How a new type of role can improve the success and efficiency of data science projects by bridging organizational gaps.
Data science has become a crucial tool for organizations looking to improve their decision-making and operations. However, many data science projects fail or progress slowly due to organizational gaps between teams. These gaps hinder the development, application, and scaling of data science projects. To address this issue, a new type of role, called connectors, is needed to bridge the organizational gaps and ensure the success of data science initiatives.
1: The Root Causes of Organizational Gaps in Data Science Projects
Data science projects face three main root causes of organizational gaps. Firstly, data science has often been added as an afterthought to the organizational chart, rather than being integrated from the beginning. Secondly, there is a tension between line managers who seek control and predictability, and data scientists who disrupt traditional processes. Lastly, companies often expect data scientists to handle all aspects of a project, from understanding the business to dealing with data quality issues, which can lead to neglecting essential tasks.
2: Closing Organizational Gaps with Connectors
The most effective way to bridge organizational gaps is by filling them with people in connector roles. These roles have existed in various forms, such as systems analysts and business analysts, but they are now needed specifically to address the gaps in data science projects. Connectors play a crucial role in facilitating communication and understanding between different departments, translating between business and technical teams, and managing the various aspects of a data science project.
3: Three Steps to Implement Connectors
To effectively implement connectors, companies should follow three steps. Firstly, they need to define the data science project process and identify the people involved at each step. This ensures that all relevant stakeholders are included in the decision-making process. Secondly, companies should evaluate where connectors can help the most by identifying the gaps in communication and understanding. Finally, specific roles for connectors should be clarified and put in place, both at the project level and at the program level to address the tension between line managers and data science professionals.
4: Examples of Successful Connector Roles
Several organizations have successfully implemented connector roles to bridge organizational gaps in data science projects. Regions Bank uses data product partners who act 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.” These examples demonstrate the effectiveness of connectors in improving collaboration and communication between different teams.
5: Managing Connector Roles
Managing connector roles presents challenges, such as determining where connectors should sit organizationally and how to attract, evaluate, and retain connectors. Companies should also consider the career path for connectors and whether more than one type of connector is needed for a project. Defining the responsibilities of connectors is crucial, as they need to have a holistic view of the project while also understanding and valuing the technical aspects.
Conclusion:
Bridging organizational gaps is essential for the success of data science projects. Connectors play a vital role in facilitating communication, understanding, and collaboration between different teams and departments. By implementing connector roles and following a disciplined approach, companies can improve the efficiency and success rate of their data science initiatives. The institutionalization of connector roles is crucial to ensure that data science projects have a high deployment percentage and significantly improve business performance.
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