Starting out in an unfamiliar domain/field should be easy
Imagine being hired as the Data Manager Consultant for a Malaria-focused clinical study and you know close to nothing about the subjects of Malaria and its associated metrics/indicators.
How then do you navigate through this unfamiliar terrain and succeed in your role?
Before proceeding to the main aim for this article, let me quickly answer this pertinent question that may be rumbling through the mind of those who are strictly pro data science: How is a Data Manager different from a Data Scientist?
My short Answer — The two terms are related but not the same.
In simple terms, the data manager ensures that the data required by the data scientist is readily available & accessible, and of high quality.
Oh, yea! there is increasing thinness of the demarcating line, separating the roles and responsibilities of the different experts in the data business; including the one between a Data Scientist and Data Manager.
Ok, back to the aim of this article — In this article, I would itemize and describe what you can do to succeed as the Data Manager Consultant in an Unfamiliar domain/field.
These four things will help you succeed as a Data Manager Consultant in an unfamiliar domain/field.
1. Identify and liaise with the go-to guy(s)
Every organization or community of practice has individuals with a huge depth of its domain/field. These individuals have a broad and overarching knowledge of the different components of the domain/field; and are capable of helping novice understand related concepts very intuitively and reduce the learning curve.
To succeed as a Data Manager Consultant, you must identify these individuals and involve them in every single process. They will help you, robustly, improve & guide your ideation, conceptualization, development, iteration, and modification of the data management system more contextually and domain-relevant.
2. Begin with the end in mind
This simply means you must think through all the processes required to achieve your deliverable, and deliberately and strategically tie all required actions, inputs, and processes to them. You must ask and answer the following questions —
a. What are my expected deliverables;
b. What are the processes required to achieve each of my expected deliverables;
c. What processes require my immediate action, which requires my later action, which process(s) must run from the start to the end;
d. Are the required processes linearly ordered or not; what is the order of listing of the processes (if linearly ordered);
e. What are the external factors or constraints that will impede the achievement of my expected deliverables; and
f. What internal constraints will take time to overcome.
It may be helpful to draw a graphical framework that visually shows the link and interconnectivity between the processes and the expected deliverables.
3. Understand the power differential and leverage on it, ethically.
Power differential, specifically — role-power plays a key role in determining how fast a request or activity gets reviewed, accepted, and implemented.
Every Consultant’s responsibility or expected deliverable is time-bound. As a Consultant, you can get stuck with emerging challenges that may impede your efficiency. You should be aware that some individuals in the workplace/community of practice can help you, ethically, surmount emerging challenges more quickly, easily, and timely. You must identify such individuals, and maintain a functional line of communication with them. It gives you the leverage of overcoming any challenge quickly.
4. Do not re-invent the wheel; explore and leverage on existing structures.
Why stress about creating what already exist anew?
Focus on optimizing efficiency by studying the landscape of the domain to identify any existing component (protocols, tools) you can leverage on. This helps you align with the standard practice within the domain without initially going off tangent or dilly-dallying.
For example, why struggle to develop a new data quality framework, if there is an existing one you can adapt.
In summary —
- A Data Manager institutionally prepares and makes the required data available for a Data Scientist. A Data Scientist extract insights from big data using questions generated through exploratory analysis.
- You can succeed as a Data Manager Consultant in an unfamiliar domain/field if you — Identify and liaise with the go-to guy(s), begin with the end in mind, explore and leverage on existing structures, and understand and leverage on the power differential (role-power)of stakeholders.
Now that’s it. Hope you find this useful? Please drop your comments and follow me on LinkedIn at Ayobami Akiode LinkedIn
4 Things to Do to Succeed as a Data Manager (Not Scientist) Consultant in an Unfamiliar Domain was originally published in The Startup on Medium, where people are continuing the conversation by highlighting and responding to this story.