How can you share data engineering goals with data scientists?

Imagine a situation, two cousins working together! There will be communication gaps and goal mis-alignment ... Leading to arguments, escalations and sadly dis-trust. It can be avoided, YES! For Data Product, end user perspective is useful to know i.e. Data Citizen (DS). The timely, granular data availability is core demand. For DE team, its should be part of KPI. Be mindful, DE team serves multiple Data Consumers like BI/Reporting Team, Auditing team etc. DE team will be processing much more data at higher frequencies than DS need. So Stakeholders should help prioritizing DE team goal. DE should never become bottleneck! If DE is bottleneck, I recommend Data Mesh with multiple Data Product teams, one of them serving Data Science use case.

To effectively share data engineering goals with data scientists, it is essential to establish communication channels and foster collaboration between the teams. Start by organizing regular meetings , workshops where data engineers can present their goals and objectives to data scientists,articulate the data engineering priorities, such as improving data quality, data integration, or infrastructure scalability. Discuss the technical aspects of data engineering projects and their potential impact on data scientists' work. Encourage data scientists to provide input and insights regarding their specific needs and requirements. Foster an environment of open dialogue and encourage data scientists to share their challenges or pain points

Ask data scientists to explain an analysis end-to-end. This helps data engineers grasp the full context and requirements. Review past data science projects and pain points together. Look for patterns and opportunities. Encourage data scientists to provide specific examples of how better data infrastructure would help them. Don't rely on general statements.

Like most cross-team collaboration, understanding starts with empathy. Instead of leading with your own assumptions, ask the DS for their priorities and concerns. Once the initial consensus has been reached, I've had great success spinning up temporary Slack channels to facilitate concise, frequent async updates. I also find the searchability can pay dividends on future projects, similar in nature.

When it comes to sharing data engineering goals with data scientists, clear communication is key. Start by having open conversations about what we're aiming for and why it matters. Make sure they see how crucial data engineering is for their work—it's all about giving them clean, structured data to analyze. Let's talk about how hitting these goals benefits both sides, like making data easier to work with and improving the quality of our analyses. We'll set up some metrics and milestones together to track our progress and keep each other accountable. And of course, we'll keep the conversation going, tweaking things as needed based on their feedback. With this approach, we'll be on the same page and working together toward success.

Be transparent about constraints like budgets, timelines, legacy systems, and technical debt. Explain how these limit options. Discuss workload priorities and tradeoffs - data scientists may need to adjust expectations based on resource availability. Establish an agreement on division of responsibilities between data engineering and data science. Document this clearly.

Define the Data Engineering Scope with attention to detail: After gaining a comprehensive understanding of the data science goals, it is imperative to meticulously outline the scope of your data engineering responsibilities and effectively convey it to your data scientist collaborators. Within this scope, explicitly detail the data sources, formats, quality benchmarks, availability windows, and security measures that fall under your purview. Furthermore, articulate the specifics of your data pipelines, platforms, tools, and adherence to industry standards and best practices. It is crucial that this defined scope is not only realistic, feasible, and measurable but also aligns seamlessly with the fundamental principles of data engineering.

Defining the data engineering scope is akin to setting the foundation for a successful data-driven ecosystem. It's the blueprint that aligns data sources, quality standards, and technological tools with the overarching data science goals. This clarity not only streamlines collaboration but also safeguards against potential pitfalls. A well-defined scope acts as a compass, guiding data engineers and scientists towards shared objectives while minimizing ambiguity and maximizing efficiency.

More than defining the scope of each role, it is important that communication will always be a pillar, the exchange of ideas regardless of whether you have the technical skill, whether programming an ETL or making a model, when talking to each other we can generate ideas or concepts that from another perspective can solve and generate great efficiency in the delivery of a product.

Provide visibility into data engineering backlogs and project planning cycles. Make status transparent. Give data scientists an opportunity to review roadmaps and influence prioritization of tasks. Frame roadmaps around delivering data scientist value vs. technical implementation details.

The roadmap should emphasize the value proposition of your data engineering deliverables, shedding light on how they enhance data quality, reliability, and usability for the data scientists. By sharing this roadmap, you not only keep your data science colleagues well-informed but also actively engage them in your data engineering journey, ensuring that they are aware of your progress and any potential challenges that may arise along the way.

Involve Data Scientists in the roadmaps and the planning of the projects to provide visibility into upcoming projects, initiatives, milestones and deliverables. Collaborate with them in the requirements gathering process. This helps Data Scientists to understand the context and dependencies of their work within the broader data engineering landscape.

