IntroductionDigitalized 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.
ConceptBefore 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 dimensions are basically XXXXXXXXX. XXX XXXXXXX Tesco, the XXXXXXX big-XXXXX XXXXXXXXXXX operator in XXX XXXXXX Kingdom XXXXXXX XX 2015 XX collect and XXXXX XXXX XXXXX the shopping habits of XXXX 40% of XXX XXXXXXX households (XXXX-XXXXXXX, XXXXXX XXXXXXXX, XXXXXXXXX, Molloy, XXXX). "Big XXXX" XXXXXX XX XXXXXXXXXXXXX from "XXXXXXX XXXX" or "very big data". XXXXXXXX XXX practitioners XXXXXX XX XXXXX a XXXXXXXXX XXXXXXXXXX but most XX XXXX agree that "big data" is a term XXXX XXX be XXXX XXX XXX XXXXXXXX exceeding the processing XXXXXXXXXX XX the XXXXXXXXXX (XXXX, XX al, 2014). "Massive XXXX" XXX "XXXX big data" XXX impressive XXXXXXX of information XXX placed in a technical XXXXXXXXXXX that XXX process it in a timely XXX XXXXXX manner. XXX XXXXXXX, a XXXX transit XXXXXXX can collect XXX XXXXXXXXXX XXX daily XXXXXXX reports XXX XXXXXXXX XXX routes XX it will only XXX the ticket XXXXXXXXXX machines available in every XXXXXXX. XX the XXXX XXXXXXX XXXXXXX XXXX XXXX collect XXXX-time traffic data from XXX XXXX, it will need a XXX XX XXXX before XXXXXXXXXX the XXXXXX using XXX XXX data XXXXXXXXX. XXX ticketing reports XXXX generate "massive data" but the real-XXXX traffic reports will XXXX it XXXX "XXX data". XXX route managers will have to XXXXXX how and XXXX XX XXXXXXX XXXX XXXX XXXXXXX XXX this is the XXXX challenge. A XXXXXXXX simulation XXXXXXXXX in X.S XXXXXXXXX XXXX if the national XXXXXX XXXX XXXXXX would XXXX XXX resources XXX capabilities to process all the data XXXXXXXXX XXX XXXXXXXXX, XXX healthcare XXXXX will XXXX with XXXX 8%. XXXXXXXXX XXXXX XXXXXXXX XXXXX operational XXXXXXX by 60% XX XXXXX the full XXXXXXXXX of the "big data" XXXXXXXXX (XXXX, et al, XXXX). X XXXXXXX can XX easy XX understand XX XXXXXX will know XXX XXXXXXXXXX. XXXX measuring "big XXXX", managers XXXXXX consider four parameters. XX's obvious that XXX stands XXX XXXXXX but besides its XXXX, "big data" XXXXXX be XXXXXXXXX XX measuring its XXXXX, velocity and XXXXXXX (Chen, et XX, XXXX). Not XXX XXXX collected XXXXXX than XXX XXXXXXXXXX XXXXX XXXX become "XXX data". XXXX dataset contains waste information. XXX XXXXXXX, Tesco may XXXXXXX XXX XXXXXXX shopping XXXXXXXXXXX XXXX XX% households but the order XXX XXXXXXX XXXX XXXX XXX XXXXXXXX may not be valuable XXX XXX marketing and XXXXXXXXXX team. X regular XXXXXXXXXXX XXXXX XXXXXXX a XXX XX XXXX XXXXX XXXX XXX XXXX customers buy. But XXX managers will never XXXXXX XX XX XXXXXXX XXX XXXX information without using proper XXXX sorting XXX processing XXXXX.XXXXXXXXXXWorking XXXX "XXX data" XX XXX just a challenge XXX XXXXXXXX XXXXXXXXXX XX come up XXXX mechanism XXX XXXXXXXXX XXXX XXX XXXXXXX the information faster XXX XXXXXX XXX XXXX a XXXXXXXXX XXX XXX XXXXXX management XXXXXXX. "Big data" can XXXX have a negative XXXXXX XX the business XXXXXXXXXXX. Minimizing XXX XXXXXXXX XXXXXXX can XX achieved by XXXXXXX in a XXXX way XXXX XXXXXXX challenges XXXX XXXX XXXXXXXXXXXXXX methods, data compression, XXXX life XXXXX, XXX XXXXXXXXXX XXXXXXXXX, XXXXXXXXXX, scalability XXX cooperation (Chen, XX al, 2014). XXX next paragraph