How Large Companies Can Grow Their Data and Analytics Talent

Companies need to identify the type of talent they need in order to become data-driven.

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The New Leadership Mindset for Data & Analytics

This MIT SMR Executive Guide offers new insights and strategies for how leaders can help accelerate their companies’ data efforts, from identifying the type of talent they need to shaping a company vision that supports a data-driven culture.

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While many companies are hiring data scientists and other types of analytical and artificial intelligence talent, there is little consensus within and across companies about the qualifications for such roles. The term data scientist might mean a job with a heavy emphasis on statistics, open-source coding, or working with executives to solve business problems with data and analysis. The idea of data scientist “unicorns” who possess all these skills at high levels was never very realistic.

As the job has grown more popular and sought-after, an increasing number of professionals have begun to use it to describe their role. Colleges and universities have responded to the demand as well by offering hundreds of new programs on data science and analytics. But the skills taught in such programs vary widely, and some universities offer multiple programs with different emphases. For both newly hired and experienced employees, titles such as data scientist and quantitative analyst are not likely to be good guides to their actual capabilities.

While there are initiatives to standardize the different types of data and analytics roles and requisite skills across organizations, they are in the early stages. The idea behind these initiatives is excellent, but developing new standards typically takes many years.

In the meantime, companies need to devote considerable attention to classifying and certifying the different types of analytical jobs they have and need. They also would benefit from expanding their talent pools by working with universities directly on educational programs and by building and nurturing communities within their organizations to develop employees for their data teams. These steps are essential for companies looking to use analytics to improve both operations and opportunities for digital innovation — companies such as TD Bank Group.

The idea of data scientist ‘unicorns’ who possess all these skills at high levels was never very realistic.

Understand the Data Roles Needed

Analytics and AI talent is a scarce and valuable resource for every company, but particularly for those with a desire to be data-driven. That’s the situation at TD Bank Group, a large North American bank (the largest by assets in Canada). TD made the decision to use its vast amounts of data to power new data-driven customer experiences and improve its own processes.

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Topics

The New Leadership Mindset for Data & Analytics

This MIT SMR Executive Guide offers new insights and strategies for how leaders can help accelerate their companies’ data efforts, from identifying the type of talent they need to shaping a company vision that supports a data-driven culture.

Brought to you by

AWS
See All Articles in This Series

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