Frank Armour

Big Data: Issues and Challenges Moving Forward

Big data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. Data, information, and knowledge are being created and collected at a rate that is rapidly approaching the exabyte/year range. But, its creation and aggregation are accelerating and will approach the zettabyte/year range within a few years. Volume is only one aspect of big data; other attributes are variety, velocity, value, and complexity. Storage and data transport are technology issues, which seem to be solvable in the near-term, but represent longterm challenges that require research and new paradigms. We analyze the issues and challenges as we begin a collaborative research program into methodologies for big data analysis and design.

Enterprise Architecting: Critical Problems

An enterprise architecture (EA) identifies the main components of the organization, its information systems, the ways in which these components work together in order to achieve defined business objectives, and the way in which the information systems support the business processes of the organization. The components include staff, business processes, technology, information, financial and other resources, etc. Enterprise architecting is the set of processes, tools, and structures necessary to implement an enterprise-wide coherent and consistent IT architecture for supporting the enterprises business operations. It takes a holistic view of the enterprises IT resources rather than an application-by-application view. Given the size and still immature nature of many enterprise architecture efforts, a number of critical challenges and problem continue to exist. This paper surveys a number of these challenges and problems in an attempt to provide a platform for a discussion on enterprise architecture problems and possible solutions.

Building an enterprise architecture step by step

For most organizations, getting started may be the hardest part of building an enterprise information technology architecture. One reason is that people have only a hazy idea of how to use a systematic architecting process to achieve specific goals. The entire idea of enterprise architecting seems grand and out of reach, so they feel more comfortable chipping away at it with patches. Unfortunately, these patches evolve to something only slightly more sophisticated. Some efforts never get that far. The architects get caught in a never-ending series of analyses and end up with nothing but a long to-do list just as the money runs out and the CEO expects to start seeing a return on investment. The article shows how to scope the project, set up the development team, and form a target architecture vision.

Enterprise architecture: agile transition and implementation

In our previous articles, we made the case for having an enterprise architecture and discussed the first phases of an architecture development process. The second article concentrated on describing the baseline architecture and defining the target architecture. We complete our discussion of the methodology by focusing on transition and implementation planning. Transition planning focuses on deriving a time-phased set of actions to achieve a given goal-in this case, implementation of the target architecture. Large organizations will remediate, renovate, or replace many systems concurrently. In doing so, they must recognize interdependencies among systems and accommodate them in activity scheduling. Implementation planning has a different time frame and a different audience. It maps resources (people, places, things, and funding) to transition planning activities.

A UML-driven enterprise architecture case study

The U.S. Capitol Police (USCP) are responsible for protecting the Capitol Hill complex encompassing the Senate and House office buildings and the Capitol building. Currently, the USCP operate numerous legacy systems, many of which exist as stovepipes. They are modernizing their suite of administrative and law enforcement systems. The USCP recognized that developing an enterprise architecture was an essential first step to ensure that the proper solution was selected, that key integration points between the individual systems were understood, and that a prioritized set of initiatives to remediate, renovate, or replace information systems could be planned and executed. We describe how to use standard UML models to capture architectural view information and some specific extensions that address areas in which UML is limited.

Mentoring for success

IT professionals must apply new information on a continuing basis. How does an organization ensure that its developers are up to speed and effectively applying the latest technology? Companies have long since discovered the value of formal training, but transferring development skills and advanced technology also requires hands-on, face-to-face assistance. The paper considers how effective mentoring is key to providing this support. A mentor has the experience and knowledge needed to offer expert guidance. Mentoring can be informal, with one team member helping another with a specific problem as needed, or it can be much more formal, with the organization providing structured, systematic support.

Advanced Analytics -- Issues and Challenges in a Global Environment

Modern businesses require a suite of appropriate analytical tools - both quantitative and qualitative - that go beyond data mining, because of issues with scalability, parallelizability, and numeric vs. symbolic representation that may well affect analytic utility and the results of an analytic. However, there is limited formal or structured guidance for new and complex problem spaces providing criteria for what analytics to use and how they are to be cascaded or integrated to obtain useful results and generate a range of alternate explanations of what is happening now, what is likely to happen soon, and what could happen in the long term. In this paper, we describe sixteen classes of analytics in which we extend previous work by Kaisler and Cioffi-Revilla [7]. We examine some issues and implementation challenges for analytics in the global business environment. We suggest several applications of these analytics to modern business problems and draw several conclusions that lead to further research.

Introduction to Big Data: Challenges, Opportunities, and Realities Minitrack

Big Data is still a maturing and evolving discipline. Big data databases and files have scaled beyond the capacities and capabilities of commercial database management systems. Structured representations become a bottleneck to efficient data storage and retrieval. Gartner has noted four major challenges (the four Vs): increasing volume of data, increasing velocity (e.g., in/out and change of data), increasing variety of data types and structures, and increasing variability of data. We have suggested a fifth V: value, which is the contribution big data has to decision making. Add to these the increasing number of disciplines and problem domains where big data is having an impact and one sees an increase in the number of challenges and opportunities for big data to have a major impact on business, science, and government.

The Big Data Analytics Gold Rush: A Research Framework for Coordination and Governance

Big data analytics (BDA) is rapidly becoming a most critical activity for organizations. Examples of how BDA enhances the competitive advantage of organizations abound in the literature, which has created a gold-rush-like atmosphere. The predictions about shortages of specialized analytical labor has caused every major university to launch big data, analytics or data science programs, and every major organization to adopt aggressive BDA initiatives. The promise of enhanced competitiveness and sustainable strategic advantage are appealing and virtually all major decisions need to be backed up with data. This has substantial implications for organizations, which need to align their business process architectures, data architectures and data management practices to support BDA initiatives. But while most research in this area has focused on either building BDA capabilities or on the benefits that these capabilities bring to organization, we know little about how this effort can be most effectively coordinated and governed to ensure organizational success. In this research we develop a research framework to study whether and how coordination and governance influence BDA outcomes. We build on the foundations of coordination theory and information systems governance.

Enterprise Architecture: Challenges and Implementations

Enterprise Architecting (EA) is the process of developing enterprise Information Technology architecture -- both its description and its implementation. An EA description focuses on a holistic and integrated view of the why, where, and who uses IT systems and how and what they are used for within an organization. An enterprise architect develops the strategy and enables the decisions for designing, developing, and deploying IT systems to support the business operations as well as to assess, select, and integrate the technology into the organization¿s infrastructure. Alignment between business and IT is one of the top issues for CIOs and IS managers for several years as report.