Andrew Springiness Module 1 CSS: Information Networking as Technology: Tools, Uses, and Socio- Technical Interactions DIMMIT: Management of Information Systems and Business Strategy Dry. Mary Lind June 17, 2014 Information Overload “Are organizations likely to find better solutions to information overload through changes to their technical systems or their social systems or both? Why? To answer this question, this paper will discuss the technical and social systems of companies specifically based on review of the articles by Blair, Belling, et al, Green, ND Lie and Ere as well as other information on related data companies such as Amazon and ASS. The context of the paper will aide in the understanding of an ideal way to process the information present in the market and then use it for company benefits.
This paper will also review and analyze the importance of info-tsunami in context of specific markers and give specific examples on how data storage and analysis is now the latest trend in the market. Various big data software present in the market and comment on the future trends of the market will be reviewed. Finally, I will propose an answer to the original question posed of what betterment is most important in dealing with information overload social systems, technological systems, or both?
History of Data Mining/Sharing In order to truly understand information overload and how to deal with it, we must start by analyzing various aspects of data starting from its history through the current and probable future trends of the market. Today there are zillions of pieces of data in the market growing for over 30% per year bases (Blair, 2010). The roots of the big data come from ancient days when people used to huge manuscripts and biblical resources to pass on the knowledge of present generation to the next one.
They not only documented information, but also backed up or made it easier to share that information by creating duplicates of the original work. People with different philosophies discussed the same issues with a different context and vision to give alternate versions of the existing issues. However, this increase in the amount of information collected led to what may have appeared to be an insurmountable collection that could not be fully read in an acceptable amount of time or never being blew to find specific information, which could be described as an information overload (Blair, 2010).
People would have too much information to sift through to find what they needed, which would need to lead to an evolution in that form of data storage such as different note-taking capabilities as well as organization (Blair, 2010). Note- taking capabilities enabled the researchers to organize the structure of different ancient texts and later on printing evolved the structure of writing as indexes and bibliographies became norm for the research papers, which helped people to find he specific information they were looking for or the source of more information.
Encyclopedias were created to serve as a set of easily accessible and searchable information on a broad amount of topics. Also, the advent of the Dewey Decimal System meant that a lot of general information could be found in a short amount of time. The Dewey Decimal Classification initially sorts information into 10 categories, and then into another 100 sub-categories, giving you 1, 000 specific categories to search (University Library, n. D. ). For example, you could search the ass’s for
Technology or Applied Sciences categories and find sub-category (also known as a “call number”) 621 and search specifically for Applied Physics (University Library, n. D. ). All of these things lead
This educed limits of how much information people and organizations could collect. People and organizations could now store a large amount of information onto cassettes, disks, diskettes, compact discs, etc. Rather than in hundreds or thousands of books or written documents. Today, we can carry a flash drive with a program to read electronic books as well as hundreds or thousands of electronic books that is the size off pack of chewing gum. In addition to the space, the information itself was now only a touch away.
People no longer needed to use indexes or bibliographies as they could sears for keywords and a computer would help to find he information they were looking for. Computer systems can search through programs, documents, or the world wide web and find information that people are looking for in milliseconds. However, there is now also a feeling that maybe there is too much information accessible through the internet nowadays. We are at a point where there is what appears to be an insurmountable pile of information available on the internet, even when computer systems help us sift through the information.
Companies may again have to sift through a lot of minutia in order to get the specific information they need. In another point of view, there were initial concerns regarding the electronic system of storage. There have been many instances where digital data has been hacked or accessed without the consent of the original writer in order to change the information or utilize it for other purposes than it was originally intended. There is also the possibility of data redundancy and the fear of data getting lost of due hardware complications.
However, with the advent of more and more advance technologies in data storage and sharing electronic storage, there are greater security and back-up procedures added to hardware and software. This leads to the it is the only feasible medium conceivable in the future. Data Analysis To help understand information overload, data analysis must be defined. According to Russell Kickoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories: data, information, knowledge, understanding, and wisdom (Belling, Castro, & Mills, 2004).
According to Belling, et al (2004), the data can be described as symbols; information is “data that can be processed to be useful” and provides answers to four of the five Was (who, hat, where, and when); knowledge is “the application of data and information” and answers the question of how; understanding gives an “appreciation of the question of why’; and wisdom is an elevated level of “evaluated understanding. ” Data is seen as a raw entity which, for the proposes of this research paper, only exists either in digital or in ink.
The significance of data is to be present in any accessible format to the user. Information is the processed data and it is specific to any context to the user. Knowledge is the output gained from that information, essentially by realizing tatters formed by information. Although ultimately wisdom will help with future operations, industry is primarily concerned regarding retrieving knowledge, as this parameter is a tool which is used by the company to either make direct or indirect revenues. Knowledge is the basic building block of data analysis that can be gained directly from computer software.
Companies such as MM, Accentuate and other consulting companies are focus teams to exploit knowledge as a parameter to give specific insights for industries and sectors. Understanding is one step ahead of knowledge in which problems are solved in a specific context. Understanding is the point which the reason for the patterns discerned from knowledge can be understood. It is involved in selecting the required information and processing it to provide the best solutions for a specific problem or multiple problems.
