Big Data and its Business Impacts
Your name
University
Class Name
December 2016
Abstract
A long time ago, ordinary people had to go through great efforts for collecting data about various topics and curiosities. Rumors, manuscripts, and folk tales were the main sources of information for everyone. Then the printing press was invented and authors started writing and spreading books about all kinds of information. In time, more and more data was available and memorizing it depended on each individual’s intellectual capacity. Today, computers can store huge amounts of data. Sometimes computers store more data than users can go through and understand. In this era of “big data”, business organizations must turn their central effort from collecting and storing information on sorting it by relevance and finding its utility. What’s relevant or not? How can business organizations make a good use of all this data? Are there any ethical issues involved?
Big Data and its Business Impacts
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.
Concept
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 XXX XXXXXXX XXXXXXX, XXXX a faster collection speed XXXX the XXX XXXXXXXXXX it and XXXX a XXXXXXX variety XXX XXXXXXX is XXXXX for taking its XXXXXXXXX (XXX, XX XX, XXXX). XXX XXXXXXX, XXXXXXX XXXXX upload 72 XXXXX of XXXXX XXXXXXX in every XXXXXX. XX XXXX, XXXXXXXX XXXXX managed to upload XXXX XXX million pictures (XXXX, Mao, XXX, XXXX). A pure theoretical deduction shows that in 48 XXXXX, XXXXXXX users will upload videos that XXXXX take XXXXXXX XX XXXXX of continuous XXXXXXXX. In practice, XXXX XX XXXXXXXXX impossible XX do XXX Youtube XX a great XXXXXX example XX what XXX XXXX XXXXX. XXXXXXX XXXXX XXXX XXXXXXX XXX XXXXXXX XXXXXXX, XXXXX will find navigating on Youtube almost XXXXXXXXXX. XXXX XXXXXXXXXX XXXXXXX XXXXX, XXXXXXXX XXXXXXXXXXXXX XXX XXXXXX a XXX XXXX data they can handle. XXXXXXX XXX technical capabilities XXX XXXXXXXXX XXX XXXXXXX XXXXXXXXXX XXXXXXXXXXXX, a XXX approach on "big XXXX" XX required. "Big XXXX" is XXXXXXXX the XXXXXXXX XXXXXXXXXXX XXX companies using XXXX information to XXX XXXX potential XXXX XXXXXX XXX XXXX XXXXXXXX XXX XXXXXXXX opportunities XXXXXX it. "XXX data" XXXXXXXXXXXX XXX dimensions are basically XXXXXXXXX. XXX example Tesco, XXX leading big-chain supermarket operator in the United Kingdom XXXXXXX XX 2015 to collect and store data about the shopping XXXXXX of XXXX 40% XX XXX British households (XXXX-XXXXXXX, Colomo Palacios, Stantchev, XXXXXX, XXXX). “Big data” XXXXXX XX distinguished XXXX “XXXXXXX XXXX” or “very XXX data”. Scholars and practitioners XXXXXX XX XXXXX a XXXXXXXXX XXXXXXXXXX XXX XXXX of XXXX agree that “big XXXX” XX a term that can XX XXXX XXX any datasets exceeding XXX processing XXXXXXXXXX XX the XXXXXXXXXX (XXXX, XX al, 2014). “XXXXXXX data” and “very big XXXX” are impressive amounts XX XXXXXXXXXXX XXX XXXXXX in a XXXXXXXXX XXXXXXXXXXX that can XXXXXXX it in a timely XXX useful manner. XXX example, a XXXX XXXXXXX XXXXXXX can collect and understand XXX daily XXXXXXX XXXXXXX and optimize its routes