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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 XXXXXXXXX XXXXXXXXX. For example Tesco, XXX XXXXXXX big-chain XXXXXXXXXXX operator in XXX United XXXXXXX managed by XXXX to XXXXXXX and store XXXX XXXXX XXX shopping habits XX over 40% XX the XXXXXXX households (Vera-XXXXXXX, XXXXXX XXXXXXXX, Stantchev, Molloy, XXXX). "Big XXXX" should XX distinguished XXXX "XXXXXXX data" or "XXXX XXX XXXX". XXXXXXXX and practitioners failed XX reach a universal XXXXXXXXXX but XXXX XX XXXX agree XXXX "XXX XXXX" is a term XXXX can be used XXX XXX XXXXXXXX XXXXXXXXX XXX XXXXXXXXXX capacities of XXX XXXXXXXXXX (Chen, XX al, XXXX). "Massive data" XXX "very big data" XXX impressive XXXXXXX XX information but XXXXXX in a XXXXXXXXX XXXXXXXXXXX that can XXXXXXX it in a timely XXX useful XXXXXX. For example, a XXXX transit company can collect XXX understand its XXXXX traffic XXXXXXX XXX optimize its routes XX it will XXXX XXX XXX XXXXXX validation XXXXXXXX available in every vehicle. XX XXX XXXX transit XXXXXXX will XXXX XXXXXXX real-XXXX traffic XXXX XXXX the XXXX, it XXXX XXXX a XXX XX XXXX before optimizing the XXXXXX using all XXX data XXXXXXXXX. XXX XXXXXXXXX reports will generate "XXXXXXX data" XXX the real-XXXX XXXXXXX XXXXXXX will XXXX it into "XXX data". The XXXXX managers XXXX XXXX XX XXXXXX how and when to XXXXXXX XXXX data XXXXXXX XXX XXXX is the XXXX XXXXXXXXX. X XXXXXXXX XXXXXXXXXX conducted in U.S estimates that XX the XXXXXXXX XXXXXX care system XXXXX have the XXXXXXXXX and XXXXXXXXXXXX XX XXXXXXX XXX XXX data XXXXXXXXX and available, XXX healthcare costs XXXX drop XXXX XXXX 8%. Retailers could XXXXXXXX their XXXXXXXXXXX profits by 60% XX using the XXXX potential of XXX "big XXXX" available (XXXX, XX al, XXXX). X XXXXXXX XXX XX XXXX to XXXXXXXXXX if people XXXX XXXX its parameters. XXXX XXXXXXXXX "XXX data", XXXXXXXX XXXXXX XXXXXXXX four XXXXXXXXXX. XX's XXXXXXX XXXX XXX XXXXXX for volume but XXXXXXX XXX XXXX, "XXX XXXX" should be XXXXXXXXX by XXXXXXXXX its value, XXXXXXXX and variety (Chen, XX XX, XXXX). Not all XXXX collected faster than XXX XXXXXXXXXX speed will XXXXXX "XXX XXXX". XXXX dataset XXXXXXXX XXXXX information. XXX XXXXXXX, XXXXX may XXXXXXX the grocery XXXXXXXX preferences from XX% households XXX the order the XXXXXXX XXXX scan XXX XXXXXXXX may not XX valuable for XXX marketing XXX XXXXXXXXXX XXXX. X regular supermarket could collect a XXX XX XXXX about what and when XXXXXXXXX buy. XXX XXX managers XXXX XXXXX XXXXXX XX XX through XXX this information without using proper XXXX sorting XXX XXXXXXXXXX tools.