Rise of the Data Designer
A buzzword to say the least, but Data Science is here to stay. The field has popular open source tools- namely R and Python- but many of the concepts and mathematics have been around for over 50 years. Many candidates are able to access this industry with little to no resistance. Data science requires a blend of both qualitative and quantitative skill sets. Quantitative analysis has been largely the focus for this emerging field, but qualitative analysis side will start gaining traction. Communication and design are slowly entering the data scientist’s repertoire, and these will give rise to a new position, the data designer.
The unique blend of backgrounds and skills required by the data scientist role has created a diverse spectrum of talent. Hiring data scientists for organizations is challenging because many do not share the same background. That, in combination with a poor understanding of what a data scientist actually does, has made finding the right data scientist like a finding a needle in a haystack.
Data science is a blend of statistics, computer science, and data visualization. Data science is a quickly evolving field that has given rise to these positions:
- Data Engineers evolve from ETL developers. Combined with machine learning, algorithm, and traditional computer science, they are able to create the big data pipelines that power all of your applications today.
- Data Analysts mix traditional business analysis with data science. Think of the data analyst as a junior data scientist. They have an emerging understanding of statistics, and their role in an organization is to undercover data trends and document opportunities for advancement. Another term called Citizen Data Science fits in this category.
- Data DesignerUsing concepts from UI design, data visualization,and entry-level computer science topics, this role takes data curated from a data scientist or applications like R or Python and turns it into discernible and digestible form.
Communicating machine learning and data curated through the data science process—namely through data visualization—is going to need a trained eye to effectively communicate. The experience and interface of data is going to be the greatest challenge for meaningful analytics. Data designers are urgently needed to effectively communicate this information.
Dieter Rams, a well-known industrial designer, put forth the principle that good design involves as little design as possible. Good design combines humanistic, social, and technical aspects into a cohesive and harmonious medium. Data is ubiquitous in our daily lives, but giving it meaning will be the primary mission of our emerging data designers. Data is our new medium and we need design to give it life.
Design will be the fabric that stitches together the qualitative and quantitative elements for a data scientist. Many disciplines are currently evolving away from the confines of a job description, and that will greatly benefit the data science field.
Republished from LinkedIn Pulse (https://www.linkedin.com/pulse/rise-data-designer-justin-richie)