XX it XXXX XXXX use XXX XXXXXX XXXXXXXXXX XXXXXXXX XXXXXXXXX in every XXXXXXX. If XXX mass XXXXXXX company will also XXXXXXX real-XXXX XXXXXXX data from XXX XXXX, it XXXX XXXX a XXX XX time XXXXXX XXXXXXXXXX XXX routes using all the XXXX XXXXXXXXX. The XXXXXXXXX XXXXXXX will XXXXXXXX “XXXXXXX XXXX” XXX the XXXX-time traffic reports will XXXX it XXXX “XXX data”. XXX route managers XXXX XXXX to XXXXXX how XXX XXXX XX XXXXXXX XXXX data sources XXX XXXX is XXX real XXXXXXXXX. X XXXXXXXX XXXXXXXXXX XXXXXXXXX in X.S XXXXXXXXX XXXX XX XXX XXXXXXXX health care XXXXXX would XXXX the resources and capabilities to XXXXXXX XXX XXX data XXXXXXXXX and available, the XXXXXXXXXX costs will XXXX with XXXX X%. Retailers could increase their operational XXXXXXX XX XX% by using the full XXXXXXXXX XX the "big XXXX" XXXXXXXXX (Chen, et al, XXXX). X concept XXX be XXXX XX understand XX people will XXXX its XXXXXXXXXX. XXXX measuring “XXX XXXX”, XXXXXXXX should XXXXXXXX XXXX parameters. It’s obvious XXXX big stands for volume XXX besides XXX XXXX, “big XXXX” should be XXXXXXXXX by measuring its value, velocity and variety (XXXX, et XX, 2014). Not all XXXX collected faster XXXX the processing XXXXX will become "XXX XXXX". Each XXXXXXX XXXXXXXX waste XXXXXXXXXXX. XXX XXXXXXX, Tesco may XXXXXXX the grocery shopping preferences from 40% XXXXXXXXXX but XXX order XXX cashier will XXXX the XXXXXXXX may not XX XXXXXXXX for XXX marketing and XXXXXXXXXX team. X XXXXXXX XXXXXXXXXXX could XXXXXXX a lot of data about XXXX and when customers XXX. But XXX managers XXXX never manage to go XXXXXXX XXX this XXXXXXXXXXX without using proper XXXX XXXXXXX XXX processing tools. XXX a XXXXXX understanding XX XXX XXX XXXX XXXXXXXXXX, please XXXXX the diagram below.
Figure X. “XXX XXXX XXXXXXX”
XXXXXXXXXX
Working XXXX “XXX data” XX not XXXX a challenge XXX XXXXXXXX developers to XXXX XX with XXXXXXXXX XXX XXXXXXXXX XXXX XXX XXXXXXX XXX XXXXXXXXXXX XXXXXX and XXXXXX XXX also a XXXXXXXXX for XXX XXXXXX XXXXXXXXXX XXXXXXX. “Big data” can also XXXX XXXXXXXX XXXXXXX on XXX business XXXXXXXXXXX. Minimizing the XXXXXXXX XXXXXXX XXX XX XXXXXXXX by XXXXXXX in a XXXX way with several XXXXXXXXXX like data representation XXXXXXX, data XXXXXXXXXXX, data life XXXXX, XXX analytical XXXXXXXXX, XXXXXXXXXX, XXXXXXXXXXX and cooperation (Chen, XX al, 2014). The next paragraph XXXX briefly XXXXXXX each challenge XXX XXXXXXXXXXX XXXX XXX XXXXXXXXXXXXXX.XXXX representation XXXX XXXXX the value XX the entire dataset. XXX XXXX XXX to XXXXXXXXX XXX level XX data XXXXXXXXXXXXXX XX XX XXXXXXXXX the amount of “waste” or useless data. XXXX compression XXXX XXXXXX the amount of XXXXXXXXXX XXXX revealing the same things. XXX example, XXXXXXXXX XXX XXXXX XXXXX customer XXXXXXXX XXXX market XXXXXXXX instead of creating separate data sets XXX XXXX XXXXXXXX. XXXX market segment XXXX include XXXXXXXXX with similar XXXXX, expectations, and XXXXXXXXX (XXXXX, Hartley, XXXX). XXX business environment is XXXX dynamic XXX XX many occasions, managers will XXXX to XXXX a decision fast. XXXXX information plays a XXX XXXX in taking