Introduction
Successful executives have long understood that relevant and reliable data is a requisite to effective decision-making. Ironically, during the 1980s and 90s, the great promise of technology to capture massive quantities of data commonly resulted in the myopic decision to shoehorn technology into paper-based systems. For many companies, this typically meant buying one or more desktops and filling them with customized transactional and operational software. Many of these systems became nothing more than expensive paperweights because intended users had no idea how to use them or adapt them to existing processes. Decision-makers consequently heeded the calls to reengineer business processes leading many of their organizations into a radical and disruptive period of change, which often resulted in downsizing to the point of oblivion. Now, in hindsight, decision-makers of the organizations that managed to survive their early technology decisions readily admit they would have done things much differently. From the author’s experience as a technology implementer of that time, the single most critical mistake decision-makers made was failing to align their technology choices with corporate goals, which requires probing the following questions:
- What are the goals and how can achievement of them be quantified and measured?
- What people and products directly and indirectly influence goal achievement?
- What data about people and products needs to be captured?
- What processes need to be changed or implemented to capture the data?
- What database alternatives exist that best support the processes?
- Can existing data and processes be adapted to them?
- Who will perform the tasks associated with the identified processes and what training do they need?
- How will the data be analyzed and interpreted?
- What reporting channels are needed to keep appropriate stakeholders and other interested parties properly informed?
- How will the results serve as a catalyst for improvement?
Although the questions are presented in a logical order, in reality, the quest for answers is an iterative and dynamic process. The important thing to keep in mind is that clearly identified goals, people, products, data and appropriate measures drive the rest of the analysis. If these variables cannot be clearly identified, the rest of the analysis is useless and it is only after the answers to these questions are understood can effective decisions be made. To explore these questions, the author draws from her experiences in business. However, these questions equally apply in educational settings as educators face similar data capturing challenges to address school reform legislation, conduct educational research, strive to improve the educational experience and adapt to a changing educational landscape (Bloomfield & Cooper, 2003; Helmi, 2002; Labaree, 2003).
The purpose of this article is three-fold. First, to provide a historical perspective of the decisions made by executives, the problems they encountered and how these problems served as the impetus to continued advancements in database tools. It is hoped that educators will identify with one or more of the problems enabling them to better understand the database tools available to them and avoid similar fates. Second, to propose a framework for conceptualizing data flows that reflects how organizational culture provides the foundation for probing the critical questions that drive the decision-making process. A systemic framing of the problem helps to minimize the culture shock that naturally occurs in a period of change and helps keep the focus on the overall goal. The third purpose is to explore how the framework can be applied to support data capturing and decision-making in education.