The roadmap helps us to technically share the entire process that we will carry out. It is always important to have clarity and be attentive to the observations that the team can give us since many depend on planning and delivery on time, additionally. We must always have the flexibility to make changes so that we do not generate friction for the team and our clients.

To effectively share data engineering goals with data scientists. Collaboration and transparency it's essential. Begin by understanding the data science objectives, then define the scope of data engineering, identifying necessary datasets, relevant data sources, and data cleaning, transformation, and integration requirements. Share a detailed roadmap , seeking feedback from data scientists to incorporate their insights. Document all engineering activities, maintaining transparency and traceability throughout the project. Encourage ongoing collaboration between data engineering and data science teams through regular status meetings, updates sharing, and adjustments to the roadmap as needed to ensure efficient goal attainment.

Establish regular touch points for data scientists to provide input on data engineering work. Don't wait for them to offer unprompted feedback. Probe for specifics when data scientists mention pain points. Drill into root causes and don't make assumptions. Incorporate data scientist feedback into data engineering retrospectives and process improvements.

This feedback-seeking process allows you to identify any gaps, issues, or opportunities within your data engineering efforts that may directly impact the work of data scientists. Moreover, it offers the chance to prioritize, adapt, and enhance your data engineering solutions and processes, tailoring them to align seamlessly with the evolving needs and preferences of the data science team.

Sharing a data engineering roadmap is more than just project management; it's about fostering transparency and collaboration. This roadmap serves as a communication tool that not only informs data scientists about timelines and dependencies but also illustrates the tangible value data engineering delivers. By involving data scientists in the journey, you create a sense of shared ownership and help them see how improved data quality and reliability directly align with their goals. It's a strategic move that empowers both teams to work cohesively towards a common objective, promoting a data-centric culture within the organization.

It is always important that we have constant feedback and not wait until the data is delivered to consume scientific pores, it is of great value to iterate, generate those spaces that do not have to be masterful, but effective for any project to generate the expected value

Data scientists often focus on model development and specific data requirements, typically sourcing data from reliable data warehouses or data lakes. To streamline this process, it's beneficial to maintain a requirement document that details the following: 1. Each data point requested by the data scientist. 2. Definitions, use cases, metadata, and scope for each data point. This way, engineers know precisely what data to pull and where to find it. The document should also include information about the location and characteristics of the output data. Both data scientists and engineers can refer to this document to ensure everyone is aligned.

By systematically documenting data engineering outputs, you promote self-service data access and analysis for data scientists, ultimately reducing the need for extensive data engineering support and maintenance. Furthermore, it fosters a culture of transparency and trust, where data scientists can confidently leverage the data engineering outputs to derive valuable insights and drive data-driven decision-making, thereby enhancing the overall collaborative synergy between data engineers and data scientists.

Documenting data engineering outputs is like providing a well-structured map for data scientists in their journey. It's not just about sharing data; it's about creating a comprehensive knowledge base. This documentation not only helps data scientists understand the data's structure and origins but also ensures transparency and trustworthiness. It's a bridge that minimizes ambiguity, accelerates analysis, and reduces the need for constant data engineering support. It showcases the commitment to data quality and collaboration, strengthening the foundation of data-driven decision-making in the organization.

Data engineering and data science teams cannot be seen as isolated teams, but rather interconnected teams. It is important to align the objectives of data engineering with those of data science under a practical and efficient data architecture, which is aligned to the strategic objectives of the business. Data engineering teams are providers of quality information to data science teams that require processed data to develop models and create studies that help the business meet its objectives.

-Engage with data scientists to understand their objectives, such as reducing churn rates and identifying factors influencing customer attrition. -Determine what data sources are needed, which databases to query, and how to extract relevant customer data. Specify data transformation steps, such as feature engineering or data aggregation. -Create a roadmap outlining when data pipelines for customer data will be developed, ensuring data scientists know the timeline and milestones. -Seek feedback from data scientists to ensure that the chosen data sources and transformation steps align with their modeling needs. -Document the data pipelines and transformation processes, making it clear to data scientists how the data is prepared for analysis.

I like to see where the concept of Data Mesh is going. I try to see the concept of data mesh by giving data scientists the ability to "own" their own data. Data can be shared across the organization for useful insights an also be used for different purposes, avoiding the data being siloed into a central management sector.