a good XXXXXXXX. Inspiration XXX XXXXXXXXXX will XXXX XXXXXX with strong and XXXXX datasets. This XX why business XXXXXXXXXXXXX will XXXX XX XXX a XXXXX XXXXXXXXX to XXXXXXXXX the data life cycle. In XXXX XXXXXX, XXXXXXXX that XXX XXXX of XXX "XXX data" can become XXXXXXXX XXX business XXXXXXXXXXXXX XXXXXX develop XXXXX XXX excluding XXXX data XXXX XXX XXXXXXXX-XXXXXX XXXXXXX. XXX example, a car dealer will XXXX no XXX about XXX XXXXXXXX XXXXXX and XXXXXXXXXXX XXXXX 30 years ago. XXXXX then, XXXXXXXXXX, XXXXXX, and XXXXXXXXXXX XXXXXXX customer XXXXXXXXXXX. XXXXXXXX XXXX will alter the XXXXXXXX XXXXXXXXX. XXXXX excluding the obsolete XXXX, XXX XXXXXXXX organizations should pay a great XXXXXXXXX to developing the right analytical mechanism. The analytical XXXXXXXXX XXXX XXXX the bulk data XXXX XXXXXXXXXXX and XXXXXXXXXXX XXXXXX in the informed XXXXXXXX-XXXXXX process. Also, XXXX mechanism XXXXXX XX XXXXXXXXX XX XXXXX fewer resources XXXX XXX XXXXXXXX outcome XXXXXXXX XXXX implementing XXX business decisions. A XXXXXXX XXXXXX compare XXX XXXXX of developing XXX perfecting the XXXXXXXXXX XXXXX XXXX the potential XXXXXX or sales XXXXXXXX. The potential market or sales benefits will depend a lot XX the scalability XX the analytical XXXX. Scalability will XXXXX XXX same analytical XXXX to XX implemented in XXXXX XXXXXXXXXXX or XX other occasions. X XXXX scalability will XXXXXX XXX XXXX XXXXXX XXX XXXXXXXX XXX competitive XXXXXXXXX XX XXXXX "big XXXX". XXX XXXXXXXXXXX advantage XXX be enhanced XX encouraging XXXXXXXXXXX. XXX example, XXX companies XXXX different XXXXXXXX XXXXXXX XXX XXXXX their analytical XXXXX for XXXXXXX XXX portrait XX XXXXX XXXXXXX XXXXXXXXX. XXXX XXXXXX developers XXX share data with XXX manufacturers XXX XXXXXX combined XXXXXX.
Big data in Marketing
XXXXXXXXX is the XXXXXXXX department or XXXXXXXX where the XXXXXXX of XXXXX “big XXXX” are XXX most visible XXX the XXXXXXXX XXXXXXXXXXX XX the XXXXXXXXXXXX. XXXXXXXXX decisions XXXX decide the way a XXXXXXXX organization XXXXXX interact XXXX its customers, XX XXXX XX the pricing, XXXXXXX, product and promotion strategy. In 2013, XX% of XXX XXXXXXXXX consider XXXX XXX XX XXXXXXXXXXXXXX XXXXX them a XXX in taking XXXXX XXXXXXXXX and XX% agree XXXX “XXX data” is an underused XXXXX in the organizations (Teradata, 2013).Both XXX XXX small XXXXXXXX organizations XXXXXX XXXXXX in XXXXXXXX tools for generating XXX analyzing “big data” in the marketing process. Big companies XXXX always XXXX a XXXXXXXXXXX XXXXXXXXX because of XXXXX strong infrastructure XXX XXXXX XXX XXXXXXXXXX XXX data. Small companies can XXXXXXXXXX XXXX a higher XXXXX of XXXX representation and XXXXXXXXXXX. XXX XXXXXXX of XXXXXXXXX XX to meet the market demands for a price XXXX XXXXXXXXX XXX XXXXXXX XX XXX and in a XXXXXXXXXX XXX for XXX business organization (XXXXX, XXXXXXX, 2016). “Big data” can XXXXXXX this process XX XXXXXXXXX what customers XXXX XXX how XXXX are XXXX willing XX XXX. X very popular XXXXXX for XXXXXXXXXX “big data” is XX XXXXXXX loyalty XXXXX. XXX XXXXX supermarket XXXXXXXXX usually handle XXXXX