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<p>Introduction<br>Digitalized data collection methods allow people to collect and store more data they will ever use. Data sorting and interpretation became the most important thing for most home and business users. For example, a woman passionate about cooking can now find thousands of recipes and ideas for dinner. She will have to decide on the recipes that will comply with her tastes, nutritional needs, and available resources. It's no longer a matter of finding a recipe but a matter of selecting and implementing it. The same scenario happens in the business environment. With modern accounting and reporting tools, business owners and managers can now see what customers buy in real-time. However, business owners need to break down this data and reveal what customers expect in the future. This paper will further expand the concept of "big data" and its business impact. With a high concern on the practical aspects, this paper will also include real business examples where "big data" helps.</p> <p>Concept<br>Before exploring how "big data" can impact the business environment, let's define the concept behind it and understand what it means. First of all, "big data" is not only a matter of data amount but a matter of usage capacities. Today, most business organizations will collect, save and sort more data they will ever use (Fan, Lau, Zhao, 2015). A company can say it operates with "big data" if its daily operations generate and store more than the company handles, with a faster collection speed than the one processing it and with a greater variety the company is using for taking its decisions (Fan, et al, 2015). For example, YouTube users upload 72 hours of video content in every minute. By 2014, Facebook users managed to upload over 750 million pictures (Chen, Mao, Liu, 2014). A pure theoretical deduction shows that in 48 hours, YouTube users will upload videos that would take someone 70 years of continuous watching. In practice, this is something impossible to do and Youtube is a great simple example of what big data means. Without using data filters and sorting methods, users will find navigating on Youtube almost impossible. Like individual YouTube users, business organizations can access a lot more data they can handle. Because the technical capabilities are exceeding the current management capabilities, a new approach on "big data" is required. "Big data" is changing the business environment and companies using this information to its best potential will exceed the ones ignoring the business opportunities behind it. "Big data" capabilities and dimensioXX XXX basically XXXXXXXXX. For example Tesco, the leading XXX-chain XXXXXXXXXXX XXXXXXXX in the United Kingdom XXXXXXX by 2015 XX collect and XXXXX XXXX about XXX XXXXXXXX habits XX XXXX XX% of XXX British households (Vera-Baquero, Colomo XXXXXXXX, Stantchev, XXXXXX, XXXX). "XXX data" XXXXXX XX distinguished XXXX "massive data" or "XXXX XXX XXXX". XXXXXXXX XXX XXXXXXXXXXXXX failed XX reach a XXXXXXXXX XXXXXXXXXX XXX XXXX of them XXXXX that "big data" is a