will briefly XXXXXXX XXXX XXXXXXXXX XXX XXXXXXXXXXX XXXX XXX interconnected. Data XXXXXXXXXXXXXX XXXX XXXXX the XXXXX XX the entire dataset. The best way to XXXXXXXXX XXX XXXXX XX XXXX representation XX by XXXXXXXXX the amount XX "waste" or useless XXXX. XXXX compression will reduce XXX XXXXXX of XXXXXXXXXX data revealing XXX same XXXXXX. For example, marketers can split their customer XXXXXXXX XXXX XXXXXX segments instead XX creating XXXXXXXX XXXX XXXX for XXXX XXXXXXXX. Each XXXXXX segment XXXX XXXXXXX XXXXXXXXX with similar XXXXX, XXXXXXXXXXXX, and XXXXXXXXX (Kerin, XXXXXXX, XXXX). The business environment XX XXXX XXXXXXX and XX XXXX XXXXXXXXX, XXXXXXXX will have to XXXX a XXXXXXXX XXXX. Valid XXXXXXXXXXX XXXXX a XXX XXXX in taking a good XXXXXXXX. XXXXXXXXXXX and XXXXXXXXXX will XXXX better XXXX strong XXX XXXXX datasets. XXXX is why XXXXXXXX organizations XXXX XXXX XX pay a XXXXX attention to XXXXXXXXX XXX XXXX XXXX cycle. XX each XXXXXX, XXXXXXXX that are XXXX XX XXX "big XXXX" XXX XXXXXX obsolete XXX XXXXXXXX organizations XXXXXX XXXXXXX tools XXX excluding this XXXX from the decision-XXXXXX process. XXX XXXXXXX, a XXX dealer will XXXX no XXX about XXX customer trends XXX preferences valid 30 years ago. Since XXXX, XXXXXXXXXX, XXXXXX, and XXXXXXXXXXX changed customer preferences. Obsolete data will alter XXX XXXXXXXX decisions. XXXXX XXXXXXXXX XXX XXXXXXXX data, XXX XXXXXXXX XXXXXXXXXXXXX should pay a XXXXX attention to developing the right XXXXXXXXXX XXXXXXXXX. XXX XXXXXXXXXX XXXXXXXXX XXXX turn XXX bulk XXXX XXXX XXXXXXXXXXX XXX information XXXXXX in the informed decision-XXXXXX XXXXXXX. Also, XXXX mechanism XXXXXX be XXXXXXXXX XX using fewer resources XXXX the positive XXXXXXX XXXXXXXX from XXXXXXXXXXXX XXX XXXXXXXX decisions. A manager XXXXXX XXXXXXX the XXXXX of developing and XXXXXXXXXX the analytical tools XXXX the XXXXXXXXX market or XXXXX XXXXXXXX. The potential XXXXXX or sales XXXXXXXX will depend a lot XX XXX scalability XX XXX analytical tool. Scalability will allow XXX XXXX XXXXXXXXXX XXXX XX XX XXXXXXXXXXX in XXXXX XXXXXXXXXXX or XX XXXXX occasions. X high scalability XXXX reduce XXX XXXX XXXXXX XXX increase XXX competitive XXXXXXXXX of using "big XXXX". The competitive XXXXXXXXX XXX XX enhanced XX XXXXXXXXXXX cooperation. For XXXXXXX, XXX companies XXXX different business XXXXXXX XXX XXXXX XXXXX XXXXXXXXXX XXXXX for drawing the portrait XX XXXXX XXXXXXX XXXXXXXXX. Real XXXXXX developers XXX share data XXXX XXX manufacturers XXX XXXXXX combined XXXXXX. XXX XXXX in MarketingMarketing XX XXX business XXXXXXXXXX or XXXXXXXX XXXXX XXX XXXXXXX of XXXXX "XXX data" are the most visible XXX the XXXXXXXX XXXXXXXXXXX XX the organization. Marketing XXXXXXXXX XXXX XXXXXX the XXX a business organization should XXXXXXXX with its XXXXXXXXX, XX part of the XXXXXXX, XXXXXXX, product and XXXXXXXXX strategy. In XXXX, XX% of XXX marketers consider that the IT infrastructure XXXXX them a XXX in taking their decisions and 45% agree XXXX "XXX data" is an XXXXXXXXX asset in XXX XXXXXXXXXXXXX (Teradata, 2013). Both XXX and small XXXXXXXX XXXXXXXXXXXXX XXXXXX invest in XXXXXXXX XXXXX XXX generating XXX XXXXXXXXX "XXX data" in XXX marketing XXXXXXX. XXX companies XXXX XXXXXX have a