<XX>XXXXXXXXXX<br>Working with "XXX XXXX" is XXX XXXX a XXXXXXXXX XXX XXXXXXXX developers XX come XX with XXXXXXXXX XXX platforms XXXX can XXXXXXX the XXXXXXXXXXX faster and XXXXXX but also a XXXXXXXXX XXX the entire management process. "XXX data" XXX XXXX XXXX a XXXXXXXX XXXXXX on XXX business environment. XXXXXXXXXX XXX XXXXXXXX XXXXXXX can be XXXXXXXX XX dealing in a XXXX way with XXXXXXX XXXXXXXXXX XXXX data representation XXXXXXX, data compression, XXXX life cycle, the XXXXXXXXXX XXXXXXXXX, efficiency, XXXXXXXXXXX XXX XXXXXXXXXXX (Chen, et al, XXXX). The next XXXXXXXXX XXXX briefly explain each challenge XXX demonstrate XXXX are interconnected. Data XXXXXXXXXXXXXX XXXX shape the XXXXX XX the XXXXXX XXXXXXX. The XXXX XXX XX XXXXXXXXX XXX XXXXX of XXXX representation is by analyzing XXX XXXXXX of "XXXXX" or useless XXXX. XXXX compression will reduce the XXXXXX XX repetitive XXXX revealing the same things. For XXXXXXX, marketers XXX XXXXX their XXXXXXXX database into XXXXXX segments instead XX XXXXXXXX separate data sets XXX XXXX customer. Each XXXXXX segment will include XXXXXXXXX XXXX XXXXXXX needs, expectations, and behaviors (XXXXX, XXXXXXX, 2016). The XXXXXXXX XXXXXXXXXXX is XXXX XXXXXXX and XX XXXX occasions, managers will have XX take a decision XXXX. Valid XXXXXXXXXXX plays a XXX XXXX in XXXXXX a good XXXXXXXX. Inspiration and creativity XXXX work better with strong XXX XXXXX datasets. XXXX is why business XXXXXXXXXXXXX will XXXX XX pay a XXXXX XXXXXXXXX to XXXXXXXXX XXX data XXXX cycle. XX XXXX XXXXXX, XXXXXXXX that XXX XXXX of XXX "big data" XXX XXXXXX XXXXXXXX XXX XXXXXXXX XXXXXXXXXXXXX should XXXXXXX XXXXX for XXXXXXXXX XXXX data from the decision-making XXXXXXX. For example, a XXX XXXXXX XXXX make XX XXX about the customer trends XXX preferences valid 30 years XXX. Since XXXX, XXXXXXXXXX, design, and innovations changed customer preferences. XXXXXXXX data XXXX XXXXX the business decisions. XXXXX XXXXXXXXX the obsolete XXXX, the business XXXXXXXXXXXXX should pay a great XXXXXXXXX to XXXXXXXXXX XXX XXXXX XXXXXXXXXX mechanism. XXX analytical XXXXXXXXX will XXXX the XXXX XXXX into predictions and XXXXXXXXXXX useful in the informed XXXXXXXX-XXXXXX process. XXXX, this XXXXXXXXX XXXXXX be XXXXXXXXX by XXXXX fewer resources XXXX XXX XXXXXXXX XXXXXXX XXXXXXXX XXXX implementing the business decisions. X manager XXXXXX compare the XXXXX XX developing XXX XXXXXXXXXX XXX XXXXXXXXXX XXXXX XXXX the potential market or XXXXX XXXXXXXX. XXX XXXXXXXXX market or sales benefits will depend a lot on XXX scalability of XXX analytical tool. Scalability XXXX XXXXX the XXXX analytical tool XX XX XXXXXXXXXXX in other departments or on XXXXX occasions. X XXXX XXXXXXXXXXX will reduce XXX XXXX impact and increase XXX competitive XXXXXXXXX of using "big XXXX". XXX competitive advantage XXX XX enhanced by XXXXXXXXXXX cooperation. For example, two companies from different business sectors can share their XXXXXXXXXX XXXXX for XXXXXXX XXX portrait XX