Question:
Write a business report based on the following:
Select a new (developed/implemented from 2012/onwards) E-Business Technology/Application. Discuss the key advantageous features of the technology/application when compared with the alternatives available. As well as the key advantages, identify and discuss key limitations of the technology as well as the risks associated with its implementation.
The volume, variety and velocity (Lohr, 2012) of big data have evolved very recently with IT companies embracing the advantages provided by the means of big data. Big data has taken a leap ahead in the few years to help the companies find some interesting ways to analyze the data. It has also conquered the realm e business and all the planning that is involved with the procedure to apply the concept of bug data analytics and data mining with retail sites and online e business. Various experts say that the market for analysis is going to enter into a revolutionary and completely new era where the technology have grown up to become capable of data driven business (McAfee, 2012) in a dynamic world and real time. The developments in big data are moving towards cost effective prospects to improve the methodologies and finally come up to refined decision making in the critical areas of development such as employment, health care, crime, security, economic productivity, resource management, e business, online social media sites (Mayer-Schönberger, 2013) etc. Big data is therefore, a bridge between the technological advancements and enormous explosion of data with business aspect of economic development and prosperity.
Big data technologies, tools and strategies are used in the process to scoop out huge amount of data that is generated by online business websites, social networking sites, online retail stores etc. In the recent times, it has also been realized that big data has the inherent potential to accelerate the development in terms of economy and act as an engine of economic development. The recent years saw a tremendous development in the field of technology of big data which, in amalgamation with business intelligence and data analytics are fueling the application development of mobile technology (Boyd, 2012). Big data has realized its transformational potential for socioeconomic development with the help of innovation, opportunities and challenges.
Big Data can help in collecting and analyzing information from simple and cheap cell phones to give an insight into the behavior, movement and patterns of people (LaValle, 2013) travelling leading to a study about the health issues or diseases spreading. The innovative new development can aid data mining techniques to adopt new measures to scoop out analysis for patterns by the means of simple text messages via cell phones. Such collected information and use of big data technology with mobile technology can be harnessed to build tools and systems for officials or health practitioners. To mine the phone records can help in giving patterns of study as per the requirements. Presently with almost six billion mobile phones used in the world, they generate an enormous amount of data that might include location tracking or information (Laurila, 2012) over some sort of commercial activity or some saved bookmarked links to social networking site. Big data along with techniques of data mining are used to analyze the patterns of mobile phone usage to predict the degree of health issues and also to predict the magnitude of the outbreak of diseases. Big data which is so far untapped to a greater extent can be used to engineer a better world to live in. In the terms of finance and resource management big data usage is making the commercial opportunity for the banks highlighted and enabled. Data is real terms is an asset and usually emerge from undeveloped or developing regions having lack of other technologically advanced resources and tools for survey or research.
In the past two years, almost 90% of the total data was generated which was a product of contribution from Facebook, Twitter, YouTube, Google, Amazon or LinkedIn (Davenport, 2013). Various businesses are utilizing the essential techniques of big data to understand the reviews of the customers and to formulate their policies and strategies based over positive or negative feedbacks. In the contemporary times, tools such as Hadoop have made storage of enormous amounts of data in cheaper ways as compared with other tools such as Exadata from Oracle, Netezza from IBM (Marz, 2013) etc. Retail stores have started to use the concepts and practicalities of big data to analyze customer’s activities via cards and coupons. Had there been a mainstream adoption for the analytics of big data, the manufacturing and retail sector alone would have accounted for approximately billions in their annual GDP. This is because of the fact that big data have now become commercially and enterprise ready to be used by the companies in a much cheaper way to store data and to later process data so that plethora of business may leverage the big data analytics. In the recent of the developments, the analytics tools for big data are opening up its realm to common people who lack specialized skills for handling or using it. The companies are democratizing big data with iterations and version updates to make it easy to use and easy to understand in the terms of user interface and visualizations. Real time analysis of big data is an innovation in the framework (Murdoch, 2013) to extend their services in various fields. For a past few years, companies are increasingly becoming interested to loom into business prospects across the borders and helping to drive the overall world economy. The economic development in these markets and developing potential markets are leading to an increase in amount and quality of data. This is a circle of development where the economic development is driving and paving path for increase in data and use of big data which is further fostering more economic development. Technology have mad the ways where people are more focused towards producing data and making it available for analysis so as to make the lies better and easier.
In the year 2014, the trumpet and drumbeats of big continued to pound the world with major developments and some of the DBMS vendors expanding the offerings of the products. There was in literal terms, a tsunami of big data in the marketplace of database and database management (Marx, 2013). There have been advancements in the fields of business intelligence, NoSQL, analytics and tools for big data.
