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Focus on specific data types: e.g., time series, video, audio, images, streaming text (such as social media or online chat channels), network logs, supply chain tracking (e.g., RFID), inventory monitoring (SKU / UPC tracking).
Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptiveanalytics for business forecasting and optimization, respectively. What is the point of those obvious statistical inferences? How does that work in practice?
Though you may encounter the terms “data science” and “dataanalytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, dataanalytics is the act of examining datasets to extract value and find answers to specific questions.
A business intelligence strategy is a blueprint that enables businesses to measure their performance, find competitive advantages, and use data mining and statistics to steer the business towards success. . Every company has been generating data for a while now. The question is, what are you doing with it?
If we dig deeper, we find that two factors are really at work: Causal data versus correlated dataData maturity as it relates to business outcomes. One of the most fundamental tenets of statistical methods in the last century has focused on correlation to determine causation.
In 2016, the technology research firmGartnercoined the term citizen data scientist, defining it as a person who creates or generates models that leverage predictive or prescriptiveanalytics, but whose primary job function is outside of the field of statistics and analytics.
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