XXXXX XXXXXXX customers. XXXX estate developers XXX XXXXX XXXX XXXX XXX XXXXXXXXXXXXX XXX create XXXXXXXX offers. <XX>Big data in Marketing<br>Marketing XX XXX XXXXXXXX department or XXXXXXXX where XXX XXXXXXX of using "XXX XXXX" are the XXXX XXXXXXX XXX the external environment of XXX organization. XXXXXXXXX XXXXXXXXX XXXX decide XXX XXX a business XXXXXXXXXXXX should interact with XXX XXXXXXXXX, XX part of XXX pricing, XXXXXXX, product XXX promotion strategy. XX 2013, 50% XX the marketers XXXXXXXX XXXX the IT infrastructure helps XXXX a lot in XXXXXX their decisions and 45% XXXXX that "XXX XXXX" XX an XXXXXXXXX asset in XXX XXXXXXXXXXXXX (XXXXXXXX, 2013). Both big and small business organizations XXXXXX XXXXXX in XXXXXXXX tools XXX generating XXX XXXXXXXXX "big XXXX" in XXX XXXXXXXXX process. Big companies XXXX XXXXXX have a XXXXXXXXXXX XXXXXXXXX XXXXXXX of their strong XXXXXXXXXXXXXX XXX XXXXX for collecting XXX data. Small XXXXXXXXX XXX XXXXXXXXXX with a higher level XX data representation and compression. XXX XXXXXXX XX XXXXXXXXX XX XX XXXX the XXXXXX XXXXXXX for a price XXXX XXXXXXXXX XXX XXXXXXX to pay and in a XXXXXXXXXX way XXX XXX XXXXXXXX XXXXXXXXXXXX (XXXXX, Hartley, XXXX). "XXX data" can enhance XXXX process XX revealing XXXX XXXXXXXXX want XXX how much XXX they XXXXXXX XX pay. A very XXXXXXX XXXXXX XXX collecting "XXX data" is by issuing loyalty cards. Big chain supermarket XXXXXXXXX usually handle these XXXXX to their customers XXX studies show that this will XXXX a XXXXXXX XXXXXX on XXXXXXXX retention XXX XXXXXXX levels (XXXXXXXX, Bristol, 2004). Customers XXXX receive XXXXX XXXXXXXXXX for XXXXX these XXXXXXX cards at every XXXXXXXXXXX XXXX XXXX XX the supermarket. XX XXXXXXXX, XXXXXXX these cards will not XXXX XXXXXXXXX XXXXX XXX it XXXX XXXX supermarkets XXXXXXX data about their XXXXXXXX XXXXXX and preferences (Bellizzi, XXXXXXX, XXXX). By XXXXXXXXX XXXXXXXX preferences, XXXXXXXXX XXX XXXXXXX XXX XXXXXX XXX XXX XXXXXXXX thus reducing the XXXXXXXX costs XXX increasing stock rotation. Holding XXX XXXXXXXXXXX about XXXXXXXX preferences will create a great competitive advantage for XXX XXXXXXXXX and XXXX is XXX manufacturers try to XXXXXXXXXX with XXXXX own methods of XXXXXXXXXX XXX using "big data". Some manufacturers XXXX invite customers XX XXXXXXXX XXXXX XXXXXXXX XXXXXX XXX XXXXXXX XXXX XXXXXXX discounts or extended warranty. XXXX methods will unleash new XXXXXXXXX XXXXXXXXXXXXX for XXXXXXXX organizations because collecting XXXX from XXXX individual will XXXXXXX the market XXXXXXXXXXXX efforts XXX allow XXX XXXXXXX XX come XX with personalized XXXXXX. Millennial customers usually expect XXXXXXXXXXXX offers XXX they appreciate retailers that XXXX up with XXXXXXX offers just XXX XXXX. XXXXXXXX for over one-XXXXX of XXX total population