The rise of the citizen data scientist

PCWorld

When Mark Pickett was a captain in the Marines, he knew he couldn't be there to make every decision for his soldiers. "You can't rehearse every scenario, and there will be times when you can't communicate," he explained. "You want to groom your Marines to be able to rely on themselves and their unit." Now senior director for online analytics and business intelligence at Sears, Pickett has been an early champion of the so-called citizen data scientist movement, by which employees in multiple parts of an organization are empowered with the analytics tools and skills to get the answers they need from their data. "The business understands the business more deeply than we ever could," he said.


The rise of the citizen data scientist

#artificialintelligence

When Mark Pickett was a captain in the Marines, he knew he couldn't be there to make every decision for his soldiers. "You can't rehearse every scenario, and there will be times when you can't communicate," he explained. "You want to groom your Marines to be able to rely on themselves and their unit." Now senior director for online analytics and business intelligence at Sears, Pickett has been an early champion of the so-called citizen data scientist movement, by which employees in multiple parts of an organization are empowered with the analytics tools and skills to get the answers they need from their data. "The business understands the business more deeply than we ever could," he said.


Democratising data to bridge the data science talent gap

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So much so that a recent report by Indeed identified a 344% increase in job postings for data scientists since 2013. This need comes at a time when data analytics is becoming mission-critical to more and more businesses. New data is constantly available, volumes are increasing and businesses need to use the data to drive deeper and more meaningful insights as they look to become more competitive. Data scientists are key to unlocking the story behind this data. These highly-skilled professionals interrogate and identify key patterns and trends within the data available to them, making a significant contribution to a company's overall performance.


The Rise of the Citizen Data Scientist

@machinelearnbot

The development of Big Data, artificial intelligence and predictive analytics has created extravagant expectations for enterprise productivity growth -- and aroused popular anxiety about intelligent information systems taking jobs from human workers. It is ironic, against that backdrop, that what is holding back widespread adoption of these technologies is, of all things, a manpower shortage. Big data and advanced analytics are the products of data science. What keeps companies from putting them to effective use is an acute shortage of data scientists. The US alone is facing a projected shortage of 140,000 to 190,000 data scientists by 2018.


Citizen Data Science and the Democratization of Analytics - InformationWeek

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The ongoing shortage of data scientists has been well documented. Even as the business world grows increasingly digitized and reliant on big data modelling and analytics to drive value and profit, those possessing the requisite education and expertise in mathematics/statistics, data prep, programming, and distributed computing to meet data science challenges are rare birds. The ability to make sense of the enormous troves of transaction, customer, and equipment data across digitized industries has become a premium skillset, and the recent explosion in machine learning (ML) and artificial intelligence (AI) capabilities has compounded the problem. Now that we can access the compute power and data volumes necessary to operationalize tasks such as pattern recognition, anomaly detection/diagnosis, customer analytics, pricing and predictive planning, we want ML systems that can learn to automatically prepare and perform data science functions with minimal programming. Thus, the irony: Machine learning is often deployed as a kind of digital surrogate for the data scientist, but one that requires the skills of a data scientist to be brought into existence.