Big Data And Data Analytics Essay

2105 words - 8 pages

In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
What are Big Data and Data Analytics?
You may ask what big data analytics is. Well according to SAS, the leading company in business analytics software and services describes big data analytics as “the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.” As the goal of many companies which is to seek insights into the massive amount of structured, unstructured, and binary data at their disposal to improve business decisions and outcomes, it is evident why big data analytics is a big deal. “Big data differs from traditional data gathering due to that it captures, manages, and processes the data with low-latency. It also one or more of the listed characteristics: high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, web, and social media which much of it is generated in real time and in a very large scale.”(IBM) In other words, companies moving towards big data analytics are able to see faster results but it continues to reach exceptional levels moving faster than the average person can maintain.
Differences between Big Dara and Traditional Data Gathering
According to MIT Sloan Management Review, users who use big data differ from those using traditional data gathering in “three ways which are 1. They pay attention to data flows as opposed to stocks, 2. They rely on data scientists and product and process developers rather than data analysts, and 3. They are moving analytics away from the IT function and into core business, operational and production functions.” (Davenport, Barth, and Bean) Organizations that pay attention to data flows instead of stocks are trying to enhance their brand by using continuous data flows. Continuous flows help the performance of the company such as using forecasting, text mining, and optimization and helps executives to make the best possible decision. When companies rely on data scientists and product and process developers instead of data analysts they know that data scientist will have the most knowledge and experience with information technology (IT). In comparison with traditional analytics, data scientist have far more advanced skills and can help to develop products and ideas whereas traditional analytics really advised the executives about the internal portion of the...

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