in the U.S, the Millennials XX the generic XXXX given to XXX XXXXXXXXXX born XXXXXXX XXXX and 1994 (XXXXXXXXX, XXXX). XXXX XXX XXXX the XXXXX XXXXXX XXXXXXXXXX, with all XX XXX XXXXXXX accommodated XXXX XXXXX the internet (Swarney, XXXX). XX XXXXX XXX XXXXXXXX, XXXX contribute a XXX on creating XXXXXXXX that will become XXXX XX the "big XXXX". XXXXXXXXX XXX XXX access a lot XX information about their customers XX XXXXXXXXX the XXXXXXXXXX XXX XXXXXX shopping XXXXXXX XX their customers. XX course that XXXXX activities can threaten the individual privacy levels XXX XXXX users XXXXX to XXXX in XXXXXXX XXXX in XXXXXXXX for XXXXXXX online (free) XXXXXXXX. XXX data in XXXXXXXXXX<br>"XXX XXXX" XX not only useful for XXXXXXXXX. XXXXX strategic departments from a XXXXXXX can benefit XXXX XXXXX it. Valid XXX relevant information extracted from "XXX data" will always XXXX XXX management XXXXXXX. XXXXXXX, "big XXXX" created a XXXXXXXXXX paradox (Olsson, XXXX-XXXX, XXXX). XXXX managers XXXX XXX many details XXXXX a XXXXXXX, they XXXX be XXXXXXX to XXXXXXXX a decision in hope XXXX they will find the perfect solution XXXX will XXXXX the XXXXXXXX XXXXXXXX. However, experienced managers consider XXXX a decision XXXXX XXXX XX better than XXX perfect decision taken too late. Let's XXXXXXX a XXX manufacturer XXXX developed the best fuel XXXXXXXXX XXX XXXXXXXXX XX the XXXXXX. XXXXXXX XXXX the XXXXXXX XXXXX XXXXX XXXX XXXX minor quality XXXXXX, the XXXXXXX XXXXXXX postpones XXX launch XX the XXXXXX. Meanwhile, a competitor will XXXXXX to XXXXXXX a XXXXXXX XXXXXXX, reducing or even XXXXXXXXX the competitive advantage of XXX XXX car. XXX XXXXXXXXXX will launch XXX XXXXXXX faster, moving most of XXX market XXXXXXXXX XX that business XXXXXXXXXXXX. "XXX XXXX" should XX a friend XX the management XXXXXXX XXX not a XXX. XXX way XX XX XXXX XX by XXXXXXX the challenges XXXXXXXX in the previous sections XX XXXX XXXXX under control. "XXX XXXX" XXX help XXX business process by simulating XXX market conditions, predicting XXX XXXXXXXXX XXXXXXXXX or XXXXXXXXX the manufacturing/sourcing process. XX using "big XXXX" XXXXXXXXX XXX reduce production XXXX, XXXXXXXXX XXXXX can predict XXX future sales XXX XXXX flow XXXXX XXX XXXXXXXX department can reduce XXXXXXXXX time and XXXXX. Business XXXXXXXXXXXXX can unleash XXX these benefits by learning how XX collect XXXX XXX how XX XXXXXXX it. Developers should XXXXXX their effort in XXXXXXXX "big XXXX" XXXXXXXXXX XXXXX that will link XXX XXXXXXXX information XXXX XXXXXXXXX XXXXXXXX. The XXXXXXXX XXXXXXXXXXX should XXXX XX XXX knowledge XXX it should also discover the patterns and trends revealed XX XXX XXXXXXXX management XXXXXXXXX XXXXXX, XXXX-XXXX, 2015). <XX>XXXXXXXX and privacy<XX>XXXXXXXX XXX XXXXXXX are a XXXXXXX for XXXX customers XXX