A glimpse of Big Data Essay Example for Free

A glimpse of protrudesize Data Essay super selective information is not a precise term rather its a depicting of the never ending accumulation of all(prenominal) kinds of entropy, most of it unstructured. It describes selective information sets that are growing exponentially and that are in like manner large, too raw or too unstructured for analysis using relational database techniques. Whether atomic number 65s or petabytes, the precise amount is less the issue than where the data ends up and how it is apply.Cite from EMCs report Big data Big opportunity to create business value. When explosion happened in mobile network, cloud computing and meshing technology, more and more dissimilar information appeared. In the past, the numerous terabyte data could be a disaster for any familiarity, because it means high cost of storage and high death penalty CPU. However, in nowadays, companies discovered many facts they havent thought ab start these data before. Companies star ted to use data analytics technology to find business values from these terabyte or petabyte data. It seems to be a Brobdingnagian opportunity instead of disaster for companies now. Data is not only defined as structured data. When we talking about big data, it could be categorized into three types of data structured data, unstructured data, and semistructured data (Please see Chart I).Especially when internet and mobile internet developed rapidly, the unstructured data and semistructured data exploded. For example, a bank could draw a conclusion by analyze unstructured data to find out why number of churn increased. Most definitions of big data all talk about the size of data. However, size, or volume, is not the only characteristic of big data. There are other two characteristics, variety and velocity. Variety means big data generates from several of sources. Data type was no massiveer attached to structured data. According to the EMCs report, most of big data related to unstru ctured data. Velocity means the speed of data production. Data was no long structured data which was stored in the structured database. Data could come from anywhere and anytime mobile, censors, devices, manufacturing machine etc. The stream of data generates in real time. This means companions deed should be taken with this speed.Structured data Structured data is organized in structure. These data can be read and stored by computer. The form of structured data is structured data base that store specific data by methodology of columns and rows. Unstructured data Unstructured data refers to the data without identified structure. For example, video, audio, picture, text and so on. These data also called loosely structured data. Semistructured data Semistructured data organized in semantic entities. The datas size and type in one group could be different. For example, XML and RSS feeds. This data try to reconcile the real world with computer based database. Chart I. lead types of data.Big data analyticsBig data analytics is not a technique. It is a terms that contains a lot of technologies (See EXHIBITION I). Based on enterprises different requirement, each program will use different technology to analyze data. However, with the big datas development, some of these techniques become popular and useful. On the basis of the exhibition II, advanced analytics, visualization, real time, in-memory databases and unstructured data have strong-to-moderate commitment and strong potential growth.The traditional techniques, for example, OLAP tools and hand-coded SQL, have gradually lost their place. When a bank want to find the reason why the number of customer churn increased, or marketing department decide to push precise advertising to their customer, they need to analyze customer behavior. These data from customer service emails, phone call records, sales interview reports, login data from mobile devices, and so on. Almost all of these data cannot be analyzed by tr aditional data analytic techniques. Thats why these new techniques development so rapid and fierce. How a go with adopt big data analytics?According to the article Big Data, Analytics and the Path from Insights to Value published on MIT Sloan Management Review, the author categorized the company who used big data analytics into three stages (See Exhibition II). For most companies, it is easy to establish an enterprise data warehouses (EDWs). However, how to interpret these data and finding the business value from these data become the most crucial factor for companies. Besides, so many techniques and tools behind the term big data. For any company who decide to adopt big data analytics, the leading obstacle is lacking of understanding of how to use analytics to improve their business. From the article, the author gave 5 recommendation to any company who wanted to adopt big data analytics.1. Think Big. Focus on the biggest and highest value opportunities. Narrow down the options. 2. Start in the Middle. Within each opportunity, start with questions, not data. Company prefer to roll data and information at primary place. In fact, start with questions could help company continue to narrow down the scope and define the most invaluable direction. 3. Make analytics come alive. When Problem was defined, company need to apply analytics. Choosing the propriety tools to analyze the data. 4. Add, dong detract. Use centralized analytics. Every analysis is connected. 5. Build the parts, intend the whole. Big data from everywhere. The data will become more and more big and complex. Building the data infrastructure is crucial for big data analytics. Big Data, Big OpportunityWhen company decide to concern big data, it means every department are involved. Big data is not IT departments or analysts responsibility. In fact, big data analytics need information and help from sales, marketing, RD, IT and even external sources. Today, number of companies have entered into big d ata market. The following graph lists some big organizations who have adopted big data analytics. Besides, some of them provide big data services to other companiesThese organizations are just the decimal point of the iceberg. When big data converted from Blue Ocean to Red Ocean, some of these organizations have turned into services provider. This become a future trend in big data area. Big data needs expensive hardware and labor cost. Not every company can afford that. Besides, big data involved so many different computer technologies, not everyone understood all these techniques. For that matter, there will be more and more companies try to assay big data service from external environment. Using the external big data platform or tools could reduce the cost for building a in all new technique teams. What the companies need to do is finding the problem, narrow down the scope and sending the needs to services provider. When they get the analysis result, they could use the set res ult to take the next action.Furthermore, these services provider will not only focus on big companies. The new fashion is to provide cordial interface and easy to use product to individual customer. What behind big data will be still mystery for people, however, the face or net of big data will become more and more friendly and simple. There is an example Twithink. Twithink is a program invented by a MIT group. They provide customized twitter behavior analysis for customer. This program could draw some conclusion by analysis the unstructured information on Twitter. They collected the gender, location, time, key words, images, etc. from tweets. so they analysis these data under certain arithmetic to draw conclusions. The last research was the Election in 2012. The latest research is Gun Control discussion which still in progress.Problem and threats.Although big data has many opportunity and advantage for enterprises, it still has some disadvantages. The first crucial problem is privacy invasion. later you searched one product on Amazon, the next time when you login to Amazon, you will find the products you may interested which was Amazon pushed to you. This is called precise advertisement. However, you even didnt know when virago collected your information. Another example was Google Analyst, company embedded code into their website to collect peoples internet behavior.These things happened every day and everywhere. It is hard to argue this action is right or wrong. Maybe some are good. However, if personal data is sold or published by someone, it will affect individuals daily life. It will become a crucial problem. The Second problem is informations validity. According to the article With big data comes big responsibilities points out that big data sets are never complete. If data is insufficient, the analysis result would be invalid or distorted. The invalid information would guide company to wrong direction and cause a big loss. Thus, big data also has two side. How to use it to create more value for company is the first consideration for all managers.Reference1. Office 2013 Brings BI, Big Data to Windows 8 Tablets. ZDNet. N.p., n.d. Web. 25 Jan. 2013. 2. Big cognition for IBM Big Data. Smarter Computing Blog Big Recognition for IBM Big Data Comments. N.p., n.d. Web. 25 Jan. 2013. 3. Big Data. Wikipedia. Wikimedia Foundation, 26 Jan. 2013. Web. 26 Jan. 2013. 4. Structured Data. Webopedia. N.p., n.d. Web. 26 Jan. 2013. 5. Unstructured Data. Webopedia. N.p., n.d. 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