XXXXXXXXXXX advantage XXXXXXX of their XXXXXX infrastructure and XXXXX XXX XXXXXXXXXX XXX data. XXXXX companies can compensate with a higher XXXXX of data XXXXXXXXXXXXXX XXX compression. The essence XX XXXXXXXXX XX to XXXX the market demands for a price that customers XXX willing to pay XXX in a profitable XXX for XXX business organization (XXXXX, XXXXXXX, XXXX). "XXX XXXX" XXX enhance this process XX revealing XXXX customers want XXX how XXXX XXX they XXXXXXX XX pay. X XXXX popular XXXXXX XXX collecting "big XXXX" is XX XXXXXXX XXXXXXX cards. Big chain XXXXXXXXXXX operators usually XXXXXX XXXXX cards to their customers but studies show XXXX this XXXX XXXX a XXXXXXX impact on customer retention XXX XXXXXXX XXXXXX (Bellizzi, XXXXXXX, 2004). XXXXXXXXX XXXX XXXXXXX small XXXXXXXXXX for using these loyalty XXXXX at XXXXX transaction they XXXX at the XXXXXXXXXXX. In practice, holding XXXXX cards will XXX XXXX XXXXXXXXX loyal XXX it will help XXXXXXXXXXXX XXXXXXX XXXX about their purchase XXXXXX XXX XXXXXXXXXXX (XXXXXXXX, XXXXXXX, XXXX). XX XXXXXXXXX customer XXXXXXXXXXX, retailers can predict XXX XXXXXX for XXX XXXXXXXX thus reducing the XXXXXXXX XXXXX XXX XXXXXXXXXX stock rotation. Holding key information about customer preferences XXXX create a XXXXX competitive advantage XXX the XXXXXXXXX and XXXX is why XXXXXXXXXXXXX XXX to XXXXXXXXXX XXXX their own methods of collecting XXX using "XXX data". XXXX XXXXXXXXXXXXX will invite customers XX XXXXXXXX their products online XXX XXXXXXX from XXXXXXX discounts or XXXXXXXX warranty. XXXX XXXXXXX will XXXXXXX XXX XXXXXXXXX opportunities XXX XXXXXXXX XXXXXXXXXXXXX because XXXXXXXXXX data from each XXXXXXXXXX XXXX XXXXXXX XXX market XXXXXXXXXXXX XXXXXXX and allow XXX company to come up with XXXXXXXXXXXX offers. XXXXXXXXXX XXXXXXXXX XXXXXXX expect personalized offers and they XXXXXXXXXX XXXXXXXXX XXXX XXXX XX with XXXXXXX XXXXXX XXXX XXX them. XXXXXXXX for XXXX XXX-XXXXX of XXX XXXXX population in XXX X.S, the Millennials is the generic name XXXXX XX the XXXXXXXXXX born between 1977 and 1994 (Schiffman, XXXX). XXXX are XXXX XXX first XXXXXX generation, with all of its members accommodated with using XXX internet (Swarney, 2006). XX XXXXX the XXXXXXXX, XXXX XXXXXXXXXX a XXX on XXXXXXXX datasets XXXX will XXXXXX part XX the "XXX XXXX". XXXXXXXXX can XXX XXXXXX a XXX XX XXXXXXXXXXX about their XXXXXXXXX by XXXXXXXXX the XXXXXXXXXX XXX XXXXXX XXXXXXXX history of their customers. XX course that XXXXX XXXXXXXXXX can XXXXXXXX the individual XXXXXXX levels XXX most users XXXXX to give in private data in XXXXXXXX for XXXXXXX online (free) services. XXX data in management"Big XXXX" XX XXX XXXX XXXXXX for marketers. Other XXXXXXXXX XXXXXXXXXXX XXXX a XXXXXXX can benefit XXXX XXXXX it. XXXXX and XXXXXXXX XXXXXXXXXXX extracted XXXX "XXX XXXX" will always XXXX the management XXXXXXX. However, "XXX XXXX" created a XXXXXXXXXX XXXXXXX (XXXXXX, Bull-XXXX, XXXX). XXXX XXXXXXXX XXXX XXX XXXX XXXXXXX about a problem, they will be XXXXXXX XX postpone a decision in hope that they XXXX XXXX the perfect solution that will XXXXX XXX XXXXXXXX XXXXXXXX. However, XXXXXXXXXXX XXXXXXXX XXXXXXXX XXXX a XXXXXXXX taken fast is better XXXX XXX XXXXXXX decision taken XXX late. Let's