XXXXXXXX organizations. XXXXX XXXXXXXXX could agree XXXX XXXXXXXXX XXX XXXXXXXXXX data about XXXXX buying XXXXXX and preferences, XXXX XX XXXX would not XXXXX XXXX XXX methods XXXXXXXX in this XXXXXXX can XXXXXXXX XXXXX XXXXXXX XXXX. XX 2012, the European XXXXX and the X.S Congress XXXXXX new legislation on customer XXXXXXX regarding "big data" (XXXXXXX, XXXX). XXXX XXXXXXXXXXX XXXXXXXX on two key XXXXXX in the new legislation: notification and XXXXXXX. People should always give XXXXX XXXXXXX before XXX business XXXXXXXXXXXXX XXX XXXXXXX XXX personal XXXX about XXXX. Business XXXXXXXXXXXXX must always XXXXXX the purpose XXXX XXX collecting the information XXX. XXX need for a XXXXXXXXXXX XXXXXX was triggered by the increasing XXXXXXXXX of "XXXX XXXXXXX" or "XXXX agencies". These XXXXXXXX XXXXXXXX XXXX Acxiom XXXXXXXXXXX managed XX XXXXXXX XXXXXXXX information from various XXXXXXX XXX XXX it in XXX place in order to create a huge database XXXX XXXXXXXX information about XXXXXXXX (Leonard, XXXX). People XXX not give XXXXX XXXXXXX XX XXXXXX for collecting and trading XXX XXXXXXX XXXXXXXXXXX XXXXX XXXX .With this XXXX XXXXXX available, XXXXXXXX organizations XXX XXXXXX to XX XXXXXX invest in XXXXXXXX their own "big data" infrastructure. XXX XXXXXXX XXXXX here is XXXX XXXXXXXXX XXX't want companies to XXX XXXXX private XXXXXXXXXXX for business purposes XXXXXXX any XXXXXXX. XXXX, XXXXXXXX the XXXXXXXXXXX from one XXXXXXXX entity XX XXXXXXX XXXX increase any security XXXXXXXX. XXXXX everyone XXX XXXX out XXXXXXXXXXX XXXXX XXXXXXXX XX XXXXXX XXX right amount XX XXXXX. Let's imagine XXX XXXXXXXXX XXXXXXX. X XXX usually XXXXXX a XXXXX supermarket XXXXX work XX buy flowers XXX a bottle of XXXX. The credit XXXX company will collect this XXXX XXX XXXX it to a data agency. The XXXX XXXXXX will XXXXXXX XXXXXXX other information XXXX XXX XXXXXXXX's gas XXXXXXX XXXXXXX card XXX XXXXX out how XXXX XXXX he XXXXX on both XXXXXXXX. The agency XXXXX the data XX a XXXXXXXXXXX supermarket XXXX will send a XXXXXX XXXX a XXXXXXXXXXXX offer to XXXX man. The man's XXXX XXXX find XXX letter XXX she XXXX XXXXX questioning her XXXXXXX about his XXXXXXXX XXXXXX XXX ask him XXXXX XXX XXX XXXX bottles XXX who received XXX flowers. XXX XXXX XXXXXXXX XXXXXXXXX XXX not XX XXXXXXXXX with XXX XXXX XXXXXXXX XXXXXXXXX. An investigation XXXXXXXXX by the XXXXXXXX XXXXX on "XXX data" XXX XXXXXXXX XXXXXXX revealed XXXX XXXX merchants XXXXXXXX XXXXXXXXXXXX, XXXXXX and XXXXXXXXXX XXXXXXXXXXX XXXXX their XXXXXXXXX in order XX use XXXXXXXXXXXXXX XXXXXXX XXXXXXXXXX (Leonard, XXXX). XXX XXXXXXX, an online store may XXXXXXXXX XXX social XXXXX of a customer XXX how XXXXX is he XX XXX a XXXXXXX XXX use automatic algorithms to XXXXXX the price of that product. Two customers XXXXXX in the XXXX area XXXXX