The Integration Challenge: data engineering vs data analysis

22 March 2024 | 4 minutes of reading time

In this second article from a series of three, we dive deep into the integration challenge of engineering and analysis. In the previous article the roles in a data team have been set out.

While each of the roles brings its unique strengths to the table, the synergy between them is not automatic. Let's explore the challenges that arise when integrating these diverse functions into a cohesive team.

Integrating distinct functions like data engineering and analysis into an organization presents unique challenges. Companies often place the data department in either IT or under marketing. The organizational placement of the data team significantly impacts the resources, tools, and methodologies at their disposal.

In larger organizations or enterprises these data departments are often also subdivided into distinct teams for data engineering (or data integration), data analysis and/or data science. Or depending on the agile approach, into chapters, guilds and squads.

A big problem is when technical and business sides of a company don't understand each other's expectations and goals. Misunderstandings can happen between departments like marketing and IT, between teams in a data department or between managers and data professionals, causing issues and confusion.

Marketing and Data Engineering Misunderstandings

• Process Rigidity: Marketing managers might find the data engineering processes too rigid or slow. They don't understand the necessity for strict data governance, security practices, and the complexity of setting up scalable data pipelines.
• Immediate Data Access: There can be frustration from marketing because of perceived delays in accessing data or implementing changes to data systems. They may not recognize the engineering requirements for ensuring data integrity and system stability.

IT and Data Analysts Misunderstandings

• Tool Selection: IT managers might view data analysts' choice of specific analytical or BI tools as lacking sophistication. They may not fully appreciate the tools' capabilities for data visualization and exploration that are vital for analysts.
• Structured vs. Flexible Workflows: IT departments may find data analysts' flexible approach in the use of tools like Git or Jira surprising. Analysts' work demands a more exploratory approach, allowing them to adapt as they delve into data analysis. This need for flexibility, vital for uncovering insights, may differ from IT's more structured methods.
• Data Practices: Confusion can occur when data analysts handle unstructured data without following IT's strict data governance and security rules. This perception overlooks the exploratory nature of analysts' work, which necessitates a different approach to data management.

General Areas of Misunderstanding

• Scope of Work: There can be a lack of understanding about the scope of work each role encompasses. This leads to unrealistic expectations or underappreciation of the complexities involved in data tasks.
• Value of Insights: Insights from data analysts are crucial for making decisions. If you do not actively participate in the process, you may not understand its importance.
• Technical Language Barrier: The use of technical jargon or data-specific language can create communication barriers, hindering effective collaboration and understanding across departments.

Strategies for Enhanced Integration:

To address these integration challenges, organizations are encouraged to:

• Facilitate Open Dialogue: Regularly scheduled meetings between departments can help clarify expectations and demystify the data team's processes.
• Promote Cross-Functional Education: Workshops and shared learning sessions can enhance mutual understanding of each department's methodologies and tools.
• Implement Clear Communication Channels: Establishing straightforward communication channels can alleviate frustrations arising from misaligned expectations and improve project coordination.
• Encourage Collaborative Projects: Joint projects that require input from marketing, IT, and the data team can serve as platforms for enhancing teamwork.

Businesses can create a respectful and collaborative environment. You can do this by addressing areas of confusion and bridging the gap between technical processes and business objectives. This ensures the successful integration of data-focused strategies across the organization.

A Proposed Solution: Cultivating an Integrated yet Independent Data Team

As we propose a solution to bridge the gaps identified, please remember the underlying theme of our discussion. That is the harmonious balance between deep specialization and the enriching potential of interdisciplinary collaboration.

To solve problems in the data team and other departments, we recommend forming a unified but independent data team. This team acts as a vital conduit, bridging IT and business units, thereby harmonizing technical capabilities with business objectives. This model helps data professionals focus on their expertise while staying in line with the organization's overall goals.

Leadership's role in nurturing this environment is paramount. It involves:

• Valuing diverse contributions: recognizing and valuing the unique skills and insights that data engineers, analysts and analytics engineers bring. Understanding that while their methods and approaches may differ, each plays a crucial role in the data lifecycle.
• Encouraging Specialization: Avoiding the trap of seeking "the sheep with five legs"—individuals expected to excel across multiple, varied domains. Instead, promoting deep specialization within each role to foster excellence and innovation.
• Fostering Communication and Understanding: Actively working to bridge any knowledge gaps within the team and between departments. This includes facilitating regular cross-functional meetings and workshops to promote a shared understanding of goals, methodologies, and challenges.
• Implementing Structured Flexibility: While maintaining the team's independence, introduce structured flexibility in workflows. This acknowledges the need for both the organized approach typical of IT and the exploratory, fluid nature of data analysis work. Emphasizing adaptable frameworks that accommodate the unpredictable nature of data work while maintaining coherence and alignment with organizational standards.

A company can create a skilled data team by following these principles. This team will be able to handle internal challenges effectively. Additionally, they will be able to make a significant impact with data-driven strategies.

This method doesn't lessen the knowledge depth, but instead adds to it with a wider view from working across disciplines. Leaders must create a culture that values specialization and teamwork to achieve success by using data effectively.

When forming an in-house data team isn't viable, i-Spark introduces a streamlined solution with its Data Team as a Solution (DTaaS). This service sidesteps the need to individually hire for each data discipline by providing on-demand access to a comprehensive suite of data experts, including engineers, analysts, and scientists. DTaaS acts as an agile, external extension to an organization, enabling swift, cost-effective deployment of data-driven projects without the overhead of maintaining a dedicated internal team. This model offers scalability and flexibility to tackle data challenges efficiently, ensuring organizations can leverage specialized skills precisely when needed, aligning with strategic goals without compromise.

Read more about establishing the unified basis for data teams