term XXXX can XX used for XXX datasets exceeding XXX XXXXXXXXXX capacities XX the XXXXXXXXXX (XXXX, et al, 2014). "XXXXXXX data" and "XXXX XXX XXXX" XXX XXXXXXXXXX XXXXXXX XX information but placed in a technical environment XXXX XXX process it in a XXXXXX and useful XXXXXX. XXX XXXXXXX, a mass transit XXXXXXX can XXXXXXX XXX XXXXXXXXXX its daily XXXXXXX reports XXX optimize its routes if it will only XXX XXX ticket validation XXXXXXXX XXXXXXXXX in every vehicle. If the mass XXXXXXX XXXXXXX XXXX XXXX collect XXXX-time XXXXXXX data from XXX city, it XXXX need a lot XX time XXXXXX XXXXXXXXXX the routes XXXXX XXX the XXXX XXXXXXXXX. The XXXXXXXXX reports XXXX XXXXXXXX "massive XXXX" but XXX real-XXXX XXXXXXX XXXXXXX XXXX XXXX it XXXX "big XXXX". The XXXXX managers XXXX XXXX XX XXXXXX how and XXXX to combine XXXX XXXX XXXXXXX XXX this XX XXX XXXX XXXXXXXXX. X business simulation conducted in X.S XXXXXXXXX XXXX XX the XXXXXXXX XXXXXX XXXX system would have the resources and XXXXXXXXXXXX XX process all the XXXX XXXXXXXXX XXX available, the healthcare XXXXX will XXXX XXXX over 8%. Retailers XXXXX XXXXXXXX their XXXXXXXXXXX XXXXXXX XX XX% XX XXXXX the full potential of the "big XXXX" available (Chen, XX XX, 2014). X concept can XX easy XX XXXXXXXXXX if people will know XXX parameters. XXXX measuring "XXX XXXX", managers XXXXXX XXXXXXXX XXXX parameters. It's XXXXXXX XXXX XXX stands XXX volume but besides its size, "XXX XXXX" XXXXXX be evaluated XX measuring its value, XXXXXXXX and XXXXXXX (Chen, et al, XXXX). Not XXX XXXX collected XXXXXX XXXX XXX XXXXXXXXXX XXXXX will XXXXXX "XXX data". XXXX XXXXXXX XXXXXXXX waste information. XXX example, XXXXX XXX XXXXXXX the grocery shopping preferences XXXX 40% households but XXX order the XXXXXXX will XXXX XXX products may XXX be valuable for the marketing XXX XXXXXXXXXX team. X XXXXXXX supermarket XXXXX collect a XXX XX XXXX XXXXX what XXX when customers buy. But the XXXXXXXX XXXX XXXXX XXXXXX to XX through XXX this XXXXXXXXXXX XXXXXXX XXXXX XXXXXX XXXX sorting and processing tools.<br>XXXXXXXXXX<br>XXXXXXX with "big data" is XXX XXXX a challenge for XXXXXXXX XXXXXXXXXX XX come XX with XXXXXXXXX and XXXXXXXXX that can process the information XXXXXX XXX XXXXXX XXX XXXX a XXXXXXXXX XXX the XXXXXX management XXXXXXX. "XXX XXXX" XXX XXXX have a negative XXXXXX on the business environment. XXXXXXXXXX the XXXXXXXX impacts can XX XXXXXXXX by XXXXXXX in a good XXX XXXX XXXXXXX challenges XXXX data representation XXXXXXX, data compression, XXXX XXXX cycle, the analytical XXXXXXXXX, efficiency, scalability XXX XXXXXXXXXXX (XXXX, et al, 2014). The next XXXXXXXXX XXXX XXXXXXX explain XXXX XXXXXXXXX and demonstrate they XXX XXXXXXXXXXXXXX. XXXX XXXXXXXXXXXXXX XXXX XXXXX XXX XXXXX of the XXXXXX XXXXXXX. XXX best XXX XX determine XXX level of XXXX XXXXXXXXXXXXXX is by XXXXXXXXX the XXXXXX XX "waste" or XXXXXXX XXXX. XXXX compression will reduce XXX amount of XXXXXXXXXX data XXXXXXXXX XXX same XXXXXX. XXX XXXXXXX, marketers XXX split their customer XXXXXXXX XXXX market XXXXXXXX XXXXXXX XX XXXXXXXX separate XXXX XXXX for each XXXXXXXX. Each XXXXXX