For the upcoming years, there are predictions being made by various companies are organizations pertaining to big data. The predictions for 2015 for the big data includes pushing of big data analytics into the realm of enterprises with some more use cases and getting deeper into the domain of real time use cases for a better analysis and for incorporation of contemporary practices. There are prospects for big data deployments into the real time. Data agility is becoming one of the key drivers for the development of big data technologies and analytics to build around legacy databases (Zaslavsky, 2013) and data warehouses to include flexibility into the development process. The initial objects in big data focused over being storage for the target data sources but instead of focusing over the organization or management of data, the focus should shift to measuring agility of the data. 2014 was an year of data hub or data lake which was an object based repository to store raw data in the native format either in a structured format or in some form of raw unstructured (Michael, 2013). These data lakes are predicted to evolve in the year 2015 with an inherent capability to bring about multiple of computing and execution engines for processing of data in place. The data lakes in real time are projected to evolve from being batch to real time processing and integrating some of the file based engines to large scale development platforms. The big trend of big data in the year 2015 would be continuous access and the processing of some of the real time events to gain success in tapping of unlimited potential. Year 2015, will also see an era of self service to embrace big data to allow business users for empowering development and analytics to conduct research for data explosions. Hadoop tool is into a maturity lifecycle with various white papers published in the context for making it steer into an innovation phase (Wu, 2014). There have been tremendous competition in the field of big data softwares and tools transforming the trends from batch analytics processor to a fully featured data platform for integration and applications services. The data stack for the Hadoop software will see enterprise architects coming onto the main stage for a sophisticated requirement sheet for applications of big data (Smolan, 2013). There will be high availability and business continuity by usage of big data and the recent tools developed which are cost effective and are coming with easy to use interfaces for users.
The management of data in the terms of business so as to collect, filter and analyze data would become simpler as the size of data is growing and tools for data handing and mining (Crampton, 2013) are becoming obsolete to match up the enormous volumes of data. The speed, capacity and the scalability (Dumbill, 2013) factor that is attached with the cloud storage is benefiting the business to make them able and competent to manage massive data sets and information without significant cost investment and secondly to reorganize platforms for big data hosting to abstract the deployment of complex big data technology. Big data tools are incorporating features of data visualization tools to be able to make the end users actually visualize the data in the real time. As the big data analytics tools begin to expand and get mature in the industry, companies are realizing its competitive advantage in the era of data driven world. Big data is being used in plethora of fields for analysis and research.
As we can all observe, that data is no longer simply numeric and numbers stored in database, the capabilities to analyze data will evolve with the introduction, use and implementation of big data (Madden, 2012). The services provided by the business organizations can be can be enriched and filtered by the use of big data technologies and analytics tools to mine and scoop out patterns of data and coming onto useful results pertaining customers. With the real time information, there can be better sales insights (Cambria, 2013) which may lead to additional methodologies for revenue generation as it can map the sales on a per second basis.
There are plethora of challenges that are occurring in the development and innovation phase of big data analytics and proving to be key barriers in general and widespread adoption of the tools and techniques fostering the use of big data in day to day routine data mining and pattern analytics techniques.
The technology requires the use of some of the special computing powers (Gantz, 2012) which is not much mapped up with the real time analysis. The tools have to be made specialized and advanced in each and every term to process data in real time (Chen, 2012) and provide the results and analysis reports in the real time. For every organization, it is practically impossible to change the culture of the company so as to make it fit it the real life scenario and make room for the analysis and results that are brought every second. The organizations adopting big data must be transformed into information centric organizations (Kaisler, 2013) focusing over real time data input and analysis output.
The big risk for the big data is the chance of real failure and risk leading to structural shifts (Fan, 2013) in the markets. There have been ambiguity in the markets and the customers using the data analytics. Security of the information and the privacy (Tene, 2013) of the customers are the other important and key risks that are associated with the disadvantages. The cost of the tools required to implement and execute big data into the real life requires is far too high for medium or small scaled industries, organizations or businesses to be able to implement big data into their routines.
Conclusion
Big data is not much new to the industry but its presence, buzz and craze is new to IT industry with its impact spreading like forest fire and big data along with its cost effective tool Hadoop becoming the buzzword for the industry. There have been many advancements made, but the use of big data in mobile applications to be able to get insights into health factors and disease spreading is a contemporary approach and development done. There are many business advantages that big data offers but also have some of the disadvantages that are occurring as huge challenges. Real time collaboration of big data shall be a tremendous success in the near future of e business and marketing (Liu, 2013).
References
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