The process of weather forecasting begins with the collection of as much data as possible about the current state of the atmosphere. Weather data (barometric pressure, humidity, temperature, and wind direction and speed) is collected from a variety of sources, including aircraft, automatic weather stations, weather balloons, buoys, radar, satellites, ships, and trained observers. Due to the variety of data types taken from multiple data sources, weather data is captured in a variety of data formats, primarily Binary Universal Form for the Representation of meteorological data (BUFR) and Institute of Electrical and Electronics Engineers (IEEE) binary. These observations are then converted to a standard format and placed into a gridded 3D model space called the Global Data Assimilation System (GDAS). Once this process is complete, the gridded GDAS output data can be used to start the Global Forecast System (GFS) model.
For purposes of this exercise, imagine that the accuracy of the weather forecasts has been slipping. In your role as project manager at the National Center for Environmental Information (NCEI), you have been assigned to lead a project reviewing the processing of the initial data and placing it into the GDAS.
One business that has benefited through the competitive advantage that was gained through the information systems technology related to an on-demand economy mega trend is Instagram. The Instagram business has done this by becoming a massively powerful big data and data analytics company.
Big data and data analytics is when large amounts of data, collected by mobile phones, machine-to-machine sensors, texts, photos, click stream data, internet searches and many other sources, is quickly processed and analyzed. Most big data is unstructured, meaning that the data does not have a predictable format. This type of data is far too complex and in large quantities that traditional technology cannot handle it. Data is collected to gain insight on consumer wants, wishes, or products used to better company’s marketing strategies and target customers in a more personal way.
Instagram uses big data and data analytics to personalize a user’s explore page, target advertising, enhance user experience, fight cyberbullying and offensive comments, and study human conditions. Instagram analyzed 100 million photos, previously an impossible amount of data, to study global clothing patterns. Because of advanced machine learning, machines are capable of extracting enormous amounts of complex data and can transform it into a look into human social, economic, and cultural customs globally. A survey was conducted that exposed Instagram as the social media platform that users experienced the most bullying. Because of this, “they became the first to use machine learning to automatically remove offensive posts, whereas Facebook and Twitter rely on users to report abusive language” (Marr, B., 2018).
Marr, B. (2018, December 12). The Amazing Ways Instagram Uses Big Data And Artificial Intelligence. Retrieved from https://www.forbes.com/sites/bernardmarr/2018/03/16/the-amazing-ways-instagram-uses-big-data-and-artificial-intelligence/#36be98115ca6
Turban, E., Pollard, C., & Wood, G. R. (2018). Information technology for management: On-demand strategies for performance, growth and sustainability. Hoboken, NJ: John Wiley & Sons.
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