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    Data Science: Short on Skills or Short on Problems?

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    Is the Data Scientist Shortage a Myth?

    Recent discourse suggests the much-discussed shortage of data scientists in HR analytics might be a misinterpretation of the actual challenge. According to Talent Analytics CEO Greta Roberts, the issue is not a scarcity of qualified candidates but rather a lack of clear, business-relevant projects that would justify hiring them.

    A Shortage of Problems, Not People

    The core argument is that data scientists are only valuable when they are solving real problems. As an example, one large firm hired 30 data scientists only to release them a year later. The reason for the failure was simple: the company had no specific problems for them to work on, rendering the hires unable to deliver business value.

    For organizations, this highlights a critical imperative: have a a few well-defined projects and the relevant data on hand before bringing in analytics experts. Without a clear purpose, even the most skilled data scientist will struggle to make an impact.

    A Shared Responsibility

    However, the burden doesn't fall solely on the employer. A successful analytics function requires a partnership between business leaders and data professionals.

    For Analytics Experts:

    • They must cleanly communicate the kinds of questions their skills can answer.
    • They need to demonstrate the value of their toolkit for solving specific business challenges.
    • They should actively guide the process of project development rather than waiting to be assigned a task.

    For Business Leaders:

    • Leaders are experts in the problems their business faces, but they aren't expected to know the full, ever-growing capabilities of the data scientist's toolkit.
    • Expectations for data science are often shaped by media hype about big data. This needs to be grounded in reality.

    Ultimately, the change must be driven by the analytics practitioners within an organization. By shifting the focus from abstract capabilities to concrete problem-solving, they can bridge the gap between business needs and data-driven solutions.

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