The Impact of Citizen Data Science
The bridge between data and action? Data scientists. They help companies gain a deeper understanding of both themselves as an organization as well as their customers. But what if the everyday employee were able to leverage advanced analytics for this insight, sans PhD? Well, they can and they have—they are known as “citizen data scientists.” You might be wondering, Can I be one of those people? (Yes, you can!). But if you already are a data scientist, you might instead be wondering, How will this impact me?
The impact of citizen data science takes center stage in the new Game-Changing Predictive Machine Learning radio series debut episode (listen to the replay). Bonnie D. Graham launches the conversation with a quote from a Ventana research report: “Only 40% of organizations [of organizations surveyed] have staff capable of performing data analysis without expert assistance.”
What’s the solution? Integrated and embedded analytics tools, found in software platforms such as the cloud, combine and automate data analysis processes to make it simpler for those without previous data science expertise. Considering the high demand for analytics and low supply of trained data science, it’s no surprise that these integrated and embedded analytics tools have gained serious traction in the business world. As Bonnie noted on the show, Ventana found that a whopping 75% of companies utilize them in efforts to help democratize data analytics.
Such technology provides the perfect foundation and breeding ground for average business users to turn into citizen data scientists. Gartner has predicted that by 2019 citizen data scientists will surpass data scientists in the amount of advanced analysis produced. But what will this mean for traditional data scientists?
- Our expert panelists¹ answer the question in this express audio segment from the show.
AI Serves to Enhance, Not Replace, Human Ability
The consensus? Data scientists will never be replaced. The increase in automation will help them be more productive, while helping rookie “citizen data scientists” add value faster and independently. As Dr. Marc Teerlink, SAP, states in the show:
“We always think that new technology should do really, really cool moonshot stuff. Reality is, when new technology arrives, we want to do the same things we always did. We just want to do it faster, cheaper. We want to use more data and we want to get all the boring, repetitive stuff out of it.”
Erik Marcade, SAP, explains that we should automate tasks where possible which will ultimately change job descriptions, with the exception of cases where creativity and innovative thinking are involved. Richard Mooney, SAP, points out that although automation will reduce some tasks within jobs, this is what technology has been doing for a long time. (Take the calculator, a prime example of technology enhancing an accountant’s ability to do math, but not replacing them completely.)
The Blending of Business and Technology Roles
The value of citizen data science resides in creating a lasting impact for both businesses and their customers. Parimala Narasimha, Cox Communications, predicts that there will be a blurring of the line between technology and business roles because for people such as citizen data scientists there must be an understanding of both ends in order to solve real business problems with great technology stacks. After all, the main challenges for traditional data scientists have been a lack of case and business context, as well as the inability to get heard by department and executives.
But if the data scientists are the “citizens” that have been working within those business departments, both problems are solved: they have the extensive business knowledge as well as a voice that has already been deemed credible and heard by colleagues. By distributing citizen data scientists at all levels of organizations, the gap between insight and action will begin to close.
With analytical tools advancing to fill the data science talent void by democratizing the data science process, there is a significant opportunity for all to use these insights to their advantage. The critical element that allows for this is the automation of repetitive and tedious tasks that don’t require extensive data science knowledge.
It can be said that this trend of citizen data scientists actually serves to empower people within their organizations, increasing efficiency and productivity.
For more thought-provoking conversation on today’s Predictive Machine Learning technology, watch past episode replays and express audio segments online. Tune in to the next show airing live on August 8th, 2-3pm EDT on the VoiceAmerica Business channel.
¹ Panelists for Game-Changing Predictive Machine Learning radio series, episode one:
Parimala Narasimha, Director for Advanced Analytics at Cox Communications
Richard Mooney, lead product manager for the Predictive Analytics Product Portfolio, SAP
Former Member , Global Vice President SAP Leonardo, AI & New Markets
Erik Marcade, head of the Advanced Analytics development team within the SAP Leonardo and Analytics end-to-end unit of SAP