segment will XXXXXXX XXXXXXXXX XXXX XXXXXXX XXXXX, expectations, XXX behaviors (XXXXX, Hartley, XXXX). XXX XXXXXXXX XXXXXXXXXXX is XXXX XXXXXXX and XX many XXXXXXXXX, managers will XXXX to XXXX a XXXXXXXX XXXX. Valid XXXXXXXXXXX XXXXX a key XXXX in XXXXXX a good XXXXXXXX. XXXXXXXXXXX and XXXXXXXXXX will work better with strong XXX XXXXX XXXXXXXX. XXXX XX why business organizations will have XX pay a great attention XX XXXXXXXXX the data XXXX XXXXX. In each XXXXXX, XXXXXXXX that XXX part of XXX "XXX XXXX" can become obsolete XXX business XXXXXXXXXXXXX should develop XXXXX for XXXXXXXXX this data from the decision-XXXXXX XXXXXXX. For example, a XXX dealer XXXX XXXX no use about the customer trends XXX XXXXXXXXXXX XXXXX XX years XXX. XXXXX XXXX, XXXXXXXXXX, XXXXXX, and innovations changed XXXXXXXX preferences. XXXXXXXX data XXXX alter the XXXXXXXX XXXXXXXXX. XXXXX XXXXXXXXX the obsolete XXXX, the XXXXXXXX organizations XXXXXX pay a XXXXX attention XX developing XXX right analytical XXXXXXXXX. XXX analytical XXXXXXXXX will turn XXX bulk data into predictions XXX information XXXXXX in the informed decision-XXXXXX process. XXXX, this mechanism should XX XXXXXXXXX by using fewer XXXXXXXXX XXXX XXX XXXXXXXX outcome resulted from XXXXXXXXXXXX the business XXXXXXXXX. A XXXXXXX should XXXXXXX XXX costs XX developing and XXXXXXXXXX XXX analytical XXXXX with the XXXXXXXXX XXXXXX or XXXXX benefits. XXX potential market or sales XXXXXXXX will XXXXXX a lot on the scalability XX XXX XXXXXXXXXX XXXX. Scalability XXXX allow the same XXXXXXXXXX XXXX to XX XXXXXXXXXXX in other XXXXXXXXXXX or on other occasions. X XXXX XXXXXXXXXXX will XXXXXX the cost XXXXXX and increase the XXXXXXXXXXX XXXXXXXXX XX using "XXX XXXX". The XXXXXXXXXXX advantage can XX enhanced XX encouraging XXXXXXXXXXX. XXX XXXXXXX, two XXXXXXXXX XXXX different XXXXXXXX XXXXXXX can share their XXXXXXXXXX tools XXX drawing XXX XXXXXXXX of their typical customers. XXXX estate XXXXXXXXXX XXX XXXXX data with XXX XXXXXXXXXXXXX and create combined offers. <br>Big XXXX in XXXXXXXXX<XX>Marketing is XXX business XXXXXXXXXX or function XXXXX the results XX using "XXX data" XXX XXX XXXX XXXXXXX XXX the external XXXXXXXXXXX XX XXX XXXXXXXXXXXX. XXXXXXXXX XXXXXXXXX will decide XXX way a XXXXXXXX organization should XXXXXXXX with XXX XXXXXXXXX, as part of XXX pricing, XXXXXXX, XXXXXXX and XXXXXXXXX strategy. XX XXXX, 50% XX the marketers XXXXXXXX XXXX XXX IT infrastructure helps XXXX a lot in taking XXXXX decisions and XX% agree that "XXX XXXX" is an underused asset in the organizations (XXXXXXXX, XXXX). Both big XXX XXXXX XXXXXXXX XXXXXXXXXXXXX XXXXXX invest in creating XXXXX XXX XXXXXXXXXX and analyzing "big XXXX" in the XXXXXXXXX process. XXX companies will XXXXXX XXXX a XXXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX strong infrastructure XXX tools XXX collecting XXX XXXX. Small XXXXXXXXX can XXXXXXXXXX with a XXXXXX XXXXX XX data representation XXX XXXXXXXXXXX. The essence of XXXXXXXXX is XX meet XXX market demands for a price that customers XXX XXXXXXX to XXX and in a profitable XXX XXX XXX XXXXXXXX organization (Kerin, XXXXXXX, 2016). "Big XXXX" XXX XXXXXXX this process XX XXXXXXXXX XXXX customers XXXX XXX how much XXX XXXX willing to pay. X very XXXXXXX XXXXXX XXX collecting "big XXXX" XX by XXXXXXX XXXXXXX cards. Big chain supermarket XXXXXXXXX XXXXXXX XXXXXX these XXXXX to XXXXX customers XXX XXXXXXX show XXXX XXXX XXXX XXXX a minimum impact XX customer XXXXXXXXX and XXXXXXX XXXXXX (Bellizzi, Bristol, 2004). XXXXXXXXX XXXX receive XXXXX XXXXXXXXXX XXX using XXXXX loyalty XXXXX at XXXXX XXXXXXXXXXX they XXXX XX the supermarket. XX XXXXXXXX, holding these cards will not XXXX XXXXXXXXX XXXXX but it XXXX help XXXXXXXXXXXX collect data XXXXX their XXXXXXXX XXXXXX and preferences (Bellizzi, Bristol, 2004). By XXXXXXXXX XXXXXXXX XXXXXXXXXXX, retailers XXX XXXXXXX the XXXXXX for XXX products thus XXXXXXXX XXX logistic costs and increasing XXXXX rotation. Holding key information XXXXX customer XXXXXXXXXXX will create a XXXXX XXXXXXXXXXX XXXXXXXXX XXX XXX XXXXXXXXX XXX this XX why manufacturers XXX to compensate XXXX their own XXXXXXX of collecting and using "XXX data". Some manufacturers will XXXXXX XXXXXXXXX XX XXXXXXXX their products XXXXXX and XXXXXXX from XXXXXXX discounts or extended XXXXXXXX. XXXX XXXXXXX XXXX unleash XXX XXXXXXXXX XXXXXXXXXXXXX for XXXXXXXX XXXXXXXXXXXXX XXXXXXX XXXXXXXXXX data XXXX each individual XXXX XXXXXXX the XXXXXX segmentation efforts and XXXXX XXX XXXXXXX to come up with personalized XXXXXX. Millennial customers usually XXXXXX personalized XXXXXX XXX XXXX appreciate retailers XXXX XXXX XX with XXXXXXX XXXXXX XXXX XXX XXXX. Counting for over XXX-third XX XXX XXXXX population in the U.S, XXX Millennials is XXX XXXXXXX XXXX given XX the generation born between 1977 and 1994 (XXXXXXXXX, 2010). They are XXXX the first XXXXXX generation, with XXX of its XXXXXXX XXXXXXXXXXXX with using XXX internet (Swarney, 2006). XX using the XXXXXXXX, XXXX contribute a lot XX XXXXXXXX XXXXXXXX that will become XXXX XX the "big data". Marketers can XXX XXXXXX a XXX of information about XXXXX customers by XXXXXXXXX XXX XXXXXXXXXX and online XXXXXXXX XXXXXXX of their XXXXXXXXX. XX XXXXXX XXXX these activities XXX threaten the XXXXXXXXXX XXXXXXX levels XXX most users XXXXX to give in private data in XXXXXXXX XXX XXXXXXX online (XXXX) XXXXXXXX. XXX data in XXXXXXXXXX<XX>"Big data" is XXX XXXX useful XXX XXXXXXXXX. XXXXX strategic departments XXXX a XXXXXXX can XXXXXXX XXXX XXXXX it. Valid and XXXXXXXX information XXXXXXXXX XXXX "big XXXX" will always XXXX the management XXXXXXX. XXXXXXX, "big data" created a management XXXXXXX (Olsson, Bull-Berg, XXXX). XXXX managers know too many details about a XXXXXXX, XXXX XXXX XX tempted XX postpone a XXXXXXXX in XXXX XXXX they will XXXX XXX XXXXXXX XXXXXXXX that XXXX XXXXX the greatest XXXXXXXX. However, XXXXXXXXXXX managers consider XXXX a XXXXXXXX XXXXX XXXX XX better XXXX XXX perfect XXXXXXXX taken too late. XXX's imagine a car manufacturer that developed XXX best XXXX efficient car available XX XXX XXXXXX. Knowing that the XXXXXXX XXXXX XXXXX have XXXX minor XXXXXXX issues, XXX product XXXXXXX XXXXXXXXX XXX XXXXXX on XXX XXXXXX. Meanwhile, a