The CIO’s Guide to Building a Rockstar Data Science and AI Team
A guide to what your data science and AI experts can be expected to contribute as companies grapple with ever-increasing amounts of data.
Just about everyone agrees that data scientists and AI developers are the new superstars of the tech industry. But ask a group of CIOs to define the precise area of expertise for data science-related job titles, and discord becomes the word of the day.
As businesses seek actionable insights by hiring teams that include data analysts, data engineers, data scientists, machine learning engineers and deep learning engineers, a key to success is understanding what each role can — and can’t — do for the business.
Read on to learn what your data science and AI experts can be expected to contribute as companies grapple with ever-increasing amounts of data that must be mined to create new paths to innovation.
In a perfect world, every company employee and executive works under a well-defined set of duties and responsibilities.
Data science isn’t that world. Companies often will structure their data science organization based on project need: Is the main problem maintaining good data hygiene? Or is there a need to work with data in a relational model? Perhaps the team requires someone to be an expert in deep learning, and to understand infrastructure as well as data?
Depending on a company’s size and budget, any one job title might be expected to own one or more of these problem-solving skills. Of course, roles and responsibilities will change with time, just as they’ve done as the era of big data evolves into the age of AI.
That said, it’s good for a CIO — and the data science team she or he is managing today — to remove as much of the ambiguity as possible regarding roles and responsibilities for some of the most common roles — those of the data analyst, data engineer, data scientist, machine learning engineer and deep learning engineer.
Teams that have the best understanding of how each fits into the company’s goals are best positioned to deliver a successful outcome. No matter the role, accelerated computing infrastructure is also key to powering success throughout the pipeline as data moves from analytics to advanced AI.
It’s important to recognize the work of a data analyst, as these experts have been helping companies extract information from their data long before the emergence of the modern data science and AI pipeline.
Data analysts use standard business intelligence tools like Microsoft Power BI, Tableau, Qlik, Yellowfin, Spark, SQL and other data analytics applications. Broad-scale data analytics can involve the integration of many different data sources, which increases the complexity of the work of both data engineers and data scientists — another example of how the work of these various specialists tends to overlap and complement each other.
Data analysts still play an important role in the business, as their work helps the business assess its success. A data engineer might also support a data analyst who needs to evaluate data from different sources.
Data scientists take things a step further so that companies can start to capitalize on new opportunities with recommender systems, conversational AI, and computer vision, to name a few examples.
A data engineer makes sense of messy data — and there’s usually a lot of it. People in this role tend to be junior teammates who make data nice and neat (as possible) for data scientists to use. This role involves a lot of data prep and data hygiene work, including lots of ETL (extract, transform, load) to ingest and clean data.
The data engineer must be good with data jigsaw puzzles. Formats change, standards change, even the fields a team is using on a webpage can change frequently. Datasets can have transmission errors, such as when data from one field is incorrectly entered into another.
When datasets need to be joined together, data engineers need to fix the data hygiene problems that occur when labeling is inconsistent.
