1. Five Key Factors in Driving Big Data Project Success by Pearl Zhu
This article says about five key factors that should be considered for the success of a Big Data Project.
The five key factors according to the authors are
1. Having a Business Goal in Mind
The project outcomes be clear, and the Organization should identify who will/can benefit from this project and how they will/can benefit from this. At the end of the day it is very essential and important that the Organization should have the right and talented team in place to make use of the project insights that are coming out, as a result of the Data analysis what good decision the Organization will be able to make and how will they track and drive those derived final decisions. Converting big data, into understandable, actionable, digestible, and useful information is the major business purpose from Big Data project. (Zhu, 2013)
2 Assuming Data Imperfection & Immature Technology
The Data will be always incomplete and may contain duplicates and may incomplete also in the Big Data analytics, and the Data analytics must properly and efficiently accommodate the real state of the data. By the time a project gets going, there is a large probably that around 30% more data will be available. (Zhu, 2013)
3. Having a good Big Data Talent
The Big Data projects have a lot of pitfalls because Big Data is a disruptive technology and the older users of the old systems are not very well equipped with the new concepts that are being developed and will go a long way to see it fails. Generally there are two distinct skill-sets, the analytical skill sets needed to create meaning based on the information available and technical skills on the side of architecture. A solid and well establ...
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...y Zalud, Bill
In this article the author speaks about the increase in sales by the use of analytics. When the directors of loss prevention first brought security video for their stores, their primary aim was to deter shoplifters or to fight against slip and falls. Times had start changing when security realized that much of the shrink ere happening from sales associates. During the ups and downs of retail, rights and lefts, security video itself has grown up by installing more cameras inside and out, a large shift to IP, integrated video into other security and business systems and also more analytics and intelligence at the edge. Analytics such as motion detection were around for many years. But according to many experts, there are a lot of advantages associated with network based video and analytics works well with digital video storage and retrieval.(Zalud, 2011)
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
The characteristics of this unstructured data are high in volume, high velocity, or high variety and complexity. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Value. Value is what matters to a person i.e. how valuable big data is to one.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data. There are many other sectors that could profit largely by proper analysis and usage of big data.
The creativity and ingenious of human beings has enabled the development of technologies that have overall, benefited all of mankind. Arguably one of the most if not the most pivotal man made technological achievement is that of the internet. The internet has allowed for the seeming less transfer of data and information in a matter of seconds. With this innovation has come an increase in communication, enhancement of understanding other cultures, and a mass gathering of data. The amount of data now in existence due to the internet has created the need for big data. Big data has developed as a solution to the traditional computer infrastructure that has become obsolete due to its inability to handle the massive amounts of data now in existence. The benefits of big data are ever expanding and attractive as it can improve the efficiency of companies, research, health sciences yet, the consequences of using big data are just as intensifying and are causing some backlash in many communities. The current issues surrounding big data and the increasingly dependent nature of the world’s people on big data will undoubtedly impact the use of big data in the future.
There is no proper definition of big data but after reading literature, The definition of big data tends to refer to the use of behavioral analytics and predictive analytics or other advanced data analytics methods to extract value from data.[1]
Evidently, there are many benefits to working with Big Data analytics and procedures, and as computing capabilities continue to advance these benefits are only projected to increase. Data’s value is now more dependent on its cumulative potential uses than its initial use; however it is unlikely that a single firm will be able to unlock all of the dormant value from a given dataset. Therefore, in order to maximize Big Data’s value firms can license their accumulated data to third parties in exchange for royalties. In doing so, all agents have incentive to maximize the value that can be extracted by means of re-using data.
Big Data is a term used to describe the large volume of data whether structured or unstructured that inundates a given operation on a daily basis (http://www.SAS.com). Big Data consists of data sets that are so huge and complex that the customary data processing applications would not adequately handle them. Of late, the concept of Big Data has been used to describe the use of predictive analysis, user behaviour analytics and other complex data analytics techniques for the extraction value from data. The concept of Big Data can be understood through the description of the three V’s as advanced by Doug Laney, who is an industry analyst. First, Big Data can be understood in terms of Volume, whereby organizations collect large data from a variety
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
The first and most crucial step is to create a solid plan. Plan should include the techniques, tools and data that are going to used in the project. The responsibilities of all the members should be distributed at this step. The utilization of resources and budgeting of the project should be done here. Management tools such as probability and Impact Matrix, FMEA are useful at this point.
Adopting big data can also help the banking industry by saving them from lots of embarrassment resulting from increase in the number of customer which in turn requires banks to improve on their performance. As stated earlier banks are entrusted with lots of information and this information must be safe will be required to be accessed ready and in a timely fashion. The use a normal small database will not be enough to perform this operation and if banks don’t embrace the use of big data they might start to experience failure in there system.