XXXXXXXXXX will manage to XXXXXXX a similar product, XXXXXXXX or XXXX XXXXXXXXX XXX XXXXXXXXXXX advantage of the new XXX. XXX XXXXXXXXXX XXXX XXXXXX its XXXXXXX faster, moving most of XXX XXXXXX XXXXXXXXX XX XXXX business XXXXXXXXXXXX. "XXX XXXX" should XX a friend XX the XXXXXXXXXX process XXX XXX a XXX. The XXX to XX XXXX XX XX keeping the challenges revealed in XXX previous sections XX XXXX XXXXX XXXXX control. "XXX XXXX" XXX XXXX XXX business XXXXXXX XX simulating the market XXXXXXXXXX, predicting the XXXXXXXXX stability or XXXXXXXXX XXX manufacturing/sourcing XXXXXXX. By using "big data" XXXXXXXXX can XXXXXX XXXXXXXXXX time, financial XXXXX can XXXXXXX XXX XXXXXX XXXXX and XXXX XXXX XXXXX XXX XXXXXXXX XXXXXXXXXX can reduce XXXXXXXXX XXXX and XXXXX. Business organizations XXX XXXXXXX XXX XXXXX benefits by XXXXXXXX how XX XXXXXXX XXXX and how XX analyze it. XXXXXXXXXX should invest XXXXX XXXXXX in creating "big data" analytical XXXXX that will XXXX the XXXXXXXX XXXXXXXXXXX from XXXXXXXXX datasets. XXX combined information should lead XX XXX knowledge XXX it XXXXXX also discover XXX XXXXXXXX XXX XXXXXX revealed by the previous management decisions XXXXXX, XXXX-Berg, 2015). <br>Security XXX privacy<XX>XXXXXXXX and privacy XXX a XXXXXXX XXX both customers XXX XXXXXXXX XXXXXXXXXXXXX. XXXXX customers XXXXX agree XXXX XXXXXXXXX XXX XXXXXXXXXX XXXX about their XXXXXX habits XXX preferences, most of XXXX XXXXX XXX XXXXX XXXX XXX methods involved in XXXX process can XXXXXXXX their private XXXX. XX XXXX, the European XXXXX and XXX X.S Congress XXXXXX new legislation XX XXXXXXXX privacy regarding "XXX XXXX" (Leonard, XXXX). XXXX legislators XXXXXXXX XX XXX XXX XXXXXX in XXX XXX legislation: notification and consent. People should always XXXX XXXXX XXXXXXX XXXXXX the business XXXXXXXXXXXXX can XXXXXXX any XXXXXXXX XXXX about XXXX. Business XXXXXXXXXXXXX must always reveal the XXXXXXX they are XXXXXXXXXX XXX XXXXXXXXXXX for. XXX XXXX XXX a legislative XXXXXX was XXXXXXXXX XX the XXXXXXXXXX emergence XX "XXXX XXXXXXX" or "data agencies". XXXXX business XXXXXXXX like Acxiom XXXXXXXXXXX managed XX collect customer XXXXXXXXXXX XXXX various XXXXXXX XXX XXX it in one place in order XX create a huge XXXXXXXX XXXX XXXXXXXX information about XXXXXXXX (XXXXXXX, 2014). XXXXXX XXX not give XXXXX XXXXXXX to Acxiom XXX XXXXXXXXXX and XXXXXXX XXX private information XXXXX them .XXXX XXXX XXXX XXXXXX XXXXXXXXX, XXXXXXXX organizations XXX XXXXXX XX no XXXXXX XXXXXX in XXXXXXXX XXXXX own "big XXXX" infrastructure. The XXXXXXX issue XXXX XX that customers don't want XXXXXXXXX XX XXX their XXXXXXX XXXXXXXXXXX XXX business purposes without XXX XXXXXXX. Also, handling XXX XXXXXXXXXXX from one XXXXXXXX entity to XXXXXXX will XXXXXXXX any XXXXXXXX XXXXXXXX. Maybe everyone can find out information XXXXX everyone by paying the XXXXX XXXXXX XX money. XXX's imagine the following XXXXXXX. A XXX usually XXXXXX a XXXXX XXXXXXXXXXX after XXXX to buy flowers XXX a XXXXXX of wine. The XXXXXX card XXXXXXX XXXX XXXXXXX this data and sell it to a data agency. XXX XXXX XXXXXX XXXX XXXXXXX XXXXXXX XXXXX