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Explain Big Data Can Be Grouped Based On Different?

Data is defined as quantities, symbol or features that are performed by the computer machines that can also be stored, transmitted by either in electrical signals and recorded on the mechanical or electric signal. Big data, therefore, is either a data but with big storage. It’s used in the description of data collection that is majorly very big and expanding at a very high rate. However, this type of data is normally very huge and complex that no any other traditional tool can operate efficiently. Big data examples are social media impact which is around 500++ terabytes to store data before being used into the system (Azarmi, 2014). However, Big data can be categorized into either structured, unstructured and finally semi structured.

This refers to the speed in which generated data operates. It describes how fast the information is produced by the demands in establishing the real strength of any data.

This shows how data is consistent with how many times the data operates to the effect on how the data can be handled more efficiently.

This describes the nature and sources of data regarding structures and semi structures (Caro, 2015). It contains some of the unstructured data containing a given issues based on storage and data analysis.

The importance of big data is not based on how much data is produced but what amount of data can be placed in practice (Belloc, 2016). You can produce data from different sources in finding out answers for cost reductions, time and ways of obtaining new product for decision making. Many tasks can be achieved by using this technique. Some of detecting any fraudulent acts, establishing ways in risk reduction, establishing the causes of any business failure and generating sale issues on customers buying trend.

Business can only develop with a good source of data (Chorafas, 2001). Therefore, processing this gives multiple benefits to the world such as-Businesses benefits from the use of intelligence while implementing decisions in access to different social sites thus enabling the company to improve their strategies in business. In additions, improved customer services can be improved whereby a traditional system is replaced with new technologies from Big data for improvement of natural language (Davenport, 2013). Also, big data can also be used in the creation of areas for storing new big data before deciding what to be transferred to the data warehouse.


A more complex type of prescriptive modeling can also help firms in predicting any business opportunities and implementing decisions that target marketing companies thus avoiding the collapse of business tools (Federal Data Science and Advanced Analytics in Agricultural Science, 2017). The Big data tool can ingest different types of data both in structured and semi structured like the web server or some phone mobile application log. It comprises both the text files, emails, text messages and documents for data management. Also, big data tools help in the provision of a framework to enhance the use of data mining system to analyze information by the introduction of patterns. This will enhance performance in business activity by adapting analytical models to correspond with the business operational application.

Big data application in wall-mart stores is doing a lot with Big data. The presentation of big data is characterized by the increased variety volume and velocity. The complimentary documents explain the details of a strategic IT plan similar to the firm in identifying all the gaps in the allocation of its materials (Shirin Hijaz Matwankar & Dr. Shubhash K. Shinde, 2016).  At this report, the evolution of big data, challenges for most companies are technologically related. The most effective implementation to be adopted relates to cultural challenges, organization alignments that result from the ignorance to adapt the new change.

The company is doing big data business has helped a lot in the improvement of technology. The total output for every business locality. The report is based on the usage and management of IT sections according to the improved ideas from big data.

From our humble beginning as being the small retailer in Rogers, Ark, the Wal-Mart business has expanded into various parts of the US and different countries internationally. But with new big data innovations, the business is creating a place to enable the potential customers to shop anywhere online. We are establishing new branches to accommodate more communities in job market around the globe. Everyday low price (EDLP) is one of the best strategies used by our firm to focus on price determinants for the benefits of our clients. From our grocery and entertainment in support of goods the company produces goods and crafts that our customers appreciate.

Wal-Mart is a powerful retail brand.

This company has a good reputation regarding currency, convenience and different items in the store. The original dream of Walton’s is to conduct a productive business of large discount with many departments. The greatest kind of strength adopted by the company includes an understanding of low prices, market conditions, the distribution channel and competence in current data technology. Also, low prices and both this technique have helped this company a lot. The greatest Wal-Mart strengths come as a result of the expansion of his prosperity. It has the core competence that promoted the use of new technology system in support of its international logistics.