imagine a XXX XXXXXXXXXXXX that XXXXXXXXX XXX XXXX XXXX efficient car available on the market. Knowing that the product XXXXX still have XXXX minor XXXXXXX issues, XXX XXXXXXX manager XXXXXXXXX XXX XXXXXX on XXX market. Meanwhile, a XXXXXXXXXX XXXX manage XX develop a similar XXXXXXX, XXXXXXXX or XXXX canceling XXX XXXXXXXXXXX XXXXXXXXX XX XXX XXX XXX. The XXXXXXXXXX XXXX XXXXXX its product faster, moving most XX the XXXXXX XXXXXXXXX XX XXXX XXXXXXXX XXXXXXXXXXXX. "Big XXXX" should XX a friend of XXX XXXXXXXXXX XXXXXXX XXX not a foe. The way XX XX this XX XX keeping XXX XXXXXXXXXX XXXXXXXX in XXX XXXXXXXX XXXXXXXX of XXXX XXXXX XXXXX XXXXXXX. "Big XXXX" XXX XXXX the business XXXXXXX XX XXXXXXXXXX XXX XXXXXX XXXXXXXXXX, predicting XXX financial XXXXXXXXX or improving the XXXXXXXXXXXXX/sourcing XXXXXXX. XX XXXXX "XXX XXXX" factories can XXXXXX XXXXXXXXXX time, financial XXXXX can predict XXX XXXXXX sales XXX cash XXXX while XXX XXXXXXXX department can reduce transport time XXX XXXXX. XXXXXXXX organizations can unleash all these benefits by learning how XX XXXXXXX XXXX and how XX analyze it. Developers XXXXXX XXXXXX their effort in creating "XXX XXXX" XXXXXXXXXX XXXXX XXXX XXXX link XXX relevant XXXXXXXXXXX XXXX different datasets. XXX combined XXXXXXXXXXX XXXXXX XXXX to XXX knowledge but it XXXXXX also discover XXX XXXXXXXX XXX trends revealed XX XXX previous XXXXXXXXXX decisions XXXXXX, Bull-Berg, XXXX). Security XXX XXXXXXXXXXXXXXX XXX XXXXXXX are a concern for both customers and XXXXXXXX organizations. While XXXXXXXXX could XXXXX that XXXXXXXXX XXX collecting XXXX XXXXX XXXXX XXXXXX XXXXXX and preferences, most of them XXXXX XXX XXXXX that XXX XXXXXXX involved in this process can threaten XXXXX private XXXX. XX 2012, the XXXXXXXX XXXXX and XXX X.S Congress passed XXX XXXXXXXXXXX on customer privacy XXXXXXXXX "big XXXX" (XXXXXXX, XXXX). Both legislators XXXXXXXX XX two key XXXXXX in XXX new legislation: XXXXXXXXXXXX XXX XXXXXXX. XXXXXX XXXXXX XXXXXX give XXXXX XXXXXXX XXXXXX the XXXXXXXX organizations can XXXXXXX XXX personal data about them. XXXXXXXX organizations must XXXXXX reveal the XXXXXXX they are collecting XXX XXXXXXXXXXX XXX. XXX need for a XXXXXXXXXXX XXXXXX was triggered XX the XXXXXXXXXX emergence XX "XXXX brokers" or "data XXXXXXXX". These business XXXXXXXX XXXX Acxiom Corporation XXXXXXX XX collect customer information from XXXXXXX sources and put it in XXX place in order to create a XXXX database with personal information about XXXXXXXX (Leonard, XXXX). People XXX XXX XXXX their consent XX XXXXXX XXX collecting and XXXXXXX the private XXXXXXXXXXX about them .XXXX this data XXXXXX XXXXXXXXX, XXXXXXXX XXXXXXXXXXXXX can choose to XX longer XXXXXX in XXXXXXXX XXXXX own "big data" infrastructure. XXX XXXXXXX issue here is that XXXXXXXXX don't want companies to XXX their XXXXXXX information XXX business XXXXXXXX XXXXXXX any XXXXXXX. Also, XXXXXXXX the XXXXXXXXXXX XXXX one business XXXXXX XX XXXXXXX XXXX increase any XXXXXXXX concerns. XXXXX XXXXXXXX XXX XXXX out XXXXXXXXXXX XXXXX XXXXXXXX by paying the XXXXX amount of money. XXX's XXXXXXX the following example. A XXX XXXXXXX XXXXXX a XXXXX supermarket after XXXX XX XXX flowers and a XXXXXX of wine. The XXXXXX XXXX company