Database Administration: DBMS, Security, and DBA Roles

Introduction

In evaluating the current field of database administration, several areas demand close attention: Database Management Systems (DBMS), Database Administrator (DBA) roles and responsibilities, database design concepts, database performance, and data integrity and availability. Database security is equally essential — as evidenced by the many breaches that occur on a near-daily basis — for any organization to continue operating effectively. The breach at social networking site Twitter is particularly troublesome given that platform's rapid growth and the volume of sensitive information stored on its servers. Additional critical areas include database backup and database recovery. Each of these topics forms the foundation of a DBA's many tasks in supporting an organization.

The intent of this paper is to provide insights into each of these areas from a DBA standpoint, examining them as strategic concerns relative to the value of data within an organization. Critical success factors for the development of effective database management systems (Guynes & Vanecek, 1996) include thorough systems analysis and design, the development of security and fault-tolerance plans, and the development of change management programs to ensure that departments, employees, and external customers can effectively use the systems designed. Database security forms the foundation of DBMS best practices (Jajodia, 1996) and also serves as a precursor to Service-Oriented Architectures (SOA) and the use of DBMS taxonomies for the creation of Web Services based on the XML language protocol (Erickson & Siau, 2008). All of these factors are taken into account throughout the analysis of DBMS operation, optimization, management, security, scalability, and future trends relative to SOAs and Web Services-based designs.

Database Management Systems

The evolution of databases from simple hierarchical structures to enterprise-wide DBMS platforms parallels their growth from being purely focused on IT tasks to being entirely focused on business and strategic processes and objectives. For this transition to occur, databases had to move away from purely hierarchical designs and adopt more relational data structures that treated each data element as a logical — not necessarily physical — element of the database (Greene, 2008). In order for databases to become more aligned with the business needs of companies, the shift from hierarchical to relational and object-based structures began decades ago and continues today. This shift allows databases to support logical table structures, record definitions, and logical connections across multiple databases regardless of their physical location.

Corresponding to the growth of relational databases is the increasing sophistication of data modeling (Geiger, 2009) and the continued development of constraint-based analysis and optimization routines to ensure high-performance queries and related applications (Benedikt, Dong, Libkin, & Wong, 1998). The development of query-based database logic in conjunction with constraint-based engines for database application development is leading to the creation of configurable Web Services (Firat, Wu, & Madnick, 2009).

The variation in data models has progressed rapidly, with the development of multidimensional tables that can pivot data sets to reveal insights not intuitively obvious from looking at the data itself. Multidimensional analysis using constraints and rules-based logic is also being applied to complex business applications, including optimizing supply chains and creating more effective distributed logistics networks (Segev & Zhao, 1994). The ability to define additional data structures through the many languages used for creating, augmenting, and improving database performance — and integrating databases into enterprise applications — is predicated on structuring databases to be more process-centric rather than purely functional in orientation (Post & Kagan, 2001). One of the primary catalysts of this process-centric approach is the widespread adoption of XML as a communications protocol, which has been continually improved in terms of security and scalability (Roth, Hernandez, Coulthard, Yan, et al., 2006). The role of XML is also critically important in the context of creating Web Services that rely on relational databases to enable both informational and financial transactions (Erickson & Siau, 2008). The use of Web Services within the broader framework of Service-Oriented Architecture is similarly predicated on databases that can scale over time, aligning with the rapidly changing needs of enterprises that rely on complex data analysis to evaluate strategies and initiatives.

DBMS systems have progressed beyond being mere data repositories and have become knowledge systems that rely on enterprise data management and Master Data Management (MDM) approaches for aligning content, intelligence, and knowledge to specific strategic needs (Reagan & Rowlands, 2007). DBMS is now more synonymous with strategic planning than ever before, and has become embedded in many of the planning, execution, and monitoring processes of companies, including strategic market planning (Weinberger, 2004).

DBA Roles and Responsibilities

The roles and responsibilities of DBAs have changed significantly — from support staff who keep database software and systems running to professionals who complete complex projects as part of broader teams or independently. As a result, the DBA role is now characterized more by collaboration and problem-solving than by purely technical expertise. There needs to be a balance among collaboration, strategic vision of where the company is heading and how databases contribute to that direction, and the ability to translate user preferences and needs into real-time data modeling (Konana, Gupta, & Whinston, 2000). These requirements shift the DBA from a single contributor to a project management-oriented role. Consequently, the ability to continually manage and coordinate people, processes, and resources in alignment with critical-path timelines is one of the most important skills any DBA can develop.

