CHIA-HUA, LIN

How might Big Data improve the conventional interaction between Business and Information Technology?

Introduction

Business today rely on Big Data to increase operation efficiency, inform strategic direction and improve customer service. With the rise of Big Data, Business try to invest and implement Big Data to reach its ultimate target, profit. Google contribution America economy by bringing over 54 billion profit in the year 2009, and it is just a tip of the iceberg of the overall benefits of Big Data. Big Data is described as the new petroleum, where not only Business have great interest about it but has become a vital matter of a nation. China list big data as a priority technique in the “Long-term Technical Development Guideline 2006-2010”, rendering Big Data is not only a technical term, but meaningful stuff to new emerging enterprise and national strategy. [1]

 

As big data play increasing important roles in modern society, most of what has been written about big data focus on the definition, concept, challenges and technical research. Researches developing findings about Big Data by providing evidences and theories from IT aspect, far less researches focus on the connection between Business and IT, showing a lack of integrated research which combined both commercial elements and practicable implementations when evaluating Big Data. Yet, only by viewing the changes from conventional approach to big data, companies may have the ability to transform and manipulate this new technology properly when putting this new technology into practicable use.

 

This research try to build the connection between Business and IT of Big Data from three aspect. First we review people in big data field, which is the most important component that pushing big data to progress. This will help us see what has changed from the past with less technical content. Second, we identify the value of Big Data. By viewing the defined value and intangible values that Big Data brings to the enterprise, we may find the motivation behind Business and see how and why Business are looking for Big Data so eagerly. Lastly, with a stable foundation that has established from those two points, we then look into how Big Data lead business to change. With this structure flow, we are about to see how Big Data improve the conventional interaction between Business and IT, form the surface to core, which hopefully may build a connection between these two field and contribute Business at the point they are taking Big Data into practice.

 

Main Findings

1.0 Notice

Before going further to Big Data, the basic definition of Big Data should be clarify. Researches has been discussing the definition of Big Data overtime. However, “There are many definitions on the term… The predominate one seems to be that BD (Big Data) comprises datasets that have become too large to handle with the traditional or given computing environment.” [7] Fortunately, researches address several Vs to describe Big Data, which are (a) Volume, the humongous amount of data; (b) Velocity, data access speed has increased due to emerging technologies; (c) Variety, which refers to diverse data sources, including structured, semi-structured and unstructured data; (d) Verification, the quality and security of data, which need centralization and pre-processing techniques; (e) Value, which reflect why Big Data is essential to Business. [1][8] These five Vs are important elements when evaluating Big Data and will be mentioned in the following paragraph develop the context of this research.

 

1.1 People in big data field

Data scientist has become a hot job ever before. [9] General speaking, this phenomenon appears because collected data now is fundamentally different from the data collected from the pass. Thus, to search certain data from a massive, unpredictable of variety data, the approach is very different from the past, which has reflected the need of personnel has become different from the past.

 

The source of Big Data mainly comes from three major categories: (a) From human activities / enterprise, such as data from finance, transportation, economy etc., (b) machine-generated / sensor data, (c) Social data. [8] Over 85% of collected data are semi-structured or unstructured. [1] In comparison, conventional approach collect structured data, which is predictable and easy to manipulate. Conventional databases tools are not designed for unstructured data [9].

 

Typical and most famous Big Data computing models and systems are HBase, Hadoop MapReduce, Scribe, HaLoop, Pregel and Dremel. The terms listed only represent models or systems take in charge for certain aspect of computing. In other words, although these techniques are all trying to manipulate Big Data, some of them focus on reducing the cost and improving the performance, some of them focus on certain type of data structure such as graphic data [1][2].

 

With the dramatically changes brought by Big Data and the appearance of numerous new techniques, most required skills for Big Data jobs are absent from university programs. Only mathematic course such as calculus, linear algebra and probability suits Business needs. Companies are crying for cross-discipline, creative and personnel, and mathematic abilities are emphasized, because they believe employees without those abilities is not able to see what’s really going on inside of Big Data [3].

Conventional Information System requires personnel have the ability to write codes, but programming is no longer a necessary skill in Big Data field. Although coding is still a big plus for employees, companies expected programming to be a way to build tools for analyzing and searching Big Data.[1] Coders might be a temporary phenomenon, domain knowledge will be something important for employees to “Swimming in data”. People in IT is longer just do the programming, they need to be part of the creative team, and also be a good communicator [3].

 

It is prove trick to find suit able people for Big Data analysis because of these universal requirements and the lack of significant courses to support necessary information to university students. In addition, even a person who have those required skills, they may require to have the insights to discover commercial values no matter how brilliant the person is. Although the need of Big Data personnel is increasing and people are interesting to be in this field, companies believes that not everyone can be suitable for those jobs because of these requirements [3].

