FRINK Linked Data Fragments server

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• W750315916 abstract "Big data promises to dramatically alter the business environment, but collecting the data is only the first step. To yield results, the data must be leveraged to support critical decision-making. For structured data--quantitative answers to questions such as how much was bought or sold or what it cost--this is fairly straightforward; many firms already apply structured approaches to big data to support routine operational decisions. But the vast majority of data--as much as 80 percent, by some unsubstantiated counts (Shilakes and Tylman 1998)--is unstructured, consisting of text; making use of this vast body of unstructured data much more difficult. Handling unstructured data requires new decision-making structures and culture, as well as specialized expertise and a disciplined approach to take advantage of the opportunities such data offer. For an extensive review of uses and approaches to big data analytics see MIS Quarterly's recent special issue, Intelligence (Chen, Chiang, and Storey 2012). Unstructured text data can be used to drive decision making in new product development and customer identification. Critical product development decisions can be improved by using unstructured text analytics to convert text into data that can support well-informed product development decisions. We offer an example of such an approach, which can help companies realize one type of value hidden in masses of unstructured data. The Uses of Structured and Unstructured Data Big data in reality consists of two types of data; structured (numerical) and unstructured (textual) (Table 1). Structured data often consists of large sets of numbers, which can be analyzed using a variety of statistical techniques. These techniques reveal patterns that allow decision makers to see what has happened in the past or even what is happening in real time. These analyses, and the patterns they reveal, are essential to operational decisions regarding, for instance, pricing, distribution, or inventory levels. Textual data, especially unstructured textual data, is more difficult to leverage. Although the exact amount of unstructured data available on the web is unknown, it is undeniably huge and includes all the text contained in government sources such as the websites for the Securities and Exchange Commission, Patent and Trademark Office, National Institute of Health, National Science Foundation, and Department of Energy, as well as academic research, business, and financial reports, consultant research results, and many other sources. Unstructured text is also found in social media outlets such as Facebook, blogs, customer complaint logs, and Twitter, as well as news transcripts, magazines and journals, and many other outlets. Unstructured text analytic approaches seek to isolate critical pieces of information from the flow of text. For example, unstructured text analytics can find announcements in local newspaper that a competitor is creating new jobs or building a new facility, or that a customer is expanding operations in a specific region. Just as Voice of the Customer initiatives search for clues to what customers need in interviews, text analysis uncovers customer needs, competitor actions, emerging trends, and other individual pieces of information necessary to inform critical product decisions. Business applications for unstructured text might include, for example: * Finding where customers or competitors are building new facilities by scanning all building permits issued across the country. * Identifying proposed changes in regulations for a compound your company produces by reading all bills and regulations under consideration in both federal and state legislatures. * Assessing the probability of a recall of defective parts by tracking complaints in blogs and other forms of social media. Reliably finding these pieces of information requires gathering vast amounts of unstructured text using tools like Hadoop or high-performance cloud computing (HPCC). …" @default.
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• W750315916 date "2015-03-01" @default.
• W750315916 modified "2023-09-24" @default.
• W750315916 title "Unstructured Text Analytics to Support New Product Development Decisions: Unstructured Text Analytics Can Be Used to Extract Information from Big Data to Support Decision Making for New Product Development" @default.
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