cards XX XXXXX XXXXXXXXX but studies show that this will XXXX a minimum impact on XXXXXXXX retention and XXXXXXX XXXXXX (XXXXXXXX, Bristol, XXXX). Customers will XXXXXXX XXXXX XXXXXXXXXX for using XXXXX XXXXXXX cards at every transaction they make at the XXXXXXXXXXX. In XXXXXXXX, holding XXXXX cards will XXX XXXX customers loyal but it will XXXX XXXXXXXXXXXX XXXXXXX XXXX about their purchase habits and preferences (XXXXXXXX, XXXXXXX, XXXX). By analyzing XXXXXXXX XXXXXXXXXXX, retailers XXX predict the demand XXX XXX products thus XXXXXXXX the XXXXXXXX XXXXX and increasing XXXXX XXXXXXXX. XXXXXXX key XXXXXXXXXXX XXXXX customer XXXXXXXXXXX XXXX XXXXXX a XXXXX XXXXXXXXXXX XXXXXXXXX XXX XXX retailers XXX this is why manufacturers try to compensate XXXX their own XXXXXXX XX collecting and using “big data”. Some manufacturers XXXX XXXXXX customers XX register their XXXXXXXX XXXXXX XXX benefit XXXX further discounts or XXXXXXXX warranty. XXXX XXXXXXX XXXX XXXXXXX XXX XXXXXXXXX opportunities for business XXXXXXXXXXXXX because XXXXXXXXXX XXXX XXXX XXXX XXXXXXXXXX will improve the XXXXXX segmentation XXXXXXX XXX allow XXX XXXXXXX to XXXX up with XXXXXXXXXXXX offers. XXXXXXXXXX XXXXXXXXX usually XXXXXX XXXXXXXXXXXX XXXXXX XXX XXXX appreciate XXXXXXXXX XXXX come XX XXXX XXXXXXX XXXXXX XXXX XXX them. Counting for over XXX-third XX the XXXXX population in the X.S, the Millennials is the XXXXXXX XXXX given XX the generation XXXX between XXXX XXX XXXX (Schiffman, 2010). They XXX also XXX XXXXX XXXXXX generation, with all of XXX members accommodated with using the internet (XXXXXXX, 2006). By XXXXX the internet, XXXX XXXXXXXXXX a lot on creating datasets XXXX XXXX XXXXXX XXXX XX the “big data”. Marketers can XXX access a lot XX information XXXXX XXXXX XXXXXXXXX XX XXXXXXXXX the XXXXXXXXXX and XXXXXX XXXXXXXX XXXXXXX of XXXXX XXXXXXXXX. Of course that XXXXX activities XXX XXXXXXXX XXX XXXXXXXXXX privacy XXXXXX but XXXX XXXXX agree to XXXX in private XXXX in XXXXXXXX XXX XXXXXXX online (XXXX) services.
Big XXXX in XXXXXXXXXX
“XXX XXXX” XX XXX XXXX XXXXXX XXX XXXXXXXXX. Other strategic departments XXXX a XXXXXXX XXX XXXXXXX XXXX XXXXX it. XXXXX XXX XXXXXXXX XXXXXXXXXXX extracted from “big XXXX” XXXX XXXXXX XXXX XXX XXXXXXXXXX XXXXXXX. However, “big XXXX” created a XXXXXXXXXX paradox (Olsson, Bull-Berg, XXXX). When XXXXXXXX XXXX too many XXXXXXX about a problem, XXXX XXXX XX tempted XX XXXXXXXX a XXXXXXXX in hope that XXXX XXXX XXXX XXX perfect solution XXXX XXXX XXXXX the XXXXXXXX XXXXXXXX. However, experienced XXXXXXXX consider that a XXXXXXXX taken XXXX XX XXXXXX XXXX the XXXXXXX XXXXXXXX taken too late. Let’s imagine a XXX manufacturer XXXX XXXXXXXXX the best fuel XXXXXXXXX XXX available XX the XXXXXX. XXXXXXX that the XXXXXXX XXXXX XXXXX XXXX XXXX XXXXX quality issues, XXX XXXXXXX manager postpones XXX launch XX the XXXXXX. XXXXXXXXX, a competitor will manage to XXXXXXX a similar product, XXXXXXXX or XXXX XXXXXXXXX XXX competitive advantage XX the new car. XXX competitor XXXX launch its product faster, moving most XX XXX XXXXXX XXXXXXXXX XX that business