how big data transforms the it department to a ...

HOW BIG DATA TRANSFORMS THE IT DEPARTMENT TO A STRATEGIC WEAPON

ABS TRACT Big Data is the creation of new flows of information enabled by a number of technological breakthroughs. Large quantities of data of varying structure can now be processed nearly in real-time (e.g. improve sales forecasts and customer analyzes) to improve business process in enterprises and along the value chain. By this means, disruptive changes of the information flows within organizations are enabled. In the past IT departments have lost much of their organizational impact due to outsourcing and cloud-computing. Big Data reverses this development by increasing the strategic relevance of IT departments. Therefore, this paper outlines the impact of Big Data on the role of IT departments in enterprises. By providing valuable insights based on large amounts of data, the IT department becomes a strategic weapon. KEYWORDS Big Data, Business Intelligence, Data-Warehouse, Data-M ining, IT Business Alignment

1. INTRODUCTION Big Data is one of the most disruptive information technological developments (Bughin, Chui, and Manyika 2010). (Zikopoulos and Eaton 2011) characterize Big Data with the three properties - Volume, Velocity and Variety. Big data applications handle data-intensive applications, which are described by a large volume of data, a specific velocity of processing and a data variability of the existing IT solutions. Big data enables handling and analyzing more types of unstructured (e.g. user statements in social media) and semi-structured data as before (LaValle et al. 2011). An example scenario for Big Data is the provisioning of real-time information to mobile users. Based on a stream of position information, information valuable to the mobile user has to be selected from a variety of sources and provided nearly in real-time. For a long time, IT departments has been regarded as a cost-driver and collection of risks (Carr 2004). ITdepartments have been compared with the production of utilities such as electricity (Carr 2004). Therefore, a multitude of outsourcing approaches has been developed. On the other hand, (Brynjolfsson, Hofmann, and Jordan 2010) show that the utility model is not applicable to complex IT resources. Complex IT resources may provide strategic advantage. Big Data can be seen as a further development of business intelligence (BI) to the three "V" (Chen, Chiang, and Storey 2012). In comparison to BI it is now possible to analyze large quantit ies of data from different data sources and with different structure processed nearly in real-time. The business impact of Big Data is shown in various examples of real business cases (McAfee and Brynjolfsson 2012). Significant cost cuts could be achieved by decreasing the estimated and actual arrival time of aircrafts. Furthermore, retailers such as SEARS can increase sales through faster data analysis and thereby better personalized promotions. Today, research on big data focusses on technological aspects. New technologies such as the globally distributed spanner database attract a large amount of attention (Corbett et al. 2012). There is also plenty of research on the business impact of big data in general. However, only little research is done on the organizational impact of Big Data (Schmidt 2013) and especially the influence on the role of IT departments in organizations. The importance of this theme is supported by an empirical study (worldwide online survey with over 1300 IT managers) from ZDNet "70% will use data analytics by 2013" (ZDNet 2012). Therefore, this article investigates the influence of Big Data on the standing of the IT in enterprises and organizations.

2. STRATEG IC ADVANTAGES PROVIDED BY BIG DATA With Big Data the IT department steadily moves up from a "business supporting unit" to an essential and central source of mission critical information. The possibilities of Big Data and the structure of this solution to improve business processes along the supply chain are illustrated in the chart below. Improved Business processes (e.g. reduce process cost, time and improve process quality) based on a better data quality can be in the enterprise ("B" in Figure 1), at the interface to the suppliers ("A" in Figure 1) and at the interface to the customers ("C" in Figure 1).

suppliers

enterprise

customers

…

… B

C

impact to business

decisions social media, search requests, content provider etc.

Figure 1.

data analyzes

data sources

Data from sales, production, purchasing etc.

Big Data

IT department

Business

A

Strategic advantages provided to business by Big Data (according to Fayyad 1996)

Advantages at the interface to the customers Big Data and the latest IT technology components are fundamental to improve order forecast because the behavior of consumers has changed relevantly (Sandhu and Corbitt 2003). The companies often make all decisions for their consumers and this can be seen best by the strategy of Amazon (Chaffey 2008). Through data mining tools they analyze customer data and develop a product comparison data to gain more detailed knowledge about customer needs. Therefore, they divide their consumers into different categories and can offer those products to a wide variety of consumers that meet the same requirements. The data thus collected has to be summarized for best evaluation and usage. Due to this received information, storage can be limited to a small level and therefore expensive storage costs can be reduced. Based on the knowledge gained by order forecasts, companies are able to improve their value -added chain. Therefore, Barnes & Noble 1 are for example able to deliver their customer orders in Manhattan on the same business day on which it was ordered. In future, it will be possible to extend the same day delivery service to more regions As social networks and blogs gain in importance, research often focu ses on sentiment analysis and opinion mining. Sentiment analysis helps the researchers to derive better marketing strategies through analyzing emotion icons in blog entries. On the basis of these icons it is possible to indicate the users’ emotional state about different or specific products (Pak and Paroubek 2010). Similar to sentiment analysis, opinion mining is a way to obtain the preferences of consumers. By orientating on subjective terms in documents researchers