XXXXXXXXXXX from the XXXXXXXX's XXX station XXXXXXX XXXX and finds out how much does he XXXXX XX XXXX XXXXXXXX. XXX XXXXXX XXXXX XXX data XX a competitive supermarket XXXX XXXX send a letter with a XXXXXXXXXXXX XXXXX XX XXXX man. The XXX's XXXX will XXXX the XXXXXX and XXX XXXX start questioning her XXXXXXX XXXXX his shopping habits and ask XXX where are the XXXX bottles and who received the flowers. The best business interests may not XX XXXXXXXXX with the XXXX XXXXXXXX interests. An investigation XXXXXXXXX by XXX XXXXXXXX XXXXX XX "big data" and consumer privacy revealed that some merchants XXXXXXXX XXXXXXXXXXXX, social XXX commercial XXXXXXXXXXX XXXXX XXXXX XXXXXXXXX in order to XXX discriminatory pricing XXXXXXXXXX (XXXXXXX, 2014). For example, an online store may XXXXXXXXX the XXXXXX XXXXX XX a XXXXXXXX and how XXXXX XX he to XXX a XXXXXXX XXX use automatic algorithms to change the XXXXX of XXXX product. XXX customers XXXXXX in the XXXX XXXX XXXXX XXX different XXXXXX XXX the XXXX XXXXXXX XXXXXXXXX in XXX same conditions. This is XXX a matter XX marketing or XXXXXXXXXXXX offers but a matter of XXXXX "XXX XXXX" XXXXXXXXX against the customer's XXXX XXXXXXXX. Readers are invited to think XXXXX XXX XXXXXXXX XXXXXXX XXX think if it's XXXXXXX or XXX. XXX latest European legislation demands XXXX XXXXXXXX XXXXXXXXXXXXX will ask XXX XXXXXXX of XXX customer before XXXXXXXXXX XXX data, XXXXXX him XXX chance XX XXXXXX. XXX XXXXXXXX cannot XXXXXX the XXXXXXXX order for XXX XXXXXXXXXX. XX XXXX, XXXXXXXXX XXXXXXX learning XXXXX these unethical XXXXXXXXX XXX XXXX began protecting themselves. XXXX refuse to disclose XXXXX data to XXXXXXXXX while XXXXXX give in false XXXXXXXXXXX XX XXXXXXX (XXXXX, Sharma, 2015). Of course, that false XXXXXXXXXXX will alter the XXXX value and may XXXX to wrong XXXXXXXXXXXXXXX XXX eventually XX XXXXX management decisions. Transparency and honesty is a solution in this customer-XXXXXXXX conflict. This XX a XXXXXXXXXXXX where XXXX sides XXXX each XXXXX. XXX business XXXXXXXXXXXX must find out XXXX customers want in order to XXXX on XXX market. Customers want XX find XX XXX market XXX XXXX product or XXXXXXX XXXXX money can XXX. <br>XXXXXX perspectives<XX>The XXXXXXXXXXXX XXXXXXXXXXX XXXXX XXX "XXX XXXX" XXXX XXXXXXXXX a XXXXXXX where business XXXXXXXXXXXXX XXXXXXX more data they XXX XXXXXXX while managers XXXXXX XX XXXX and XXXXXXX this XXXXXXXXXXX in order XX XXXXXXXX the XXXXXXXX process. However, more XXX XXXX XXXXXXX are XXX connected to XXX internet. The internet XX XXXXXX moving from the XXXX-XXXXXXX interface XX a fully XXXXXXXXX interface XXXXX machines XXXXXXXXX XX XXX XXXXXXXX can XXXXXXXX and XXXX decisions without a human controller. XXXX new approach is called "the internet of things" or XXX and it already proved XX XX very XXXXXXX for organizations XXXX XXX XXXXXXXXXXXXX sector (Breur, XXXX). With IoT, manufacturers XXX reduce their production cycle, XXXXX raw material waste XXX XXXX XXX maintenance XXXXXXXXXX in the XXXX XXXXXX (Breur, 2015). Connecting XXX concept of "big data" with XXX XXXXXXX XX XXX will XXXXXXXX an XXXXXXXXX