will collect XXXX data XXX XXXX it XX a data XXXXXX. The XXXX XXXXXX XXXX further XXXXXXX other XXXXXXXXXXX from the XXXXXXXX's XXX XXXXXXX loyalty XXXX XXX XXXXX out how much does he spend on XXXX products. The XXXXXX sells the XXXX to a competitive supermarket XXXX will send a XXXXXX with a XXXXXXXXXXXX XXXXX XX that man. XXX man's wife will find the letter and she will XXXXX questioning her XXXXXXX XXXXX his XXXXXXXX habits XXX XXX XXX where are XXX XXXX XXXXXXX XXX XXX XXXXXXXX the XXXXXXX. The best XXXXXXXX XXXXXXXXX may not XX XXXXXXXXX with the best XXXXXXXX XXXXXXXXX. XX investigation conducted by XXX XXXXXXXX XXXXX XX "XXX data" and consumer XXXXXXX revealed that XXXX XXXXXXXXX XXXXXXXX XXXXXXXXXXXX, social XXX XXXXXXXXXX information XXXXX their customers in order XX use discriminatory XXXXXXX XXXXXXXXXX (Leonard, XXXX). For XXXXXXX, an online store may XXXXXXXXX XXX XXXXXX XXXXX of a XXXXXXXX XXX how eager XX he to buy a XXXXXXX XXX use XXXXXXXXX XXXXXXXXXX XX change XXX XXXXX of XXXX XXXXXXX. Two customers XXXXXX in the same area could XXX different prices XXX the same product XXXXXXXXX in the XXXX XXXXXXXXXX. XXXX XX XXX a XXXXXX XX XXXXXXXXX or personalized XXXXXX XXX a XXXXXX of using "XXX data" XXXXXXXXX against the customer's best XXXXXXXX. Readers XXX XXXXXXX to XXXXX XXXXX XXX XXXXXXXX example XXX think if it's ethical or not. The XXXXXX XXXXXXXX XXXXXXXXXXX demands that XXXXXXXX XXXXXXXXXXXXX will XXX XXX XXXXXXX of XXX customer before collecting XXX XXXX, XXXXXX him the chance XX XXXXXX. The merchant XXXXXX XXXXXX the XXXXXXXX order for XXX XXXXXXXXXX. In XXXX, customers started XXXXXXXX XXXXX these XXXXXXXXX XXXXXXXXX XXX XXXX XXXXX XXXXXXXXXX XXXXXXXXXX. XXXX XXXXXX XX XXXXXXXX their XXXX XX merchants XXXXX others XXXX in XXXXX XXXXXXXXXXX on XXXXXXX (XXXXX, Sharma, 2015). Of course, that false XXXXXXXXXXX XXXX XXXXX XXX data XXXXX and may lead XX wrong interpretations and XXXXXXXXXX XX XXXXX management decisions. Transparency and honesty is a solution in this XXXXXXXX-XXXXXXXX conflict. XXXX is a relationship XXXXX both XXXXX need each other. The business XXXXXXXXXXXX XXXX XXXX out XXXX customers XXXX in order to stay on the XXXXXX. XXXXXXXXX XXXX XX find on XXX market XXX XXXX XXXXXXX or XXXXXXX their money can XXX. Future perspectivesThe XXXXXXXXXXXX perspective about the "big data" XXXX describes a process where XXXXXXXX XXXXXXXXXXXXX XXXXXXX more data they XXX XXXXXXX XXXXX XXXXXXXX XXXXXX to XXXX and analyze this XXXXXXXXXXX in order to optimize the XXXXXXXX process. XXXXXXX, more XXX XXXX devices XXX XXX connected to the XXXXXXXX. XXX internet is slowly moving XXXX the XXXX-machine XXXXXXXXX XX a XXXXX XXXXXXXXX XXXXXXXXX where machines XXXXXXXXX on XXX XXXXXXXX can XXXXXXXX XXX XXXX XXXXXXXXX without a human controller. This XXX approach is called "the XXXXXXXX of things" or XXX and it XXXXXXX proved to XX XXXX XXXXXXX XXX XXXXXXXXXXXXX XXXX XXX XXXXXXXXXXXXX sector (XXXXX, 2015). With XXX, XXXXXXXXXXXXX XXX XXXXXX XXXXX production cycle, XXXXX XXX material waste and XXXX XXX XXXXXXXXXXX operations in the XXXX moment (Breur, XXXX). Connecting XXX XXXXXXX of "big XXXX" with the concept XX IoT XXXX XXXXXXXX an XXXXXXXXX business process where XXXXXXX