Succeeding in data analytics today requires more than excelling in data science alone. In fact, it’s often the last phases of a data analytics project, including communications and employee adoption, that ultimately determine its success or failure. Here at the APEX of Innovation, we’ve offered tips on overcoming soft skills challenges to help achieve better results, including building the right team to innovate, applying a product management approach to digital initiatives, and avoiding common pitfalls to data analytics adoption.

According to a Harvard Business Review (HBR) article on the “art of persuasion” and data analytics success, “Despite heavy investments to acquire talented data scientists and take advantage of the analytics boom, many companies have been disappointed in the results.”The reason: poor communications and a lack of the right talents to successfully bring projects to completion. To help companies get to the root of the issue and start to make a change, the article offers four steps for building a better data science operation, including:

1. Define Talents, Not Team Members. According to the article, “A talent is not a person; it’s a skill that one or more people possess.” To that end, the article encourages data leaders to define the talents their company needs to be successful, rather than the roles. These talents can include project management, data wrangling, data analysis, subject expertise, design, and storytelling. By taking this approach, data leaders can stay more agile and flexible should objectives change or new talent needs emerge.

2. Hire to Create a Portfolio of Necessary Talents. Look beyond traditional resource models to ensure that you have the talent you need. This can include grouping existing talents that go together to help better accomplish something, such as design and storytelling, as well as looking externally to engage new talents as contractors or outsourced support. This approach enables companies to bridge the gap between the people doing the data science and the people communicating its value.

3. Expose Team Members to Talents They Don’t Have. Success requires team members to understand the talents and contributions of others. To do this, data leaders can offer training in areas where team members need to know the basics of certain subjects, such as data wrangling. According to the article, “Neither must become experts in their counterparts’ field—they just need to learn enough to appreciate each other.” Other methods to support talent awareness include stakeholder Q&A sessions, talent workshops, and cross-talent pollination, such as inviting marketing to technical meetings.

4. Structure Projects Around Talents. With talents defined and in place, data leaders can move on to defining projects and managing the execution that will leverage them. According to the article, “Strong project management skills and experience in agile methodologies will help in planning the configuration and reconfiguration of talents, marshaling resources as needed, and keeping schedules from overwhelming any part of the process.”

If you’d like to learn more, including how to succeed through “the last mile” to project completion, read the complete HBR article.