1

(http://www.barnesandnoble.com/help/cds2.asp?PID=8112#3)

are able to discover the consumers’ opinion about the topic (Esuli and Sebastiani 2006). In this way, hidden knowledge can be revealed. However, these approaches could be seen as an interference with private life and help enterprises to improve product developments, sales forecasts and specific customer requests. As Big Data supports the decision-making process, therefore decisions can be made faster and in real-time. This (nearly) real-time data could be provided by the IT department as a kind of "live -data-feeds" and different departments - and even other companies (Forbes 2010) - could subscribe to those fundamental datastreams. Especially in the field of market analysis, the collection of data has a high impact and significance . Due to that fact, more data also offers more space for misinterpretation or false correlations, and if a company relies on false discoveries, the company will not be competitive anymore. Data mining tools as a part of Big Data are in particular applied in the customer relationship management for analyzing the data about present and potential customers, business partners and suppliers (Ngai and Florian 2001).

Advantages in the enterprise Nowadays, companies are faced with huge amounts of data (Lynch 2008) - estimated "Walmart collects more than 2.5 petabytes of data every hour from its customer transactions" (McAfee and Brynjolfsson 2012) which need to be captured, stored and cured. This increasing amount of data and interdivisional role poses new challenges to the IT because "more data" not automatically means "better data. In detail, the challenge is to get large amounts of unstructured data in an organized manner. In this way, the company gets the great opportunity to extract the valuable and essential information because "Data driven decisions are better decisions" and often a better foundation compared to intuition (McAfee and Brynjolfsson 2012). Better decisions allow the enterprise to optimize their processes by reducing process costs and time as well as raising process quality. A data analyze with focus on Big Data can lead to accurate forecasts and costumer relationship management and so decisions are more specific. For example, a more detailed sales forecast supports a better warehousing and logistical infrastructure. In this way, a company gets the opportunity to implement a competitive business strategy. Continuous analysis of internal data in combination with miscellaneous mechanisms (e.g. thresholds) could start predefined workflows or processes for further action (e.g. fraud detection). On top of that, the IT department could provide a kind of "self-service portal" as a simple to use high-level interface that allows the different divisions of the enterprise to create real-time reports that are suited for their individual needs of manage compliance and support audits.

Advantages at the interface to the suppliers Better sales forecasts based on a good data quality and processing in real-time supports a consistent information flow between the suppliers. Through this, they reduce warehousing costs and continuance material flow. Therewith the high level of maturity establishment in the supply chain is secured. Big Data allow a processing of different data sources for tracking and handle shipping's, through this Supply Chain Event Management (Otto 2003) is more enabled in real-time than in the present. Moreover data from different sources (e.g. also from public internet sites) and in different formats (e.g. ad -hoc text based weather forecasts and notice from organizations) combined with already established internal enterprise information about shipping's. Furthermore evaluation of suppliers safe costs by stock keeping (e.g. based on better knowledge about vendors and products). Through those new data sources (e.g. online ratings and evaluations of suppliers, public directories etc.) it is possible to respond to changes in the flow of goods at an early stage.

3. CONCLUSION Big Data creates new strategic advantages for enterprises and IT departments. Data from different sources, formats and quantities can be processed nearly in real time. Sales forecasts, decision-support, sentiment analysis, customer needs and product range can be improved and raise earnings and competitiveness. An integrated implementation of Big Data can boost the standing of the IT in enterprises from a cost -driver to a

strategic weapon. Business cannot implement this approach without the IT department because of required specialized knowledge of the data, data structure and data sources in relation to the business case. Despite the advantages, there are also risks associated with the use of Big Data. One of these risks is the data protection (e.g. data from content provider) and the protection of privacy of persons and organizational units (Bollier and Firestone 2010). Because of the rapid analysis of the data, algorithms could made wrong decisions, which can have fatal impact on business and privacy of people. Some limitations have to be discussed. Not in all cases, a larger quantity of data implies a better quality of data and decisions. For each case, a proof of concept is needed. A distinction between industry sectors has to be developed. IT departments in some industry sectors could improve more than other (e.g. internet banks as small retailers) and have a better return on investment. Furthermore, an integrated empirical study with all aspects available to support the argumentation in in all facets has to be done. The research agenda for the future may contain an empirical study about the impact of Big Data for the standing of it departments and in comparison to different countries and business process es (in and between enterprises). Therefore, it may be possible to make this impacts clear for the management and show at which point or interface in the enterprise Big Data generates the highest profit. Furthermore risks and possibilities of data (e.g. from external content provider) and influencing factors to the IT Business Alignment must be examined.

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