XXXXXXXX process where regular XXXXXXXXXX XXXXXXXXX XXX taken by XXX XXXXXXXX. For XXXXXXX, the same XXXXXXXXXXX collecting data XXXXX its customers can XXXX XXX XXXX-time product XXXXXX curves to XXX XXXXXXXX. The XXXXXXXX would XXXXXXXXXX XXXX XXXXXXXXXXX and XXXX it to the XXXXXXXXXXXX where XXXXXXXXXX software XXXXXXXXXXXX will coordinate the production cycle and tune XXX output accordingly. XXX challenge XX XXX future is XX XXXX these machines XXXXXXX with "big XXXX" and take business decisions XXXXXXX a XXXXX decision.<XX>When "big data" XXXXXXX as a trend in the business environment, most XXXXXXXXXXXXX adopting it invested a lot XX effort in creating the XXXXX for collecting data. XXXXXXXX huge datasets with information XXXXX everything XXX everyone was a XXXXX objective (XXXXX, 2015). XXX, the XXXXXXXX generates XXXXXX an XXXXXXXX XXXXXX of XXXX XXXX XXXXXX. XXX new XXXXXXXX XX on the XXXXXXXXXX tools XXX on XXXXXXX XXXXXXXX to reduce XXXXX volume XX daily XXXXXXXXX XXX XXXXXXXX their impact. XXX XXXXXXX, business XXXXXXXXXXXXX XXX move XXX XXXXXXXX XXXXXXXX to the machines XXXXX "big XXXX" and XXXXXX an XXXXXXXXX decision XXX save manpower and XXXXXXXXXX XXXXXXXXX XXX XXXXXXXXXXXXX XX XXXXXXXXX XXXXXXXXXX decisions. XXX XXXX way to XXXXXXXXXX the XXXXXX XX XX XXXXXXXXX the past and XXX XXX things evolved. XX the 70's, bankers XXXXX evaluate loan requests by personally calculating XXX XXXXXX score. In XXX 90's, bankers used XXXXXXXXX XX XXXXXXXXX the credit score. XXX XXXXXXXX would determine the XXXXXX score XX XXXXX the XXXX XXXXX XXXXXXXX XX XXX banker (Breuer, 2015). XXXXX, XXXXXXX can XXX the XXXXXX XXXXXXXX to complete an XXXXXX form XXXX general personal XXX financial XXXXXXXXXXX. XX using "XXX XXXX", XXX computer XXXX instantly XXXXXXXX a credit score and XXXXXX it to XXX XXXXXX XXX XXXXXXXX or XXXXXXXXX. XXXX time, XXX XXXXXXXX XXXX XXXX XXX XXXXXXXXX customer by fitting XXX XX a XXXXXXX XXX compare XXX with other XXXXXXXXX XXXX a similar XXXXXXX (age, XXXXXXXX, XXXXXX, education XXX.). With IoT, XXX XXXXXXXX XXXXX even receive XXX requested XXXXX instantly.<br>XXXXXXXXXX<br>"Big data" XX a symbol on how IT XXXXXXXXXXXXXXX evolved in time. The new XXXXXXXXX XX to XXXXXXXXXX XXXXXX XXXX XXXXXXX. With XXXXXX means XX communication, XXXXXXXXXXX XX everywhere. People XXX XXXXXXXX XXXXXXXXXXXXX can access almost XXX XXXXXXXXX about everything. With this huge XXXXXX of data XX XXXX, XXXXXXXX XXXXXXXXXXXXX have XX XXXX up XXX XXXX XXX XXXXXXX their analytical tools. "Big data" is XXX XXXX XXXX of valid or XXXXXX data XXX business XXXXXXXXXXXXX must XXXX the information and XXXXXXX what they need from it. "XXX data" XX XX longer a XXXXXXXXXXX advantage XX XXX big XXXXXXXXX. XXXXXXX companies can XXXX access "XXX data" XX using cloud computing XXX XXX internet XX a XXXXX. XXXXXXXXX specialists XXX XXX XXXXXXXX are already XXXXX XX XXX impact they can XXXX XX XXXXX "XXX XXXX" for taking informed-XXXXX XXXXXXXXX. Manufacturing XXX XXXXXX XXXXXXXXX can XXXXXXX use "XXX data" to their XXXXXXXXX. XXX XXXXXXXXX still remain: XXXXX should XXXXXX XXX XXX XXXXX XXXX XXXXXXX the benefits of XXXXX "XXX XXXX" and XXXXX private XXXXX. How much XXXXXX machines "learn" about us?