Questões de Concurso Para analista de tecnologia da informação - desenvolvimento de sistemas

Some things don't change, even during a pandemic. Consistent with previous years, in CIO’s 2021 State of the CIO survey, a plurality of the 1,062 IT leaders surveyed chose “data/business analytics” as the No.1 tech initiative expected to drive IT investment.

Unfortunately, analytics initiatives seldom do nearly as well when it comes to stakeholder satisfaction.

Last year, CIO contributor Mary K. Pratt offered an excellent analysis of why data analytics initiatives still fail, including poor-quality or siloed data, vague rather than targeted business objectives, and clunky one-size-fits-all feature sets. But a number of fresh approaches and technologies are making these pratfalls less likely.

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New technology invariably incurs new risks. No advancement has had more momentous impact on analytics than machine learning – from automating data prep to detecting meaningful patterns in data – but it also adds an unforeseen hazard. As CSO Senior Writer Lucian Constantin explains in "How data poisoning attacks corrupt machine learning models," deliberately skewed data injected by malicious hackers can tilt models toward some nefarious goal. The result could be, say, manipulated product recommendations, or even the ability for hackers to infer confidential underlying data.

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In the end, the secret to successful analytics is not in choosing and implementing the perfect technology, but in cultivating a broad understanding that pervasive analytics yields better decisions and superior outcomes. Usually, you can iron out technology kinks or requirements misunderstandings. But if you can't change the mindset, few will use the beautiful analytics machine you just built.

Disponível em: https://www.cio.com/article/3614692/5-perspectiveson-modern-data-analytics.html.

Acesso em: 15 out. 2021.