Closing the Organizational Gaps in Data Science Projects: The Role of Connectors

How a New Type of Data Professional Can Bridge the Divide

In today’s data-driven world, organizations are increasingly turning to data science, analytics, and AI to enhance decision-making and improve operations. However, despite the potential benefits, many data science projects still face significant challenges and often fail to deliver the desired outcomes. One of the main reasons for this is the organizational gaps that exist between different teams within a company. These gaps hinder the development, application, and scalability of data science projects. To address this issue, a new type of role has emerged – the connector. This article explores the importance of connectors in bridging organizational gaps and managing data science projects effectively.

The Root Causes of Organizational Gaps in Data Science Projects

Data science projects are complex undertakings that require collaboration and coordination across various departments within an organization. However, three root causes often contribute to the organizational gaps that impede the success of these projects.

Firstly, data science is often treated as an add-on to the organizational structure, rather than being integrated into the core functions of the company. This outdated approach fails to recognize the pervasive nature of data science in today’s business landscape.

Secondly, data science projects inherently disrupt day-to-day operations and decision-making processes. While data scientists strive for innovation and improvement, line managers often prioritize control, predictability, and stability. This misalignment creates tension and resistance to change, hindering the progress of data science initiatives.

Thirdly, there is a misconception that data scientists should possess a wide range of skills, including deep business understanding, data management expertise, and the ability to navigate organizational dynamics. This unrealistic expectation often overwhelms data scientists, who may struggle to balance these diverse responsibilities.

The Role of Connectors in Closing Organizational Gaps

To bridge the organizational gaps in data science projects, companies are increasingly relying on connectors – individuals who can bridge the divide between different departments and facilitate effective collaboration. Connectors play a crucial role in overcoming the challenges posed by organizational gaps and ensuring the success of data science initiatives.

Connectors are not limited to a specific job title but can include roles such as systems analysts, business analysts, coverage officers, and systems engineers. These individuals possess a unique combination of technical expertise, business acumen, and interpersonal skills, enabling them to understand the needs and perspectives of both technical and non-technical teams.

Connectors act as intermediaries, translating technical concepts into business terms and vice versa. They facilitate communication, build relationships, and foster collaboration between departments, ensuring that everyone is aligned towards the common goal of leveraging data science for organizational success.

The Benefits of Connectors in Data Science Projects

The presence of connectors in data science projects brings several benefits to organizations. Firstly, connectors help bridge the gap between business and technical teams, ensuring that both sides understand each other’s requirements and constraints. This alignment reduces miscommunication and enhances the efficiency of project execution.

Secondly, connectors facilitate the integration of data science into day-to-day operations by advocating for the adoption of data-driven decision-making processes. They work closely with line managers to address concerns and overcome resistance, enabling a smooth transition towards a data-driven culture.

Thirdly, connectors play a vital role in managing expectations and setting realistic goals for data science projects. By understanding the capabilities and limitations of data science, they can guide stakeholders towards achievable outcomes and avoid unrealistic expectations that may lead to project failure.

Challenges and Considerations in Implementing Connectors

While connectors offer significant value in bridging organizational gaps, their implementation is not without challenges. Identifying individuals with the right skill set, balancing their workload, and ensuring their authority and influence within the organization are crucial factors to consider.

Organizations must also provide adequate support and resources to connectors, including training opportunities, access to relevant data, and a clear mandate to facilitate collaboration. Additionally, fostering a culture of collaboration and data literacy across the organization is essential to maximize the effectiveness of connectors.


In an era where data science is increasingly vital for organizational success, bridging the organizational gaps that hinder the progress of data science projects is crucial. Connectors, with their unique blend of technical expertise and interpersonal skills, play a pivotal role in closing these gaps and ensuring the success of data science initiatives. By facilitating collaboration, aligning goals, and managing expectations, connectors enable organizations to leverage the full potential of data science and drive meaningful outcomes. As organizations continue to embrace data-driven decision-making, the role of connectors will become increasingly indispensable in managing this new kind of data professional.






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