But in contrast with strengths that makes Wal-Mart successful, there is also weakness in conducting out various duties. Unfortunately, the company has a poor public image that sometimes viewed as being unfriendly to the community. Also, rumors on how employees are treated are also a major thread for the company. Some of the people say that the company tends to pollute the environment and congestion within the place of work. Despite this entire rumor, the Wal-Mart has this problem of inflexibility mostly in dealing with customers around.

However, the marketing activities in Walmart establish a marketing Sandra piccolo whereby is one of the growing industry in the US. This technique has helped it in attempting ways on how to deal with their external competitors around the market (Srinivasa & Bhatnagar, 2012). But effective stakeholders contribute a lot in the market both direct and indirect for the benefit of business. The engagement of stakeholders in business field has generated a good communication in effective settings. The suppliers, on the other hand, can be traced by the video from Wal-Mart explains the benefits with the company supply trucks. The evidence attributes for the effective relationship with suppliers on how the company survives. The community has evidence in Walmart explains the importance of financial benefits.

 With the current macro-Environment, The external environment influences the decisions that managers possess in orders to improve the continuity of the company. Sometimes, managers have a little impact when it comes to environmental issues. But there is a new trend that introduces market trends known as green retailing. The concept to reduce this kind of waste is by the act of retailers increasing the level of customer expectation. The industry and strategic group explain the existence of competitors together with those in the market. It’s important to understand that big data helps a lot in real time fraud detection, social media analysis, optimizations of call center and display of web for the need of business technological advancement. It has also improved all the analytical issues within the business environment.

Wal-Mart is the biggest retail store; it has various data operating within the system. Therefore it has to maintain the flow of vital information into the system by establishing the data café. This and hub where the art focuses on handling information by putting in a working environment in solving different issues. The company has employed over 200 streams both in external and internal data. This comprises of 40 pet bytes where the data can be analyzed. The company is beneath the business access of concentrating majorly on data within the business field. It has heavily invested in data management (U?urlu & Sevim, 2015). It can, therefore, track, gather by using social data in improving their productivity within the market.

Benefits of Big Data Processing

Normally, the largest social network ever used in the marketing industry happened in the year 2012. This has given lots of lips service necessary for social data storage in arriving at crucial business decisions. The retailer has also invented a program known as product content collection system to promote vendors in delivering their products catalogs to Wal-Mart. Also, the company has implemented a technique in conjunction with product information through the Global Data Synchronization network, the product content collection system. However, to improve such technique, it’s necessary to ensure that once the customer enters the job market, data can be collected by importing, put in a good cloud storage and customers can use it for their benefits.

When it comes to Wal-Mart, having big data suggest that the company has impressed the conduct of business in many ways. Being one of the leading markets, it has Hadoop-based technologies in regulating the use of big data systems (McNeill, 2013). The real time data analysis has enabled the company in driving direction on decisions from different people by improving the level of the supply chain. But in these days, accessing big data has become easier than before. This is due to reduced cost of storage and how to process this data has become easier. Wal-Mart Company collects its information majorly on web traffic, mobile apps and other different sources.

 At, the root storage of big data requires some measure to help improve the performance. A larger amount of data in the system needs input/output operations per second to deliver data to delivering tool. The data can either be stored in re-size, e-handbook or e-zine. This is made up of direct –attached storage, but redundancy happens when the system suffers an outage of any component. IDC’S storage solutions explain on how the big data can be stored based on the look of big software and hardware in the market. Attention is given to new software like NoSQL database. The impact of storage is also studied on architecture, storage media, capacity and perspective of revenue across the center in Wal-Mart business center.

Recommendation and Conclusion

Some of the organization doesn't concentrate on the data management in their system. But the only required knowledge is being confident in business decisions. You have to possess a strategy to help in the management of big data as shown in Wal-Mart business. The final step one has to make includes researching the technologies that help in deciding the most appropriate big data analytics. The data obtained on different social media platforms on data management is an attractive source of information and therefore needs to be tackled with care.

Organizational analysis

Big data can be described by the volume, variety, velocity, variability, and veracity.  The volume contains the quantity of and the data stored. Variety looks at the type of data while velocity is the speed which the data is generated.  On the other hand, variability is the irregular process that involves managing of the data.  Veracity includes the quality of which quality of data varies.

