Evolving quickly alongside today’s business demands, the data scientist profession is faced with the challenges of interdisciplinary rarity, Forbes reports.
And the job pool is struggling to keep up. Just as we’re beginning to put Big Data in its place, its human counterpart seeks definition within the modern enterprise.
Can the data scientist keep up with today’s growing data demands? The specialized and expensive skills required for this coveted profession are hard to find, making the data scientist a difficult role to scale. So even as the data scientist is hailed as the hero to optimize business as we know it, automation must assist this practice in efficiency.
How do you multiply the role of the data scientist without the workforce to support it? As organizations are asked to do more with the same, they’ll rely increasingly on data to root out efficiency gaps and provide opportunities for workflow automation.
And that goes for the data scientist and his workload as well. Automation will empower the data scientist to empower everyone else at the company, and they’ll need the help of software. Merely throwing more data scientists at the problem of data management won’t solve it.
“Adding more humans like expensive data scientists is not the solution – software is the answer. More data, more people, more complicated questions. You can’t just make up data scientists,” insists Bruno Aziza, CMO of Alpine Data Labs.
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