Data Science Skills Vital for the Next Generation: Learn These 4 Crucial Abilities
Data science plays a crucial role in identifying and resolving problems that aren't immediately apparent, offering valuable insights affecting strategy and operations. James J. Corbett's words, "Only those who have the patience to do simple things perfectly ever acquire the skill to do difficult things easily," resonate in this context, as data scientists must develop problem-solving skills.
One example that illustrates the importance of thoughtful data analysis is a hotel's check-in process, which, at first glance, appeared flawed due to guest complaints. By analyzing customer surveys' texts, data scientists discovered that problems with the hotel itself, such as Wi-Fi, keys, and cleanliness, were the root cause of frustration. Guests didn't necessarily complain about checking in, but the recent interaction with the hotel was what they associated with their unsatisfactory experience.
The significance of lateral thinking is evident in this situation. Data professionals must be able to look beyond the obvious factors and consider the broader context of the guest experience to identify the true root cause. Focusing solely on immediate problems can prevent data leaders from recognizing bigger opportunities for business improvement and fostering a comprehensive approach that maximizes the value they bring to an organization.
In order to solve problems more effectively, data leaders must define their scope, set achievable success metrics, identify data gaps, and develop actionable insights. Four critical questions can help guide the problem-solving process:
- Problem Definition: Break down complex problems into manageable parts and define the core issue and its key components.
- Success Metrics: Define clear, measurable success criteria to evaluate the impact of any proposed solution.
- Data Inventory: Identify the data needed to solve the problem and address data gaps.
- Actionable Insights: Determine how the data analysis will translate into concrete actions and decisions.
Open communication and feedback are essential throughout the problem-solving process. Data analysts should not work alone, but rather engage business teams at every stage, seeking feedback and building trust to ensure buy-in for the final deliverables. Effective problem-shepherding fosters open communication and feedback loops, and it facilitates a collaborative journey rather than a final presentation.
Lastly, the increasing integration of data and analytics into business necessitates a shift towards collaboration and communication for data teams. By excelling in broader roles, including problem identification, scope definition, analytical process management, and results communication, effective data leaders and their teams can provide valuable contributions to organizations.
Source: 4 Skills the Next Generation of Data Scientists Needs to Develop by Joel Shapiro, HBR 2023/09
Data science, with its emphasis on machine learning and problem-solving skills, plays a pivotal role in the education-and-self-development landscape, particularly in the realm of learning. As partnerships between data science and various industries grow, so does the demand for broad-spectrum data professionals. To excel in this field, these professionals must be adept at problem identification, scope definition, and analytical process management, while effectively communicating results. This is crucial in leveraging data to maximize the value brought to an organization, as demonstrated by the hotel check-in process example, where data analysis beyond the obvious factors led to significant business improvement.