Industry influencers, academicians, and other stakeholders certainly concur on the fact that, big data has become a big game changer to organizations in the society today. First and foremost it should be noted that modern industries have embraced technology globally. The 21st century has been witnessed with industries investing big for the future.  However, the goals of organizations differ on big data projects depending on their goals and resources. Big data is essential in industries making the market efficient by ensuring cost reduction. Furthermore, big data technology is important in the banking industry. For instance, the banking industry is faced with many challenges which include: credit risk, card fraud, customer data transformation and general IT problems.  The employment of the big data in the banking and security has been vital to monitor financial market activity. Financial institutions are using network analytics to curb illegal training activity. On the other hand, retail traders and major banks use big data trade analytics to monitor payment. This has provided customer driven promotions which are an important relationship management. In the manufacturing industry; it has enhanced product research which is essential for budgeting and product preference.  Healthcare industries are considered to be using big data analytical capabilities to improve surgical and ultrasound facilities.

Big data analytics, therefore, is a trending practice that companies and organization have considered adopting.  Big data analytics are software products which are fundamental in an application running on big data computing platforms.  The most commonly used software is Hadoop and NoSQL data bases. Hadoop helps organizations make decisions that have been categorically rather than a small sampling of data. Additionally, advanced data analysis can be carried out to avoid the operational expense of outsourcing. Government agencies collect vast amounts of data in public fields and cyber security. It should be noted that Hadoop not only ensures security to the public but makes a collection of data simple. Telecom companies often process millions of calls every second. The rapid pace of telecommunication requires efficient storage of Apache Hadoop. When it comes to manufacturing systems Hadoop ensures quality provision of products. Organizations can design their products with the help of big data (Srinivasa & Bhatnagar, 2012). To begin with, health and life sciences deal with a high population of health cases. With the busy health institutions.  Big data technology can be used globally to provide devices to patients who are miles away. For example, a Doctor is based in India can send medical information to a fellow doctor in Africa.  In the business sector, big data can be found used to ease storage of large data and monitoring of the business for several years.

Nature of business

Recommendation systems in business are used to prevent business risk and customers interactions. More often than not in the contemporary society, today business stakeholders have engaged in online transactions and marketing which has proven to be vital with the current changes in technology. Moreover, Recommendation systems can provide an opportunity for the customer's preference of certain products. For instance, with the online websites of products buyers can go through the specifications of their choice. Another important factor of the recommendation system is that the user can distinguish between qualities of the certain site depending on the services that they offer. This is usually in the case games, music and streaming live sites. It is no doubt that YouTube has proven to be the widely used sight due to the reliability and the quality of their services (Chorafas, 2001). A user will never opt to use another site because YouTube provides a proper recommendation system that can be used at any place any time. In the world today, smart phones are widely used to the advancement in technology. With this effect, mobile phone companies have ensured that their data is available on search engines, websites, and emails. Samsung phones are used widely in the globe yet their company is situated in Asia. The success of the massive sale is the suggestion of products for customers to buy via their email. They have not only used several media houses to market their phones but also ensuring that their mobile phones image are available to all continents. Recommendation systems have also been effective in the Car manufacturing industry. Toyota cars are quite common because they came up with terms and conditions that are friendly to the buyer. For example, they have given a friendly warranty and have also recommended Vehicles that belong to the middle and elite class in the society. They are several car manufacturers in the world today, but Toyota is the most preferable.  Goods that are sold abroad have also been a key factor in the recommendation systems. Buyers who ship their goods and services online have been able to cost share with the manufacturer. It should be noted that the manufacturer attracts plenty of customers and also spends less when exporting their products.  Word of mouth through friends has proven to an effective way of recommendation. For instance, a friend can tell a friend who spreads the message to another third party. This is usually in the services provided by transport, bar, salons and the health sector. Organizational transformation refers to activities that improve the day-to-day running of the business systems. This is usually accompanied with the aim of improving the quality and provision of services by the organization. Transformation occurs by redesigning, re-energizing and redefining. For effectiveness of the organization, transformation teamwork is required by colleagues and the senior management. It should be noted that transformation has the fundamental root which is human values.