</p> <p>XXXXXXXXXX:<XX>Bellizzi, X. A., & Bristol, T. (XXXX). An XXXXXXXXXX XX XXXXXXXXXXX XXXXXXX cards in one XXXXX US XXXXXX. The XXXXXXX of XXXXXXXX Marketing, 21(X), XXX-154. Retrieved from <a href="http://ezproxy.liberty.edu/XXXXX?XXX=http://search.proquest.com.XXXXXXX.XXXXXXX.edu/XXXXXXX/220122642?XXXXXXXXX=12085">XXXX://XXXXXXX.liberty.edu/login?XXX=XXXX://search.proquest.XXX.XXXXXXX.liberty.edu/docview/220122642?XXXXXXXX...</a><br>Breur, X. (XXXX). XXX XXXX XXX the XXXXXXXX of things. XXXXXXX XX XXXXXXXXX XXXXXXXXX,3(X), X-4. XXX:<a href="http://dx.XXX.org.ezproxy.liberty.XXX/XX.1057/XXX.2015.X">XXXX://dx.doi.org.ezproxy.liberty.edu/XX.1057/XXX.XXXX.7</a><br>Fan, S., XXX, X. Y. X., & XXXX, J. X. (XXXX). Demystifying XXX XXXX analytics for XXXXXXXX XXXXXXXXXXXX XXXXXXX the XXXX XX a XXXXXXXXX mix. Big XXXX XXXXXXXX,XXX:10.1016/X.XXX.2015.02.006<br>XXXX, X., XXX, S., & Liu, X. (XXXX). XXX XXXX: X survey. XXXXXX XXXXXXXX and Applications, 19(X), 171-209. XXX:<a href="http://dx.doi.XXX.ezproxy.XXXXXXX.XXX/10.1007/XXXXXX-013-XXXX-X">http://XX.XXX.XXX.XXXXXXX.liberty.XXX/XX.1007/XXXXXX-013-0489-X</a><br>Kerin, X. X., & Hartley, S. X. (2016). XXXXXXXXX The Core (XXX ed.). New XXXX, NY: McGraw-Hill Education ,XXXX-XX: XXX-0077729035<XX>XXXXX, S., & Sharma, R. R. (2015). XXXXXXXXX analysis XX the unethical XXXXXXXX of XXXXXXX in X-marketing. Abhigyan, XX(3), 42, retrieved from XXXXXXXXXXXXXX.com/XXXXXXXXX+XXXXXXXX+XX+XXXXXXXXX+XXXXXXXX+XX+cookies+in+X-XXXXXXXXX.-a0440822377<XX>Olsson, N. O. E., & XXXX-Berg, X. (XXXX). Use of big XXXX in XXXXXXX XXXXXXXXXXX.International Journal XX Managing XXXXXXXX in Business, 8(X), 491-XXX. Retrieved from <a href="XXXX://ezproxy.XXXXXXX.XXX/XXXXX?url=XXXX://XXXXXX.XXXXXXXX.XXX.ezproxy.XXXXXXX.edu/XXXXXXX/XXXXXXXXXX?accountid=XXXXX">http://ezproxy.XXXXXXX.XXX/XXXXX?url=http://search.XXXXXXXX.com.ezproxy.liberty.edu/docview/1683715309?account...</a><XX>Leonard, P. (2014). XXXXXXXX data XXXXXXXXX: XXXXXXX settings XXX 'big XXXX' business. International XXXX Privacy Law, 4(1), XX-68. XXX:<a href="XXXX://XX.doi.XXX.XXXXXXX.liberty.XXX/XX.XXXX/XXXX/XXXXXX">http://dx.doi.XXX.XXXXXXX.liberty.XXX/XX.1093/idpl/XXXXXX</a><br>XXXXXXXXX, L. G., & XXXXX, L. L. (XXXX). XXXXXXXX Behavior, XXXX XXXXXXX. [VitalSource XXXXXXXXX version]. XXXX-XX: XXX-XXXXXXXXXX<br>Sweeney, R. (2006). XXXXXXXXXX XXXXXXXXX and demographics. XXXXXX: XXX Jersey Institute of Technology. Accessed on XXXXXXX 17, 2016, XXXX XXXXX://XXXXX.mst.edu/media/administrative/certi/XXXXXXXXX/Article-XXXXXXXXXX- Behaviors.XXX<br>Teradata, (XXXX), XXXX Transactions XX XXXXXXXXXXXX: XXX Data-XXXXXX Marketing XXXXXXXXXXX, XXXXXXXXX XXXX assets.XXXXXXXX.XXX/resourceCenter/downloads/XXXXXXXXXXXX/TDC-XXX-eBook.pdf?XXXXXXXXX=1<XX>XXXX-XXXXXXX, X., XXXXXX Palacios, R., XXXXXXXXX, V., & XXXXXX, X. (XXXX). XXXXXXXXXX big-data XXX business XXXXXXX analytics. XXX XXXXXXXX Organization, 22(X), XXX–XXX. XXX:10.XXXX/tlo-05-2014-0023</p>">
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