“Collaboration” is always a popular enterprise buzzword, but getting stakeholders from different disciplines and styles to work together rarely comes without conflict. 

Looking specifically at data scientists and data analysts, the former are trained in research and hypotheses. They create complex queries with big data algorithms that might take numerous iterations to yield results. Data analysts, by contrast, come from a world of highly structured data work, querying data from structured databases, which deliver rapid results. 

While some friction may be natural when these two groups work together, companies need to help data scientists and data analysts overcome any potential issues that might hinder productivity. Read on for three tips to support greater collaboration between these two important data groups. 

  1. Foster a Collaborative Culture that Addresses Both People and Tools 

Companies can build a sense of teamwork and collaboration between data scientists and data analysts in numerous ways. These efforts are often aligned with broader initiatives to foster a data-driven culture—check out this previous APEX of Innovation post for more on how to do that effectively. 

From a tools perspective, companies should strive to consolidate all data into a single data repository. As part of this process, data scientists, data analysts, and the database administrator must collaborate to standardize data definitions and decide which datasets to combine to build this standard platform. 

  1. Consider Building a Corporate Center of Excellence 

Data science is changing quickly with a growing set of frameworks and algorithms that support statistical analysis, supervised learning, deep learning using neural networks, and more. In this environment, the CoE would ensure thorough communication, the development of best practices, and that data teams are all working toward a shared goal. 

  1. Tie the Unification Effort Back to the Business 

The final consideration for increasing collaboration between data scientists and data analysts is to link the effort back to broader cultural goals. As mentioned above, there are similarities between this unification effort and what the company may be doing to foster a greater data-driven mentality throughout the organization. Companies should emphasize that greater collaboration between these two key groups creates a stronger and more integrated culture while also accelerating data-driven decision-making at every level of the organization. 

You can read more about the above considerations in this TechRepublic article.