Can't find data scientists? Don't worry about it

New study says five factors are democratizing data science, potentially easing the talent shortage.

Data science skills give Bloomberg a competitive advantage CTO Shawn Edwards says strong AI capability helps the firm build data-led products for its customers.

It's no secret that data scientists continue to be among the most sought-after professionals in all of IT. As organizations continue to look for ways to gain value and insights from their data, these are the people they frequently turn to in order to make sense of all the information pouring into their systems from a growing number of sources.

Also: The AI, machine learning, and data science conundrum

The good news for companies desperate to find these needed skill sets is that data science is becoming "democratized," which will help bridge the talent gap.

Five factors are democratizing data science and putting this critical capability into the hands of more professionals, potentially alleviating the crippling talent shortage, according to a report released today from consulting firm Deloitte.

Automated machine learning. Some estimates show that data scientists spend about 80 percent of their time on repetitive and tedious tasks -- data preparation, feature engineering and selection, and algorithm selection and evaluation -- that can be fully or partially automated.

Both established vendors and startups have introduced tools and techniques designed to automate tasks. Automating the work of data scientists can make them more productive and effective, the firm said, and organizations can make aggressive use of data science automation to empower and leverage oversubscribed talent.

Application development without coding. Low-code and no-code software development platforms provide graphical user interfaces, drag-and-drop modules, and other user-friendly features that can help IT as well as non-technical staffers accelerate the development and delivery of artificial intelligence (AI) applications.

The firm provides the example of salespeople using a no-code platform to create a machine learning-based tool to provide product recommendations to customers based on cross-sell opportunities. Such platforms can potentially make software development 10-times faster than traditional methods, according to the report.

Pre-trained AI models. As Deloitte points out, building and training machine learning modules is a main activity of data scientists. Some software vendors have launched pre-trained AI models, "effectively packaging machine learning expertise and turning it into products," the report said. These products can cut the time and effort needed for training, or even begin producing specific insights right away.

Self-service data analytics. Business and other non-technical users have tools available that can deliver data-based insights without involving analytics specialists such as data scientists. Self-service analytics tools offered by many business intelligence (BI) and analytics vendors now include features to augment data analytics and discovery.

Some of them automate the process of developing and deploying machine learning models, and features such as natural language query and search, visual data discovery, and natural language generation can help users automatically find, visualize, and narrate data findings such as exceptions, clusters, links, and predictions. This enables business users to perform complex data analysis and get quick access to customized insights without relying on data scientists.


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Accelerated learning. There has been a proliferation of data science and AI-related training courses and boot camps, the report said. These programs are aimed at professionals who possess basic mathematics and coding backgrounds and can impart basic data science skills in a period ranging from a couple of days to a couple of months. Such courses are designed to enable professionals to bring basic data science skills to projects quickly.

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