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How Big Data and Advanced Analytics change Operations Management

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Business Administration>General




Term Paper: Operation management Operation management focuses on the different processes of the company, they deal with the production or processing of products. The most important goal is to make these processes the most efficient and effective way, because the more efficient and effective they are, the more money it saves for the company. The execution of operations management varies greatly between different markets, but the main idea is always the same: get the best possible output from a given input. 2.2-Big data Big data, which means that a large amount of collected data first appeared in 1941 80 years ago. There, in the "information explosion" in the following decades, until 1999, several researchers and publicists focused on collecting and using the aforementioned "data" to calculate the most different types of growth rates in the data. Most different aspects. Then, in August 1999, Steve Bryson, David Kenwright, Michael Cox, David Ellsworth, and Robert Haimes first mentioned the term "big data" in their article "Exploring gigabyte data sets visually in real time." Now is the early stage of exponential growth in computing and information storage technology, which will make the world almost completely digital from the mid-1980s to most analogues in the mid-2000s. According to Forbes Magazine, the 1986 data was 99.2% analog, while in 2006 it was 94% digital. Now start to understand the meaning of "big data". According to the "Oxford English Dictionary" (OED), big data is "an all-encompassing term used to describe any large and complex data set that is difficult to process with existing data management tools or traditional data processing applications", and McKinsey described it as "...the size of the data set exceeds the capture, storage, management, and analysis capabilities of typical database software tools." (2011) Although there are many definitions, the above definition is the most basic and most important Used. Since big data is a set of data that is too large to be processed manually, a computer (verbal algorithm) is required to understand them, separate useful information from useless information, and use the correct data required for analysis, which is desirable . In 2007, researchers at IDC, the world's leading market intelligence company, found that the amount of available data doubled every 18 months. This study calls for a steady increase in the effective use of these data. In 2007, another technological invention shocked the world, which gives us reason to believe that the amount of data is growing faster than before: smartphones. 2.3-AdvancedAnalytics Advanced Analytics is the next step in intelligent business analysis to make the most accurate predictions and create a competitive advantage in competition with other businesses. It is based on "business intelligence" technology. Business intelligence uses past data to analyze the company's location and the basis on which managers can make decisions. Advanced analysis goes one step further, predicting changes in the business world through predictive analysis. This enables the company to better predict changes and improve responsiveness to changes. Quick adaptation is an important aspect of today's society and companies, and they are destined to be more successful than companies that don't. Advanced analytics are based on intelligent "self-interested" algorithms to improve themselves while performing tasks. This makes them more efficient like old business intelligence, and they analyze date sets much faster than anyone, while also freeing up time for managers to deal with the company's more important matters. A very important part of analyzing data is to filter all data based on its qualifications for a given question. Dr. Uwe Schnetzer, professor of marketing analysis at Cologne International Business School, believes that there are five factors for evaluating the eligibility of data: 1 objectivity of the data, 2 reliability of the data, 3 validity, 4 robustness and 5 completeness of the data. (1) Objectivity is processing data analysis from the aspect of data independence, and (2) Reliability is to clearly analyze the authenticity of data. (3) Validity is based on whether the data is valid for being analyzed to answer the given question, it belongs to the correct part. (4) Robustness The strength of the processing data, if it can withstand the stress test, test the results. Finally (5) completeness processing problem, whether the analyzed data is complete, so it may or may not be effective for the analysis. All the above processes, from analyzing the eligibility of the data, to analyzing the past and present, and possible future results, are being executed by advanced analysis algorithms faster than ever. This is why companies like "Intel" and "SAP" focus on making the fastest processors to support the best algorithms on the market, making processing as fast as possible (Intel) and making machine learning as effective as possible (SAP). Unlike what the outside world might understand, advanced analytics is not just a tool implemented by a company in its business operations. Read Less