Getting to the understanding phase is difficult with such a superfluous amount of data available to companies. In modern era this process is called Data Analytics or Just Analytics. This is slightly different according to Green (2010) who refers to only four sections of data, information, knowledge, and wisdom. The first three sections namely data, information, and knowledge are concepts of past data, but wisdom is a future analysis and vision concept. It develops our internal experience which helps in our future decision making.
Wisdom is very similar to understanding, with the main difference being that wisdom allows one to predict fracture outcomes based on understanding the reasons behind specific patterns and how changes will affect behaviors of related processes. The first four sections can be represented in a hierarchy and the level of complexity will increase downwards from data to wisdom. In addition, the amount of effort and technological resources used will decrease from top to bottom as you require maximum resources to build and maintain data.
The advance methods of data capturing tools have been efficient in blurring the lines between information and knowledge as companies are becoming efficient in data analysis. Socio-Technical System According to Lie and Ere (2006), “the Socio-Technical Systems theory considers that every organization is made up of people (the social system) using tools, techniques and knowledge (the technical system) to produce goods and services that are valued y customers (who are part of the organization’s external environment). Essentially, this can be described as the interaction between personnel in an organization, or people in general, with that of technology. People and employees have certain behaviors that may need to be modified along with technology in order to create an optimized process or improve quality of fife. The Socio-Technical Systems theory also considers the usage of social information and incorporates it into the development of technology to make it more relevant and desired. Amazon’s Analytics Concept Data analysis has now become a latest trend in the market.
Amazon. Com has become a leader in promoting the analytics-as-a-service concept. They are approaching this as a cloud-enabled business model and not Just an innovation in the industry. It is a great model and will provide as an alternative having better architectural patterns to Justify business priorities. Amazon aims at firms dealing with large amount of data and need flexible infrastructure. Targeted domains in web analytics include gene sequencing, cyber- security, human resource workforce and others.
The challenge is to bind data and draw insights without building complex entities and spending years in restoring those entities. Predicting entities infrastructure is yet emerging and the case is not trivial for Amazon. The model is to give forecast estimations to the companies using their own data which stored with Amazon cloud computing servers. Additionally, Amazon uses data analysis to evaluate data on historical purchases and “wish-lists” to predict the amount of specific products that will be ordered and need to be shipped to certain locations (Devils, 2014).
Amazon can then pre-ship items to hubs in bulk shipments prior to people ordering, which saves in future costs and enables faster shipping services (Devils, 2014). Role of Advance Technology in Data Analysis and Future Trends Technology plays a crucial role for big data analysis as it enables the forecasters to apply the data onto the model and get significant results. With tests such as the Durbin Watson Test, white noise is a small statistical test but needed to implement on all of the data to identify the required useful parameters.
There are trillions of megabytes worth of data in companies. So, how are results found using simple tools such as excel which, according to Microsoft Office (n. D. ), has a limitation of “1 ,048,576 rows by 16,384 columns? ” Dedicated servers and software’s are built and designed specifically for these kinds of requirements. The open source language R is specially built for statistical operations, to extract and interpolate data and can run on multiple operating systems (Wirtschaftuniversitat Wine, n. D. ). A lot of additional software has also come along the way.
Some of these include SAPS, ASS, Maintain, Stats, Jump, Mathematical and others. All have their pros and cons but ASS and SAPS are considered two of industry favorites (Munched, 2014). Another technology which is on the rise is Apache Hoodoo. Hoodoo is designed to create a partition in the virtual memory and allows different users to implement the same function on large databases to give the results (The Apache Software Foundation, 2014). It is more of a server application and can be combined and other technologies to get optimum results.
To solve the memory problem these days, companies are shifting to cloud computing as an alternative. Cloud computing is where all the data is saved n the dedicated servers of a third party vendor and not with the company itself. Whenever the data is required it is processed from those servers and for each transaction a certain sum is paid by the company to the third party. Although cloud computing isn’t 100% effective all of the time either as several companies have found out in the past decade according to Dan Mariner’s (2013).
In the next five years, a trend of adaptation of analytics in more and more countries and in different domains can easily be predicted. As of now, pharmaceutical industry has endorsed use of clinical trails led by analytics. Also, the concept of “Moneybags” has been in used in baseball for several years now. Additionally, credit risk has been managed by these same analytical financial models. All signs for the rising demand of analytics in future. Software’s like ASS, Hoodoo are here to stay and we will see more data managing software been introduced in the industry.
Analytics will act as a backbone of E-commerce industry driving their profits and market share. In the future, big data analysis will not Just be a tool to gain competitive edge but will become a necessity for the survival of the company in highly competitive market. In marry, information and its quest have been long running from the past. Companies are inclined to use as much information possible to enhance their productivity and achieve competitive level in the ever evolving market.
The trends of the market suggests that companies are more inclined to use technology and data mining software and there dependency has been shifting from senior officials to software inputs. Still the importance of experience cannot be neglected and companies must make a balance between the two to achieve high growth rates. I believe the priority will focus on improvement of the technical system; however, any many that refuses to look at the importance of the social system will continuously see high turnover rates. References Blair, A. (2010) Information Overload, Then and Now.