Mapping of Data Ecosystem and Job Description
As entrepreneurs it's of prime importance to understand how success is highly correlated with Data. Therefore, being able to navigate Data ecosystem and being able to hire the right team to own that ecosystem in your organization is of prime importance.
Who am I:
I assume authority based on my top three relevant attributes. First, I have worked in the technology domain of Data Center as a software engineer for 6+ years. During this time I developed distributed systems. Second, for 5+ years, I worked in development teams to build web applications that aim to serve millions of customers. Third, I have earned my Masters degree in Engineering Leadership in developing and maintaining dependable software systems.
The prime reason you should read this article is to help us at Women In Data to effectively design our events in order to fill the talent pool gap in metro Vancouver area of British Columbia, Canada.
Let's get started....
Bill Howe categorizes functional areas of data ecosystem as follows - 1) Business Intelligence professionals, 2) Statisticians, 3) Database management, 4) Visualization and 5) Machine Learning. Let's look at these roles in that order.
Business Intelligence professionals are concerned with design, implementation and use of data warehouses. They use database oriented technologies to design dashboards by pulling data from data warehouses. With these dashboards business managers make decisions. These are not data scientists because data scientists are less concerned with building permanent infrastructure for others to use but are more concerned about answering questions and communicating the results.
Statisticians work with data that fits in main memory. They have less data to work with so they have to be sophisticated in their mathematical abilities to extract as much information from the data as possible.
Database management professionals are primarily concerned with RDBMS (Relational Database Management Systems). New age professionals in this arena work with a variety of database systems that are designed to support larger volume, variety and highly dynamic data that are schema-less (contrary to RDBMS).
Visualization experts like statisticians are concerned with data that fits in main memory.
Machine learning engineers are that come closest to the role of data scientist. To understand this role properly please read my earlier article here.