e identifica??o dos pontos de maior aderência e sinergia. Cada projeto oferece oportunidades e desafios diferentes e isso também é determinante para a escolha da melhor solu??o.

Understanding the business and your adaptation to your company's infrastructure is key, for this reason make understand at the team how important it is to choose the right tool to the right job is fundamental, lest you be killing flies ?? with a rocket ?? or climb the everest with flip-flaps .

A jornada na estrutura??o e acelera??o da Cultura Data Driven é o primeiro passo para fomentar a inova??o dentro das organiza??es. Todas as áreas de negócio da corpora??o e principalmente os profissionais de dados como os Engenheiros, Cientistas e Analistas de Dados precisam estar engajados com este objetivo estratégico para o sucesso nessa jornada.

??To foster a data driver culture; data and business team must need to understand how process generate data: ?? What terms use in process to explain it? ?? What business area is owner of process? ?? How processes are related each other with others business area? ?? What are dependencies, inputs and output of process? ?? What is the flow of activities inside process? ?? How well data engineering team understand the processes, better they will meet the data needs of the business from a data solution.

Data governance can be useful when working on a data project. As it lays the necessary guidelines and answers the hows and whats.

Database security is now the most important aspect. You need to understand how critical the data is and how much confidentiality is required. Ensure the security is planned beforehand with a good reporting mechanism to identify the security changes.

- ??Start with 'do we know what our business truly needs?' - ???Effective Communication between data and business units (IMPORTANT) - ?? Promote flexibility in terms of technologies and methodologies adaptation - ??Maintaining Data Integrity Checklists - ??Emphasize on User Centric Design Thinking

Ever faced the chaos of missing documentation in projects? It’s like navigating without a map. Documentation isn’t just paperwork; it’s the blueprint of project history, driving logic and decision-making. How do you ensure knowledge isn’t lost when team members move on? Are you documenting your creation, decisions, and changes systematically? Think about the risks of inadequate documentation – misunderstood or completely unknown objectives, repeated mistakes, lost knowledge. Documentation is the backbone for consistency and clarity. How are you safeguarding your project’s legacy?

To meet business data needs: 1. Understand requirements through stakeholder collaboration. 2. Maintain transparent communication for aligned priorities. 3. Enforce robust data governance policies. 4. Build scalable data infrastructure aligned with business goals. 5. Foster collaboration between data and other departments. 6. Prioritize and plan projects based on business impact. 7. Promote data literacy through training initiatives. 8. Implement agile methodologies for adaptability. 9. Monitor and optimize data systems for efficiency. 10. Establish feedback loops with business users. 11. Stay current with evolving data technologies. 12. Measure key performance indicators for continuous improvement.