Data Quality Challenges in Net-Work Automation Systems Case Study of a Multinational Financial Services Corporation - van Roozendaal, Jan (fi=Turun yliopisto. Turun kauppakorkeakoulu|en=University of Turku. Turku School of Economics|, 2016)
With the emerging trends of IPv6 rollout, Bring Your Own Device, virtualization, cloud computing and the Internet of Things, corporations are continuously facing challenges regarding data collection and analysis processes for multiple purposes. These challenges can also be applied to network monitoring practices: available data is used not only to assess network capacity and latency, but to identify possible security breaches and bottlenecks in network performance. This study will focus on assessing the collected network data from a multinational financial services corporation on its quality and attempts to link the concept of network data quality with process automation of network management and monitoring. Information Technology (IT) can be perceived as the lifeblood within the financial services industry, yet within the discussed case study the corporation strives to cut down operational expenditures on IT by 2,5 to 5 percent. This study combines both theoretical and practical approaches by conducting a literature review followed by a case study of abovementioned financial organization. The literature review focuses on (a) the importance of data quality, (b) IP Address Management (IPAM), and (c) network monitoring practices. The case study discusses the implementation of a network automation solution powered by Infoblox hardware and software, which should be capable of scanning all devices in the network along with DHCP lease history while having the convenience of easy IP address management mapping. Their own defined monitoring maturity levels are also taken into consideration. Twelve data quality issues have been identified using the network data management platform during the timeline of the research which potentially hinder the network management lifecycle of monitoring, configuration, and deployment. While network management systems are not designed to identify, document, and repair data quality issues, representing the network’s performance in terms of capability, latency and behavior is dependent on data quality on the dimensions of completeness, timeliness and accuracy. The conclusion of the research is that the newly implemented network automation system has potential to achieve better decision-making for relevant stakeholders, and to eliminate business silos by centralizing network data to one platform, supporting business strategy on an operational, tactical, and strategic level; however, data quality is one of the biggest hurdles to overcome to achieve process automation and ultimately to achieve a passive network appliance monitoring system.