Introduction.  This week’s second post will look at three important job functions within the modern IT landscape. By examining these positions we get a clearer picture of the requirements for large enterprises in managing and analyzing data.

Personal Take-Aways.  The purpose of this post is not to create an all-encompassing list of data workers, but rather clarify some definitions for some of the most common roles in the industry.  I learned through the importance of machine learning in the IT job market, and learned important distinctions between data management roles – although ultimately many of the lines can be blurred between these roles.

Definitions.  First, we must differentiate data and analytics. Data is information in a raw form, and analytics is a process “of discovering, interpreting, and communicating significant patterns in data” (Oracle, People working in data roles focus on material-agnostic storage and processing of raw data, while people working in analytics strive to attach meaning and tangible understanding to data. Both functions are equally important, and the best businesses do a good job of synergizing data and analytics to do things such as predictive analysis or identifying critical areas of the business (or market) for further attention.

Data Roles include data modelers and data engineers.  The important distinction for data jobs are that they focus on agnostic data facilitation – transport and storage – rather than content.

Data Modelers.  Like the name indicates, modelers are most concerned with describing the current state of data storage and transfer.  While engineers design, modelers describe.  Data modelers’ primary job is to determine existing or desired states, as described by architects, practitioners, and users, and create models (“artifacts”) of these states.  A primary difference between engineers and modelers is that engineers often are asked to create new solutions to problems or challenges while modelers describe the state of the system based on, among other tools, interviews of subject matter experts and other forms of research.

Data Engineers are primarily concerned with providing data in an efficient manner, in raw form. In order to do this, they will examine metadata (“data about data”) and focus on how data is shared and used throughout the enterprise and across functional areas.  Data engineers design IT architecture, data marking techniques, and data structures such as data pipelines to more efficiently and effectively store and transport data. A key aspect of data engineering is collaborating with the originators of the data and understanding cross-functional uses of data, with the goal of increasing usability of data across functional areas.

Analytics Roles.  Here, the main distinction is that analysts assess content and attempt to find patterns, meaning, and establish significance – which can look different for each business.  A production or mining company will likely prioritize different analytics than banking or service companies.  Business analysts fall into this category, but we don’t examine that in this post.

Data Scientists generally take a predictive approach by using models to understand, describe, and predict the behavior of large systems. Data scientists typically examine more broad strategic issues or perhaps more technical issues but in a larger scale, and employ both predictive and prescriptive analytics.  While business analysts attempt to understand processes and the customer or the business itself, data scientists look attempt to understand the larger system in which data resides. As in any field, however, lines are blurred. For example, Heizenberg and Judah note that “cloud computing engineers and machine learning engineers are both considered data scientists. Machine learning engineering is one of the fastest growing fields and could soon represent more than half of all data scientist positions.”

Special Note.  One of the key predictive trends of Gartner is that artificial intelligence technology will increase the participation of people with disabilities in the economy. “By 2023, the number of people with disabilities employed will triple due to AI and emerging technologies reducing barriers to access.” This is an example of computing technology not only making processes more efficient, but also allowing people and personnel to become more efficient by broadening the opportunities within the labor market.

Source: Gartner. “What Are Must-Have Roles for Data and Analytics?”
6 July 2021.

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