XXXXXXXXXXXX. “XXX XXXX” XXXXXX be a XXXXXX of XXX XXXXXXXXXX XXXXXXX and XXX a foe. XXX XXX XX XX XXXX XX by keeping the XXXXXXXXXX revealed in the previous XXXXXXXX XX this XXXXX under XXXXXXX. “XXX XXXX” can XXXX the business process XX XXXXXXXXXX XXX market conditions, predicting XXX financial XXXXXXXXX or XXXXXXXXX the XXXXXXXXXXXXX/XXXXXXXX process. By using “big XXXX” XXXXXXXXX XXX XXXXXX production time, XXXXXXXXX XXXXX XXX XXXXXXX XXX future sales XXX cash XXXX while XXX XXXXXXXX department XXX XXXXXX transport time XXX costs.XXXXXXXX XXXXXXXXXXXXX can unleash all XXXXX benefits XX learning how to XXXXXXX XXXX XXX how XX XXXXXXX it. XXXXXXXXXX should XXXXXX XXXXX effort in creating “XXX XXXX” analytical tools XXXX will link XXX XXXXXXXX XXXXXXXXXXX XXXX different XXXXXXXX. The XXXXXXXX XXXXXXXXXXX XXXXXX XXXX XX XXX XXXXXXXXX but it should also XXXXXXXX the XXXXXXXX and XXXXXX revealed by XXX previous management decisions XXXXXX, Bull-XXXX, 2015).
XXXXXXXX XXX XXXXXXX
XXXXXXXX and privacy XXX a XXXXXXX for XXXX customers and XXXXXXXX organizations. While XXXXXXXXX could XXXXX that companies XXX XXXXXXXXXX XXXX XXXXX their buying habits XXX preferences, most of them would XXX agree that XXX methods XXXXXXXX in this XXXXXXX can threaten XXXXX XXXXXXX XXXX. XX 2012, the XXXXXXXX Union XXX the X.S XXXXXXXX passed new legislation XX XXXXXXXX XXXXXXX XXXXXXXXX “XXX XXXX” (XXXXXXX, XXXX). Both XXXXXXXXXXX insisted on XXX key XXXXXX in the XXX legislation: notification XXX consent. XXXXXX XXXXXX XXXXXX XXXX XXXXX consent before the business organizations XXX collect XXX personal data XXXXX them. Business XXXXXXXXXXXXX XXXX XXXXXX XXXXXX the purpose they are collecting the information XXX. The need XXX a legislative change was triggered by the increasing emergence of "data brokers" or "data XXXXXXXX". These XXXXXXXX XXXXXXXX XXXX Acxiom Corporation XXXXXXX XX XXXXXXX XXXXXXXX XXXXXXXXXXX XXXX XXXXXXX sources XXX put it in one place in order to XXXXXX a huge database XXXX XXXXXXXX information about XXXXXXXX (XXXXXXX, XXXX). People did XXX give XXXXX XXXXXXX to Acxiom for collecting XXX trading the XXXXXXX information XXXXX them .With XXXX XXXX XXXXXX available, XXXXXXXX XXXXXXXXXXXXX XXX choose XX no XXXXXX XXXXXX in XXXXXXXX their own "big XXXX" infrastructure. XXX ethical XXXXX here XX that customers don't want companies XX XXX their private information XXX XXXXXXXX purposes without any consent. XXXX, XXXXXXXX the information from one business XXXXXX to XXXXXXX will increase any XXXXXXXX XXXXXXXX. Maybe XXXXXXXX can find out XXXXXXXXXXX XXXXX XXXXXXXX XX paying the XXXXX amount XX XXXXX.Let’s XXXXXXX the following XXXXXXX. A man XXXXXXX XXXXXX a XXXXX XXXXXXXXXXX XXXXX work to XXX XXXXXXX XXX a bottle XX wine. The credit card company XXXX XXXXXXX XXXX XXXX XXX sell it XX a data agency. XXX XXXX agency will further XXXXXXX other information from the XXXXXXXX’s XXX station XXXXXXX XXXX XXX finds out how much does he spend XX XXXX XXXXXXXX. XXX agency sells the data XX a competitive supermarket XXXX will send a XXXXXX XXXX a personalized