In conjunction with this collaborative function, the DBA must also anticipate and respond to the need for making workflows as agile as possible and aligned to both system-user goals and internal requirements (Smith, Smarkusky, & Corrigall, 2008). DBAs' roles therefore also require establishing exceptionally clear communication processes between teams — particularly important because many DBAs are involved in developing internal applications that depend heavily on databases to deliver useful results.

While technical expertise remains essential, DBAs also need the ability to create significant value through strategies that impact the company positively over time. Studies have shown that the ability to translate and teach technical skills is as important as — if not more important than — pure technical acuity. It lies in the ability to teach others, assist them in reaching their goals, and help them see how their contributions make a meaningful impact on strategic initiatives and results (Smith, Smarkusky, & Corrigall, 2008). DBAs must be able to define roles within projects and create programs that lead to more complex, shared objectives over time. The DBA as a strategic-level project management leader also needs a high degree of shared accountability, transparency, and responsibility — all aimed at earning trust as a leader. DBAs must also nurture a shared sense of purpose and keep teams motivated to accomplish longer-term goals. As these requirements make clear, DBAs are much more than technicians who keep systems running; they are critical members of strategic teams who provide guidance and leadership in the attainment of complex, cross-functional goals and objectives.

References

Michael Benedikt, Guozhu Dong, Leonid Libkin, & Limsoon Wong. (1998). Relational expressive power of constraint query languages. Journal of the Association for Computing Machinery, 45(1), 1–34.

Elisa Bertino, & Ravi Sandhu. (2005). Database security — concepts, approaches, and challenges. IEEE Transactions on Dependable and Secure Computing, 2(1), 2–19.

Danahy, J. (2008). The 'phasing-in' of security governance in the SDLC. Network Security, 2008(12), 15–17.

Dolgicer, M. (1993, June). The ACID test for distributed transactions. Data Communications, 22(9), 25.

Erickson, J., & Siau, K. (2008). Web services, service-oriented computing, and service-oriented architecture: Separating hype from reality. Journal of Database Management, 19(3), 42–54.

Firat, A., Wu, L., & Madnick, S. (2009). General strategy for querying web sources in a data federation environment. Journal of Database Management, 20(2), 1–18.

Geiger, J. G. (2009). Why build a data model: A business data model provides many benefits to a BI project. Information Management, 19(5), 42.

Greene, A. (2008). Managing subject guides with SQL Server and ASP.Net. Library Hi Tech, 26(2), 213–231.

Guynes, C. S., & Pelley, L. (1993). Monitoring database performance in an end-user environment. Journal of Systems Management, 44(8), 27.

Guynes, C. S., & Vanecek, M. T. (1996). Critical success factors in data management. Information & Management, 30(4), 201.

Iwaihara, M., Hayashi, R., Chatvichienchai, S., Anutariya, C., & Wuwongse, V. (2007). Relevancy-based access control and its evaluation on versioned XML documents. ACM Transactions on Information and System Security, 10(1), 1.

Jajodia, S. (1996). Database security and privacy. ACM Computing Surveys, 28(1), 129.

Konana, P., Gupta, A., & Whinston, A. B. (2000). Integrating user preferences and real-time workload in information services. Information Systems Research, 11(2), 177–196.

Pangalos, G. (1994). A tutorial on secure database systems. Information and Software Technology, 36(12), 717.

Post, G., & Kagan, A. (2001). Database management systems: Design considerations and attribute facilities. The Journal of Systems and Software, 56(2), 183–193.

Ma, J., & Orgun, M. A. (2008). Formalising theories of trust for authentication protocols. Information Systems Frontiers, 10(1), 19–32.

Reagan, J., & Rowlands, I. (2007, January). Key technologies enabling a seismic shift in enterprise data management. Business Intelligence Journal, 12(1), 17–25.

Smith, H., III, Smarkusky, D., & Corrigall, E. (2008). Defining projects to integrate evolving team fundamentals and project management skills. Journal of Information Systems Education, 19(1), 99–110.

Segev, A., & Zhao, J. L. (1994). Rule management in expert database systems. Management Science, 40(6), 685.

Son, S. H., & Choe, K. M. (1988). Techniques for database recovery in distributed environments. Information and Software Technology, 30(5), 285.

Weinberger, J. (2004, October). Database marketers mine for perfect customer segmentation. Customer Relationship Management, 8(10), 19.

Winkler, H., & Seip, H. (2006). Employment analysis of ERD vs. UML for data modeling. Journal of Information Systems Education, 17(1), 65–72.

Zdonik, S. (2008). Revolutionize database performance. DM Review, 18(1), 20.