Connectors play a critical role in bridging the organizational gaps that hinder the success of data science projects, offering a solution to the slow progress and high costs often associated with these initiatives.
Data science projects have become increasingly important for organizations looking to leverage data, analytics, and AI to enhance decision-making and operations. However, many of these projects fail to deliver the desired outcomes, while others progress at a sluggish pace. The root cause of these challenges lies in the organizational gaps that exist between teams, hindering the development, application, and scaling of data science projects. To address this issue, a new type of role has emerged – the connector. These connectors serve as the bridge between different departments, facilitating collaboration and ensuring the smooth execution of data science initiatives.
Closing the Gaps With Connectors:
Organizational gaps can be closed by filling them with people who can act as connectors. These individuals are responsible for facilitating communication and collaboration between different teams and departments. While various roles have been assigned to bridge these gaps in the past, such as systems analysts and business analysts, the role of connectors in data science projects is unique. They not only address the gaps between business and technical departments but also handle the specific challenges that arise in data science initiatives.
Defining the data science project process and identifying the people involved:
To effectively bridge the organizational gaps, it is crucial to define the data science project process and identify the key individuals involved at each stage. This includes senior leaders, middle managers, business domain experts, IT professionals, external software providers, process analysts, trainers, and change professionals. By involving the right people at the right time, organizations can ensure that all aspects of the project are considered and executed effectively.
Evaluating where connectors can help:
Connectors play a vital role in translating between business and technical teams, dealing with data quality issues, communicating requirements and progress, and keeping track of the overall goal of deployment and organizational change. They help align the goals and expectations of line managers and data scientists, ensuring a smoother collaboration throughout the project.
Clarifying specific roles for connectors and putting them in place:
Once the gaps have been identified, it is essential to clarify the specific roles for connectors and assign them accordingly. This can include data product managers, data ambassadors, solution design consultants, or any other role that fits the organization’s needs. Additionally, it is recommended to have connectors at both the project level and the program level to ensure effective communication and coordination between data science and the rest of the business.
What Companies Are Trying Now:
Companies are experimenting with various connector roles to bridge the organizational gaps in data science projects. For example, Regions Bank has implemented data product partners who function as both data product managers and partners with the business. Gulf Bank in Kuwait has created a network of data ambassadors, while the Federal Statistical Office of Switzerland employs a “data science bridge” to coordinate its statistical functions. These connector roles have proven to be successful in improving collaboration and project outcomes.
Connector Management Challenges: Five Questions to Ask:
While connectors offer significant benefits, managing them can present challenges. Companies need to consider questions such as where connectors should sit organizationally, how to attract and retain them, what the connector career path looks like, whether multiple types of connectors are needed, and what specific responsibilities connectors should have. Answering these questions will help organizations effectively manage their connector roles and ensure their success.
Connectors play a crucial role in bridging the organizational gaps that hinder the success of data science projects. By facilitating communication, collaboration, and understanding between different teams and departments, connectors enable the smooth execution of data science initiatives. Companies that have implemented connector roles have seen increased deployment percentages and improved business performance. As data science continues to gain importance, the role of connectors will become even more critical in driving successful outcomes.