The most important role of the leading data scientist and analytics person is to lay a solid foundation for the rest of the team.
There is a high demand for enterprising data scientist and data professionals to pave the way for data in all types of organizations. Businesses have the FOMO or fear-of-missing-out and the frenzy for talent is exacerbated by the increasing number of data sources, third party tools, and success stories. As businesses orient to data driven cultures, professionals have opportunities to reframe their experience, skills, and abilities to meet those needs.
In the early boom time of “data scientists,” the role can hold a level of mystic and magic tricks. In other words, expectations may be very high and even unreasonable. It is important early on to recognize the pitfalls of being the first data scientist and ask the right questions.
The data consciousness of an organization and its people are too complicated to understand and discover within a few meetings.
The success of data science and analytics (DS/A) are highly dependent on context. Outcomes require aligning the majority of stakeholders and leaders. There are indisputable fundamentals that must be in place a DS/A team, even if for the time being that team is 1…YOU! The most important role of the lead data scientist is to lay a solid foundation for new team members, especially so they can join the team with less obstacles and unencumbered by politics.
The data consciousness of an organization and its people are often too complex to understand from a few meetings. As earliest as possible, ask the following questions about the organization and its data culture to aid in the listening and strategy phases. Try to understand the lowest hanging fruits and biggest possible successes. Resources for building foundations for data science are listed in the Show me the Data post.
The Data Landscape and Culture Questionnaire:
- What was the organization doing with data before they hired a data scientist? What were the deliverables of this person or team? Perceptions of your team and role in the organizations is shape by history.
- What is your budget for staff, tools, and training? It’s not always about money but business driven entities often show their love quantitatively.
- How many departments or people have access to your expertise?
- What data tools are used by each department or group of people? How much analysis does each department apply to each metrics or report? Evaluate the potential resistance to learning new tools and quick wins for easing the burden of data related tasks. Also, do you anticipate having to teach people how to sort and filter information in spreadsheets? This view of the company will determine tools the culture and people are willing to adopt and whether the democratization of data / self-service are realistic.
- What are the key measures used by the organization, executives, and individuals now? You probably would have asked this question without getting prompted. Use the answers for understanding the organizations data skills and aptitude.
- Where are data practices and sources documented?
- Are there effort to create more open access to data?
- What data analysis products do they have in production right now?
- How does leadership and management talk about the future of data science?
- What stories inspire and excite your stakeholders the most, i.e. stories in the media and internal wins?
Then in order to break your expectations down to a more granular level, use the check list below to identify tasks that you and your team are expected to implement. This determine the potential workload, people, and resources solutions.
Your Data Scientist Duties Checklist (check all that apply):
Which functions are expected solely on you listed below. If you check more than 5 functions then consider a conversation about delegation, resourcing, and budget for SaaS products. Focus is the key to success so set yourself up for success from the start.
- requirements gathering,
- task or project management,
- product management,
- project management,
- people management,
- data warehousing,
- data pipelining,
- data cleaning,
- business intelligence tools development, such as tools and dashboards,
- data mining and engineering (similar to both 5 and 6),
- analysis of trends,
- reporting (list the frequency of reports),
- responding to ad hoc or urgent requests (list the departments and people that urgent requests may originate),
- training and education
If it isn’t pretty obvious by now, data scientists have lots of skills but limited time to implement. Thus, focus your data scientist’s time on the highest impact areas and expect to ask the organization for more resources and people to help the orgnaization achieve maximum data drivenness.