Internal analysis

 Risk management, therefore, refers to a process of identifying, assessing, and controlling potential threats to an organization. It is simply an organization preparedness in and after a threat.  The main types of business risk include reputational risk, financial risk, operational risk, compliance risk and strategic risk. Reputational risk refers to the name of a business being damaged due to a certain situation. A reputational risk can easily lead to loss of customers. Therefore it is the mandate of an organization to stand firm in such scenarios. Financial risk refers to the money flowing in and out of business and the possibility of a loss. The chief accountant of a business should always be on the forefront to manage cash flow in the business. Operational risk is the unexpected failure in the running of a company due to unavoidable situations. Compliance risk involves the terms and conditions that the government puts across in the running of the business. Failure to adhere leads to leads to dismissal of the business. On the other hand strategic risk is a situation whereby, the company’s strategy becomes less effective due to changes in technology, raw materials, and active competitors. If a company is with any risk, there should be proper communication to avoid the last minute rush. Having proper channels of communications ensures that every employee works extra hard to restore the reputation and quality operation of the company. Risk management contains processes which in the long run prevent the company or business from collapsing. The first step, in this case, is to identify the risk, then analyzing the risk as a team in the organization. It is then followed by evaluating the risk and responding by looking at solutions then lastly monitoring and reviewing the risk. Big data in the industry sector has ensured that there is operational optimization. This is witnessed by gaining more precise predictive systems and less risk failure. Furthermore, it has ensured that there is employee engagement by monitoring their performance. In addition customer experience by ensuring that their preferences have been met. However big data on the industry has come out which challenges. Most companies are only analyzing little content of their data in the manufacturing industry due to their complex operations. Storing of data has proven to be very challenging because they become useless with changes in time. Dealing with security has also proven to be a challenge which industries are spending much but are not adequately doing away with insecurity. One of the most significant impacts of big data is the transformation change to support data opportunity. Existing data preferences have simplified the process of monitoring and evaluation of banks and other financial services. Using data to make marketing decisions has led to the increase of market productivity which has improved the annual global marketing spending. The challenges that organizations face include: data quality is often very poor. Data stored in structured databases are often out- of –date. At times there are cases of duplicate mailings from clients. Most of the data gathered are unstructured which is a difficult task to distinguish quality data from the less important. It is also easy to confuse data by easily assuming that the output of the data proved to be reliable.

In the society, today with the changes in technology economic stakeholders should train their staff. As much as technology has brought up efficiency and time saving; the negative impacts should be emphasized. Shortly, big data is a factor that will transform organizations information technology. Clearly, in the 21stcentury, big data has played a big role in time management and penetration of the wider market (Chorafas, 2001). However, it should, therefore, be noted that in some areas accessibility of big data is limited due to lack of electricity and poor internet server. For better reliability of big data, organizations should be up-to-date with the current software.


Azarmi, B. (2014). Talend for Big Data. Packt Publishing.

Caro, C. (2015). A Retrospective Look at College Football in the Late BCS Era A Case Study in Sports Analytics, Sports Management, and Sports Economics. Journal Of Business Case Studies (JBCS), 11(2), 71.

Belloc, H. (1967). On. Freeport, N.Y.: Books for Libraries Press.

Chorafas, D. (2001). The Internet Supply Chain. New York: Palgrave Macmillan.

Davenport, T. (2013). Enterprise analytics. Upper Saddle River, N.J.: Pearson Education.

Federal Data Science and Advanced Analytics in Agricultural Science. (2017).

Shirin Hijaz Matwankar, & Dr. Shubhash K. Shinde. (2016). Case Study: Political profiling based on Twitter Sentiment analysis for Big Data using Data Mining Algorithms. International Journal Of Engineering Research And, V5(02).

Srinivasa, S., & Bhatnagar, V. (2012). Big Data Analytics. Berlin, Heidelberg: Springer Berlin Heidelberg.

McNeill, D. (2013). A framework for applying analytics in healthcare. Upper Saddle River, New Jersey: Financial Times/Pearson Education.

U?urlu, M., & Sevim, ?. (2015). Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector. Journal Of Business Research - Turk, 7(1), 60-60.

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