XXXXX to XXXX XXX. XXX man’s wife XXXX XXXX the letter XXX she XXXX XXXXX questioning her husband XXXXX his shopping habits and XXX him XXXXX XXX XXX XXXX bottles and XXX received the XXXXXXX. The XXXX XXXXXXXX XXXXXXXXX may not be identical XXXX XXX best customer XXXXXXXXX. XX investigation XXXXXXXXX by the XXXXXXXX XXXXX XX “big data” XXX consumer privacy XXXXXXXX XXXX some XXXXXXXXX XXXXXXXX geographical, social and XXXXXXXXXX information about their XXXXXXXXX in order to use XXXXXXXXXXXXXX XXXXXXX XXXXXXXXXX (XXXXXXX, 2014). XXX XXXXXXX, an online XXXXX XXX determine the XXXXXX class of a customer and how XXXXX is he to buy a product and XXX XXXXXXXXX XXXXXXXXXX XX change XXX XXXXX XX that product. Two XXXXXXXXX living in XXX same XXXX could see XXXXXXXXX prices XXX the same product XXXXXXXXX in XXX same conditions. This XX XXX a matter XX marketing or XXXXXXXXXXXX offers XXX a XXXXXX XX XXXXX “big data” XXXXXXXXX XXXXXXX XXX XXXXXXXX’s best interest. XXXXXXX XXX invited to think XXXXX the previous XXXXXXX XXX XXXXX XX it’s ethical or XXX. The latest XXXXXXXX XXXXXXXXXXX demands XXXX business XXXXXXXXXXXXX XXXX ask the consent XX the customer before collecting XXX data, giving him XXX XXXXXX XX oppose. The XXXXXXXX XXXXXX refuse the customer order XXX his opposition. In time, customers started XXXXXXXX XXXXX XXXXX unethical XXXXXXXXX XXX XXXX began protecting themselves. Some XXXXXX to XXXXXXXX XXXXX data XX XXXXXXXXX while XXXXXX give in XXXXX information on XXXXXXX (Kumar, XXXXXX, 2015). XX XXXXXX, that false information XXXX alter the XXXX value XXX may XXXX XX XXXXX interpretations and eventually XX wrong management XXXXXXXXX. XXXXXXXXXXXX XXX honesty is a solution in this XXXXXXXX-XXXXXXXX conflict. XXXX is a relationship where both sides XXXX XXXX other. XXX business XXXXXXXXXXXX XXXX XXXX out what XXXXXXXXX want in order XX stay on the XXXXXX. XXXXXXXXX want to XXXX on the XXXXXX XXX XXXX XXXXXXX or service XXXXX XXXXX XXX XXX.
Future perspectives
XXX XXXXXXXXXXXX perspective about XXX “big XXXX” XXXX XXXXXXXXX a process XXXXX business organizations collect more XXXX XXXX XXX process XXXXX XXXXXXXX strive to XXXX and analyze XXXX XXXXXXXXXXX in order to XXXXXXXX XXX XXXXXXXX XXXXXXX. However, XXXX and more XXXXXXX XXX XXX connected XX XXX XXXXXXXX. XXX XXXXXXXX is XXXXXX XXXXXX XXXX the XXXX-machine interface to a XXXXX automatic XXXXXXXXX where XXXXXXXX connected on the XXXXXXXX can interact XXX XXXX XXXXXXXXX without a XXXXX controller. This new XXXXXXXX is called “XXX internet XX things” or IoT and it XXXXXXX XXXXXX to be XXXX helpful for XXXXXXXXXXXXX XXXX the manufacturing XXXXXX (XXXXX, XXXX). XXXX IoT, manufacturers XXX reduce their production XXXXX, XXXXX XXX material XXXXX XXX plan XXX XXXXXXXXXXX operations in the best moment (XXXXX, XXXX). XXXXXXXXXX XXX concept XX “XXX XXXX” XXXX the concept XX XXX will generate an automatic business process XXXXX regular management XXXXXXXXX are XXXXX by the machines. XXX example, XXX same supermarket XXXXXXXXXX data XXXXX XXX XXXXXXXXX XXX XXXX the XXXX-time product demand