XXX different prices XXX XXX XXXX product delivered in the same conditions. XXXX is XXX a matter of XXXXXXXXX or XXXXXXXXXXXX offers but a XXXXXX XX using "big XXXX" XXXXXXXXX against XXX XXXXXXXX's best XXXXXXXX. Readers are XXXXXXX to think XXXXX XXX XXXXXXXX example and think if it's XXXXXXX or XXX. XXX XXXXXX European XXXXXXXXXXX demands that XXXXXXXX XXXXXXXXXXXXX XXXX ask XXX consent XX the customer before collecting XXX data, XXXXXX him XXX chance XX XXXXXX. XXX XXXXXXXX cannot XXXXXX XXX XXXXXXXX order for his opposition. In XXXX, customers XXXXXXX learning XXXXX XXXXX unethical practices and XXXX XXXXX protecting XXXXXXXXXX. Some XXXXXX to disclose XXXXX XXXX XX merchants while others give in XXXXX XXXXXXXXXXX XX purpose (Kumar, Sharma, XXXX). XX course, XXXX XXXXX information XXXX alter XXX XXXX XXXXX XXX may XXXX XX wrong interpretations XXX eventually XX XXXXX XXXXXXXXXX decisions. Transparency and XXXXXXX is a solution in XXXX XXXXXXXX-XXXXXXXX XXXXXXXX. XXXX XX a XXXXXXXXXXXX where XXXX XXXXX XXXX XXXX other. XXX business organization must XXXX out XXXX XXXXXXXXX XXXX in order to stay on XXX XXXXXX. Customers XXXX to XXXX XX the XXXXXX XXX XXXX XXXXXXX or service XXXXX XXXXX XXX buy. <XX>Future perspectives<br>XXX XXXXXXXXXXXX XXXXXXXXXXX XXXXX XXX "XXX XXXX" XXXX describes a process XXXXX business organizations collect XXXX data they XXX XXXXXXX while XXXXXXXX strive XX XXXX XXX XXXXXXX this XXXXXXXXXXX in order to XXXXXXXX the XXXXXXXX process. However, XXXX XXX XXXX devices are now XXXXXXXXX XX XXX XXXXXXXX. The internet is slowly XXXXXX from the XXXX-XXXXXXX XXXXXXXXX to a XXXXX XXXXXXXXX XXXXXXXXX XXXXX XXXXXXXX XXXXXXXXX XX XXX internet XXX XXXXXXXX XXX XXXX decisions without a XXXXX controller. XXXX new approach XX called "XXX internet of things" or XXX and it already XXXXXX XX XX very helpful for organizations from the manufacturing sector (Breur, XXXX). With IoT, manufacturers XXX reduce XXXXX XXXXXXXXXX cycle, XXXXX raw XXXXXXXX waste and XXXX XXX XXXXXXXXXXX operations in XXX best XXXXXX (XXXXX, XXXX). XXXXXXXXXX XXX concept XX "big XXXX" with the XXXXXXX of IoT XXXX generate an automatic XXXXXXXX process where XXXXXXX management XXXXXXXXX are taken XX the machines. For example, the same XXXXXXXXXXX XXXXXXXXXX data about its customers XXX XXXX the XXXX-XXXX XXXXXXX demand XXXXXX XX XXX XXXXXXXX. XXX supplier XXXXX centralize this information XXX XXXX it to the XXXXXXXXXXXX where XXXXXXXXXX software applications XXXX XXXXXXXXXX XXX production cycle XXX XXXX the output XXXXXXXXXXX. XXX XXXXXXXXX XX XXX future XX XX make XXXXX XXXXXXXX XXXXXXX XXXX "big data" and XXXX XXXXXXXX decisions without a human decision.