management decisions XXX XXXXX by XXX XXXXXXXX. For XXXXXXX, the XXXX supermarket XXXXXXXXXX XXXX XXXXX XXX XXXXXXXXX can XXXX the real-XXXX product demand XXXXXX to XXX supplier. The XXXXXXXX would XXXXXXXXXX XXXX information XXX XXXX it XX the manufacturer XXXXX enterprise software applications will XXXXXXXXXX XXX production XXXXX and tune the output accordingly. The XXXXXXXXX XX XXX future is to XXXX XXXXX machines operate XXXX "big XXXX" and take XXXXXXXX XXXXXXXXX without a XXXXX decision.When "big XXXX" emerged XX a XXXXX in XXX business XXXXXXXXXXX, most XXXXXXXXXXXXX XXXXXXXX it XXXXXXXX a lot XX XXXXXX in XXXXXXXX the means XXX XXXXXXXXXX XXXX. XXXXXXXX huge XXXXXXXX XXXX XXXXXXXXXXX XXXXX everything XXX XXXXXXXX XXX a major objective (XXXXX, XXXX). XXX, XXX internet XXXXXXXXX XXXXXX an infinite amount of data each second. XXX XXX emphasis XX XX XXX analytical XXXXX XXX XX helping XXXXXXXX to reduce their XXXXXX of XXXXX decisions XXX increase XXXXX XXXXXX. For example, business XXXXXXXXXXXXX XXX XXXX XXX logistic planning to the machines XXXXX "big data" XXX XXXXXX an automatic decision and save manpower and XXXXXXXXXX XXXXXXXXX for XXXXXXXXXXXXX XX strategic XXXXXXXXXX decisions. The XXXX XXX XX understand the future is XX analyzing the past and XXX way XXXXXX XXXXXXX. XX the XX's, XXXXXXX XXXXX XXXXXXXX XXXX requests by XXXXXXXXXX calculating XXX credit score. XX XXX 90's, bankers used computers XX determine the credit score. XXX XXXXXXXX XXXXX XXXXXXXXX the credit XXXXX by XXXXX XXX XXXX input XXXXXXXX by the XXXXXX (XXXXXX, 2015). XXXXX, bankers can XXX the XXXXXX borrower XX complete an online form with XXXXXXX XXXXXXXX and financial information. XX using "big data", the computer will instantly XXXXXXXX a credit score and XXXXXX it to XXX XXXXXX XXX approval or XXXXXXXXX. XXXX time, the computer will XXXX the potential customer XX XXXXXXX him to a pattern XXX compare him XXXX other XXXXXXXXX with a similar profile (age, XXXXXXXX, gender, education XXX.). With XXX, XXX borrower XXXXX even XXXXXXX the requested money instantly.Conclusion"XXX XXXX" XX a symbol XX how XX infrastructures XXXXXXX in time. The XXX challenge is XX understand rather XXXX XXXXXXX. With modern means XX communication, XXXXXXXXXXX is everywhere. XXXXXX and business XXXXXXXXXXXXX XXX XXXXXX XXXXXX XXX knowledge about XXXXXXXXXX. XXXX this XXXX XXXXXX XX XXXX XX XXXX, business XXXXXXXXXXXXX have to keep XX XXX pace and XXXXXXX XXXXX XXXXXXXXXX tools. "Big XXXX" is not XXXX XXXX of valid or useful data XXX XXXXXXXX XXXXXXXXXXXXX XXXX XXXX XXX XXXXXXXXXXX and XXXXXXX what they need from it. "XXX data" XX no longer a competitive advantage XX XXX XXX companies. XXXXXXX XXXXXXXXX can also access "XXX data" by XXXXX cloud computing and XXX XXXXXXXX as a whole. Marketing XXXXXXXXXXX and top managers are XXXXXXX aware of the XXXXXX they XXX XXXX XX using "XXX data" for XXXXXX XXXXXXXX-based XXXXXXXXX. Manufacturing XXX retail companies can XXXXXXX use "XXX XXXX" to XXXXX XXXXXXXXX. XXX questions still remain: XXXXX should people XXX the cross XXXX XXXXXXX the XXXXXXXX XX using "big data" and XXXXX XXXXXXX XXXXX. How much XXXXXX machines "XXXXX" XXXXX XX?
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