 

 

1.2 Value of big data

Value is the final Vs definition of Big Data which usually comes up with the forth V, Verification. The initial definition of Big Data only contains the 3Vs, the appearance of last two Vs show the progress of Big Data.

 

Verification represent the security part of Big Data. The value of Big Data made it described as a new petroleum [1], it also reflect the features of petroleum, where Big Data is restricted by laws and regulations that keep it away from unlimited uses. Since Big Data collected personal information from customers, laws are set to make sure collected data is record only if it is necessary and with the agreement of customers. [5] Business need to understand these laws before collect any data from the customers and make profits.

 

The Value of Big Data has changed fundamentally because of its usage. The conventional technology collect data as a main process target. In Big Data approaches, data is not only a recorded log, but also see as a resource [1]. Big Data has become an assistant component which are reuse to predict use behavior and also being a collect data for sell as a product.

 

 

Leading organization to improve

 

Volume, the first V of Big Data is the essential elements that lead the organizations to fundamental changes. In human civilization, 90% data is generated from the past 2 years and will doubles each two years. With the dramatically increase of data, we were trying to fishing in sea rather than pool. The approach will be fundamentally different from the past [1]. Existing technologies don’t work well on long term, large scale analytics. Supporting technologies are used to transforming security analytics by facilitating the storage, maintenance and analysis of security information [4]. Organizations need not only different personnel but an appropriate adjustment for the tools they use to deal with Big Data. With those arrangements, the organization may have the ability to profit by improving the performance and efficiency of their operation process.

 

When trying to improve the efficiency of tasks, event logs plays an important role. By finding the correlation between events, a company is able to fill the gap which is the main reason for what cause the approach less efficient. For instance, in network security, there are many traditional techniques about traditional event correlation, such as rule based correlation, Bayesian network inference, model-based reasoning and filtering case-based etc. [6] When leveraging the approaches into Big Data ear, the most important step is to find the association among events as well. However, because of the volume of Big Data, even with the same focus, the way to reach the target need to have some improvements. Back to the security example, to send an alert to a necessary alert from massive event logs, the system need to have the ability to extract the real non-redundant information. A new models and systems are in need, and sensitivity and reliability are two elements that need to be considered in one time [6]. Thus, although business may find many opportunities in events log, they first need a proper model to analyses those humongous amount of event logs first.

 

Conclusion

To understand how Big Data has improved the conventional interaction between Information and Technology, the 5Vs of Big Data has been carefully viewed in order to comparing those features with past data analysis approach. Since Big Data is fundamentally different from traditional data techniques, people involving Big Data field requires different skills. Although programming is still an essential skill, but companies are looking for people who are more creative, insightful and have university –level of mathematical abilities to explore the value from massive, unstructured data. Finally, with those values provides by Big Data, the increasing event logs generated by process-oriented information system, organizations may have a clear guide to improve Business Process.

 

Discussing and Limitation

This research established a basic connection between Business and Information Technology, from threefold aspect: people, organization and technology itself to evaluate Big Data. Future work should focus on specific position for people involving Big Data and provide a more detailed interaction content and should work on how Business bring advantages to Big Data to complete a two-way interaction relationship.

 

Reference

[1]Fang, W. Big Data: Conceptions, key technologies and application. Nanjing Xinxi Gongcheng Daxue Xuebao. (10/01/2014) ,  6 (5), p. 405 – 419.

[2]Dean MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM. (01/01/2008) ,  51 (1), p. 107 – 113.

[3]Tankard, Colin Big data security. Network security. (07/2012) ,  2012 (7), p. 5 – 8.

[4]Cardenas, Alvaro A. Big Data Analytics for Security. IEEE security & privacy. (11/2013) ,  11 (6), p. 74 – 76.

[5]Leonard, P. Customer data analytics: privacy settings for ‘Big Data’ business. International data privacy law. (02/2014) ,  4 (1), p. 53 – 68. Edit this Citation

[6]Boukri, K. Security Analytics in Big Data Infrastructures. International journal of computer science and information security. (05/01/2015) ,  13 (5), p. 91 – 95.

[7]A. Vera-Baquero, R. Colomo Palacios, V. Stantchev and O. Molloy, ‘Leveraging big-data for business process analytics’, The Learning Organization, vol. 22, no. 4, pp. 215-228, 2015.

[8]Opresnik, David The value of Big Data in servitization. International journal of production economics. (07/2015) ,  165 p. 174 – 184.

[9]Dearstyne Big Data’s Management Revolution. Harvard business review. (12/2012) ,  90 (12), p. 16 – 17.

[10]George, G. BIG DATA AND MANAGEMENT. Academy of Management journal. (04/01/2014) ,  57 (2), p. 321.