<br>When "XXX XXXX" emerged XX a XXXXX in XXX business XXXXXXXXXXX, XXXX organizations XXXXXXXX it XXXXXXXX a XXX XX XXXXXX in creating XXX means XXX collecting XXXX. Creating XXXX datasets with information about XXXXXXXXXX and XXXXXXXX was a major objective (Breur, 2015). Now, XXX XXXXXXXX XXXXXXXXX XXXXXX an XXXXXXXX amount of XXXX each second. The new emphasis XX XX the analytical tools and on helping managers XX reduce XXXXX volume XX XXXXX decisions XXX XXXXXXXX XXXXX impact. For example, business organizations XXX move XXX XXXXXXXX planning XX the XXXXXXXX XXXXX "XXX data" and taking an XXXXXXXXX decision XXX save XXXXXXXX XXX XXXXXXXXXX XXXXXXXXX XXX concentrating on XXXXXXXXX XXXXXXXXXX decisions. XXX best XXX to XXXXXXXXXX the XXXXXX XX by analyzing XXX past XXX XXX XXX things evolved. In XXX 70's, bankers would XXXXXXXX XXXX XXXXXXXX XX personally XXXXXXXXXXX XXX XXXXXX score. In XXX XX's, XXXXXXX XXXX computers XX determine the credit XXXXX. XXX computer XXXXX determine XXX credit XXXXX XX XXXXX the XXXX XXXXX provided XX XXX XXXXXX (XXXXXX, XXXX). Today, bankers can XXX XXX future borrower XX XXXXXXXX an online form with general XXXXXXXX XXX XXXXXXXXX information. By XXXXX "big data", the XXXXXXXX will instantly generate a XXXXXX score XXX XXXXXX it to the banker for approval or XXXXXXXXX. This XXXX, the computer will rate the potential XXXXXXXX XX fitting him XX a XXXXXXX XXX compare XXX XXXX XXXXX XXXXXXXXX XXXX a XXXXXXX XXXXXXX (age, location, XXXXXX, education XXX.). XXXX XXX, the borrower XXXXX even receive the XXXXXXXXX XXXXX XXXXXXXXX.<XX>XXXXXXXXXX<br>"XXX data" is a symbol on how XX infrastructures XXXXXXX in time. The new XXXXXXXXX is to understand rather than XXXXXXX. XXXX modern XXXXX XX communication, XXXXXXXXXXX XX XXXXXXXXXX. XXXXXX and XXXXXXXX organizations XXX access almost XXX XXXXXXXXX XXXXX everything. With this huge XXXXXX of XXXX XX XXXX, business XXXXXXXXXXXXX XXXX to XXXX up XXX pace and XXXXXXX their XXXXXXXXXX XXXXX. "Big XXXX" XX not XXXX XXXX XX valid or XXXXXX XXXX and XXXXXXXX organizations XXXX sort the XXXXXXXXXXX XXX XXXXXXX XXXX XXXX need XXXX it. "XXX XXXX" XX XX XXXXXX a XXXXXXXXXXX XXXXXXXXX XX XXX XXX XXXXXXXXX. Smaller companies can also XXXXXX "big XXXX" XX XXXXX cloud XXXXXXXXX XXX XXX XXXXXXXX XX a whole. XXXXXXXXX XXXXXXXXXXX XXX XXX managers are XXXXXXX XXXXX of XXX impact they XXX make XX XXXXX "big data" XXX taking informed-based decisions. XXXXXXXXXXXXX XXX XXXXXX XXXXXXXXX can XXXXXXX use "big XXXX" to their advantage. XXX questions still XXXXXX: XXXXX XXXXXX people XXX XXX XXXXX line between XXX XXXXXXXX XX using "big data" and XXXXX XXXXXXX lives. XXX XXXX XXXXXX machines "XXXXX" about XX?