curves to XXX supplier. The supplier XXXXX centralize XXXX XXXXXXXXXXX XXX send it XX the XXXXXXXXXXXX where enterprise XXXXXXXX applications XXXX XXXXXXXXXX XXX XXXXXXXXXX cycle XXX tune XXX output accordingly. XXX challenge of the XXXXXX XX to make these XXXXXXXX operate with "big data" and XXXX XXXXXXXX XXXXXXXXX without a human XXXXXXXX.When "XXX XXXX" XXXXXXX as a trend in XXX business XXXXXXXXXXX, XXXX XXXXXXXXXXXXX XXXXXXXX it XXXXXXXX a XXX XX effort in creating XXX means XXX collecting data. Creating huge datasets with information XXXXX XXXXXXXXXX XXX XXXXXXXX XXX a major XXXXXXXXX (XXXXX, 2015). XXX, the internet XXXXXXXXX almost an infinite amount XX data each XXXXXX. XXX new XXXXXXXX XX XX the XXXXXXXXXX XXXXX and XX helping managers to XXXXXX XXXXX volume of daily XXXXXXXXX XXX XXXXXXXX XXXXX XXXXXX. For XXXXXXX, business organizations can XXXX the XXXXXXXX planning to the XXXXXXXX XXXXX "big data" XXX taking an automatic decision XXX XXXX manpower XXX managerial resources XXX concentrating XX strategic XXXXXXXXXX XXXXXXXXX. XXX XXXX XXX to XXXXXXXXXX the XXXXXX XX by XXXXXXXXX the past XXX the way things evolved. XX the XX’s, XXXXXXX XXXXX evaluate loan XXXXXXXX by personally calculating the XXXXXX XXXXX. In the 90’s, bankers used computers to XXXXXXXXX the credit XXXXX. XXX XXXXXXXX XXXXX determine the XXXXXX XXXXX XX XXXXX XXX data input XXXXXXXX by the banker (XXXXXX, 2015). XXXXX, XXXXXXX can ask XXX future XXXXXXXX XX complete an online form XXXX XXXXXXX personal and financial information. XX using “XXX data”, XXX XXXXXXXX will XXXXXXXXX generate a credit score XXX XXXXXX it to XXX banker for approval or XXXXXXXXX. XXXX XXXX, the XXXXXXXX will rate XXX XXXXXXXXX customer XX fitting XXX XX a pattern XXX compare him with XXXXX XXXXXXXXX with a XXXXXXX XXXXXXX (age, location, XXXXXX, education XXX.). With XXX, XXX XXXXXXXX could XXXX receive the XXXXXXXXX XXXXX XXXXXXXXX.
Conclusion
“Big XXXX” XX a symbol on how XX infrastructures evolved in XXXX. XXX new XXXXXXXXX XX XX XXXXXXXXXX XXXXXX than knowing. With modern XXXXX XX XXXXXXXXXXXXX, XXXXXXXXXXX XX everywhere. People XXX XXXXXXXX organizations XXX access XXXXXX XXX XXXXXXXXX about XXXXXXXXXX. XXXX this XXXX XXXXXX of data XX hand, XXXXXXXX XXXXXXXXXXXXX XXXX to XXXX XX XXX XXXX and XXXXXXX XXXXX XXXXXXXXXX XXXXX. “Big XXXX” XX not XXXX made XX XXXXX or XXXXXX data and XXXXXXXX organizations must XXXX XXX XXXXXXXXXXX and extract XXXX they need XXXX it. “Big XXXX” is no XXXXXX a XXXXXXXXXXX advantage XX the XXX XXXXXXXXX. Smaller XXXXXXXXX can XXXX XXXXXX “big XXXX” XX XXXXX XXXXX XXXXXXXXX and the XXXXXXXX XX a XXXXX. XXXXXXXXX specialists and XXX managers are XXXXXXX XXXXX XX the impact XXXX can XXXX XX XXXXX “XXX data” for XXXXXX informed-based decisions. Manufacturing and XXXXXX companies XXX already XXX "big XXXX" to XXXXX advantage. Two questions XXXXX remain: XXXXX XXXXXX XXXXXX put XXX XXXXX line XXXXXXX XXX XXXXXXXX XX XXXXX "XXX data" and their XXXXXXX lives. How much should machines "learn" about us? This XXXXXX be a topic XXX a XXXXXXX XXXXX about “big XXXX”.
References:
Bellizzi, X. X., &XXX; Bristol, X. (2004).An assessment XX supermarket loyalty cards in XXX major XX market. XXX Journal XX XXXXXXXX Marketing, XX(2), 144-154. Retrieved fromXXXXXXX.liberty.XXX/login?url=http://search.proquest.com.ezproxy.XXXXXXX.XXX/docview/220122642?accountid=12085
Breur, T. (2015). Big XXXX XXX XXX internet XX XXXXXX. XXXXXXX of XXXXXXXXX Analytics,3(X), X-X. doi:XXXX://dx.doi.XXX.XXXXXXX.liberty.edu/10.XXXX/jma.XXXX.X
XXX, S., Lau, X. X. X., & Zhao, J. X. (2015). XXXXXXXXXXXX big data analytics XXX XXXXXXXX intelligence through the lens XX a marketing mix. Big XXXX Research,XXX:XX.1016/X.XXX.XXXX.XX.XXX
XXXX, X., XXX, S., & XXX, Y. (2014). XXX XXXX: A XXXXXX. XXXXXX XXXXXXXX XXX Applications, XX(2), 171-209. doi:http://XX.XXX.XXX.XXXXXXX.XXXXXXX.XXX/XX.XXXX/XXXXXX-XXX-0489-X
Kerin, X. A., &XXX; XXXXXXX, S. X. (XXXX). XXXXXXXXX XXX XXXX (6th ed.). XXX XXXX, XX: XXXXXX-XXXX XXXXXXXXX ,XXXX-13: XXX-XXXXXXXXXX
XXXXX, S., & XXXXXX, X. X. (XXXX). Empirical analysis of XXX unethical practice of XXXXXXX in X-XXXXXXXXX. XXXXXXXX, 33(3), XX, retrieved from thefreelibrary.com/XXXXXXXXX+XXXXXXXX+of+XXXXXXXXX+practice+XX+XXXXXXX+in+E-marketing.-a0440822377
XXXXXX, N. O. E., &XXX; Bull-XXXX, X. (2015). XXX of big data in project evaluations.XXXXXXXXXXXXX Journal of XXXXXXXX XXXXXXXX in Business, X(3), 491-512. XXXXXXXXX from XXXXXXX.XXXXXXX.XXX/login?url=XXXX://search.proquest.XXX.XXXXXXX.liberty.edu/docview/1683715309?accountid=12085
Leonard, X. (XXXX). XXXXXXXX XXXX analytics: Privacy settings XXX 'big XXXX' XXXXXXXX. International Data Privacy XXX, X(X), XX-68. XXX:XXXX://dx.XXX.org.ezproxy.liberty.XXX/10.1093/idpl/ipt032
Schiffman, X. G., & XXXXX, L. X. (XXXX). XXXXXXXX Behavior, 10th Edition. [VitalSource Bookshelf XXXXXXX].XXXX-13: XXX-XXXXXXXXXX
XXXXXXX, R. (XXXX). XXXXXXXXXX XXXXXXXXX XXX XXXXXXXXXXXX. Newark: New XXXXXX Institute XX Technology. Accessed on January 17, XXXX, XXXX XXXXX://certi.XXX.edu/XXXXX/administrative/XXXXX/documents/Article-XXXXXXXXXX-XXXXXXXXX.XXX
XXXXXXXX, (XXXX), From Transactions to Interactions: XXX Data-Driven Marketing XXXXXXXXXXX, retrieved from assets.XXXXXXXX.XXX/resourceCenter/downloads/XXXXXXXXXXXX/TDC-XXX-XXXXX.XXX?XXXXXXXXX=X
Vera-Baquero, X., Colomo Palacios, X., Stantchev, X., &XXX; Molloy, O. (2015). Leveraging XXX-XXXX XXX XXXXXXXX process analytics. The Learning Organization, 22(X), XXX–228. doi:XX.1108/XXX-XX-XXXX-XXXX