</p> <p>References:<XX>XXXXXXXX, J. X., & Bristol, T. (2004). XX assessment of supermarket XXXXXXX XXXXX in XXX XXXXX XX market. XXX XXXXXXX XX XXXXXXXX Marketing, XX(2), 144-XXX. Retrieved from <a XXXX="XXXX://ezproxy.XXXXXXX.XXX/login?url=http://XXXXXX.proquest.XXX.ezproxy.XXXXXXX.edu/docview/220122642?XXXXXXXXX=12085">http://ezproxy.XXXXXXX.edu/login?XXX=http://XXXXXX.XXXXXXXX.XXX.XXXXXXX.liberty.edu/docview/220122642?accounti...</a><XX>Breur, T. (XXXX). Big data and XXX XXXXXXXX XX XXXXXX. XXXXXXX XX Marketing Analytics,X(1), 1-4. XXX:<a XXXX="XXXX://dx.doi.XXX.ezproxy.liberty.XXX/XX.XXXX/XXX.2015.7">XXXX://dx.XXX.org.ezproxy.XXXXXXX.edu/10.XXXX/XXX.2015.X</a><br>Fan, S., Lau, X. X. X., & XXXX, X. X. (2015). XXXXXXXXXXXX big data XXXXXXXXX XXX business intelligence through XXX lens of a XXXXXXXXX XXX. Big XXXX Research,XXX:10.XXXX/X.bdr.XXXX.02.XXX<br>Chen, M., Mao, S., & Liu, X. (2014). XXX data: A survey. Mobile Networks XXX XXXXXXXXXXXX, 19(X), 171-209. XXX:<a XXXX="XXXX://dx.doi.XXX.XXXXXXX.liberty.XXX/10.XXXX/s11036-013-XXXX-0">XXXX://XX.XXX.org.XXXXXXX.liberty.XXX/10.XXXX/s11036-013-XXXX-0</a><br>XXXXX, R. A., & Hartley, S. W. (XXXX). XXXXXXXXX XXX XXXX (XXX ed.). New York, XX: XXXXXX-Hill XXXXXXXXX ,ISBN-13: 978-0077729035<XX>XXXXX, S., & Sharma, R. X. (2015). XXXXXXXXX analysis of XXX unethical XXXXXXXX of cookies in X-marketing. XXXXXXXX, XX(X), XX, XXXXXXXXX from thefreelibrary.com/XXXXXXXXX+analysis+XX+XXXXXXXXX+XXXXXXXX+XX+cookies+in+X-marketing.-a0440822377<XX>Olsson, N. O. X., & XXXX-Berg, X. (XXXX). Use XX XXX data in XXXXXXX XXXXXXXXXXX.International XXXXXXX of XXXXXXXX XXXXXXXX in Business, X(X), XXX-512. Retrieved from <a XXXX="XXXX://ezproxy.liberty.edu/XXXXX?url=http://XXXXXX.XXXXXXXX.XXX.XXXXXXX.liberty.XXX/XXXXXXX/1683715309?XXXXXXXXX=XXXXX">XXXX://ezproxy.liberty.edu/login?XXX=http://search.proquest.XXX.ezproxy.XXXXXXX.edu/docview/1683715309?account...</a><br>XXXXXXX, X. (XXXX). XXXXXXXX XXXX analytics: XXXXXXX XXXXXXXX XXX 'big XXXX' XXXXXXXX. International Data Privacy XXX, X(X), 53-XX. doi:<a href="XXXX://dx.XXX.org.ezproxy.liberty.edu/10.XXXX/idpl/XXXXXX">http://XX.doi.org.XXXXXXX.XXXXXXX.XXX/XX.XXXX/idpl/XXXXXX</a><br>Schiffman, L. G., & Kanuk, X. X. (XXXX). XXXXXXXX XXXXXXXX, 10th Edition. [VitalSource Bookshelf version]. ISBN-13: XXX-XXXXXXXXXX<br>XXXXXXX, X. (2006). XXXXXXXXXX behaviors and XXXXXXXXXXXX. XXXXXX: XXX Jersey XXXXXXXXX of Technology. Accessed on XXXXXXX XX, 2016, from https://XXXXX.mst.XXX/media/XXXXXXXXXXXXXX/XXXXX/documents/XXXXXXX-Millennial- XXXXXXXXX.XXX<br>XXXXXXXX, (XXXX), XXXX XXXXXXXXXXXX XX XXXXXXXXXXXX: The Data-Driven XXXXXXXXX XXXXXXXXXXX, XXXXXXXXX XXXX XXXXXX.XXXXXXXX.XXX/resourceCenter/XXXXXXXXX/XXXXXXXXXXXX/XXX-XXX-eBook.pdf?XXXXXXXXX=1<XX>XXXX-Baquero, X., XXXXXX Palacios, X., XXXXXXXXX, V., & Molloy, O. (2015). Leveraging XXX-XXXX XXX business XXXXXXX XXXXXXXXX. XXX Learning Organization, XX(4), XXX–228. XXX:10.XXXX/XXX-XX-XXXX-0023</p>">
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