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Introduction

This book, Effectively Managing LDS Data, is the third in a four-part series about longitudinal data systems (LDS). The first book in the series, What is an LDS?, focused on the fundamental questions of what an LDS is (and what it is not), what steps should be taken to achieve a sound system, what components make up an ideal system, and why such a system is of value in education. The second book, Planning and Developing an LDS, focused on the early stages of LDS development to help state and local education agencies determine what they want their LDS to accomplish and what they need to achieve these goals. The present installment discusses organizational issues aimed at moving the project forward; and at ensuring the data are of high quality so that users may leverage them with confidence for data-driven decisionmaking. It looks at the establishment of governance structures and processes, getting the right people in place, and creating committees and working groups of diverse expertise to oversee and inform the process. This process is ultimately aimed at improving data quality and increasing the use of those data to improve education. This book also explores ways to ensure data quality through staff training, validation procedures, and establishment and adherence to data standards. Finally, the document discusses the challenges of securing the system to protect individual privacy and the confidentiality of education records.

Figure 1 lays out the major issues discussed in each of the four books in this series. For more information on the purpose, format, and intended audience groups of this series, see Book One: What is an LDS?


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LDS Lore:

The ungoverned education agency

The history and consequences

The state education agency’s data collection and management practices had come about over time, driven mainly by compliance and funding. Various program areas were created to focus on specific federal surveys, and staff collected the data needed to do their jobs. Program area staff administered the surveys, followed their own quality assurance processes, and maintained and secured the data in their own silo systems. And, of course, data were reported as required by the federal government. Individual managers took their own approach to directing staff and organizing work, and coordination across program areas was limited.

Over time, different departments began to collect some of the same data elements. Inconsistencies were commonplace. For instance, while the student information system listed an “Aileen Hutchinson,” who was not in special education, the Special Education Department’s system included a girl named “Allie N Hutchinsen.” Despite these discrepancies, the staff knew the two records referred to the same student based on other directory information. However, structural differences between the systems—different definitions, formats, and option sets — further complicated matters. For example, Allie’s race, White, was coded as “1” in one database and “2” in the other because the systems used different option sets. Furthermore, because program areas defined their own data elements and used different software to manage them, the ability of the agency’s many data systems to “talk” to one another varied from limited to nonexistent, burdening staff with redundant data entry work and introducing errors into the system. With no clear requirement for documentation of data processes, methodologies often changed, as happened when Joe left the agency and no one else knew how to produce the dropout rate. The new guy, Steve, calculated it the way he had at his previous agency. No one saw a problem with this, especially since Steve’s numbers were lower than Joe’s. But when the time came to compare the new rates to previous years,’ some staff realized that they were comparing apples to oranges.

Year in and year out, the work was done, but specific tasks weren’t assigned to anyone in a consistent manner, and sometimes reports were late or incomplete. And since there was no “official” source for each data element, data for federal reports might come from one source one year, another the following year.

Similarly, without clear guidelines, staff fielded data requests as best they could. When inquiries came in, the recipient would decide to which program area the request should be sent. In one instance, Talia was asked for the school addresses in a district. While McKenna and Vita both managed school directory data in separate systems, Vita was given the task because her desk was near Talia’s.

When postsecondary education leaders asked about sharing data, agency officials cried “FERPA!,” invoking the federal privacy law because they mistakenly thought it prohibited such exchange (see chapter 7). Security and access protocols were not well understood, and staff often took a lax approach to protecting sensitive information (see “Identity theft in the printer room” in chapter 8). Data quality problems were handled as they arose, but no long-term changes were made to ensure the same issues wouldn’t crop up again—like treating symptoms as they flare up, but never working to cure the underlying illness. And since no responsible group or process was in place to identify the sources of the agency’s problems, they went undiscovered and data quality issues were just blamed on IT. This went on for some time and, other than the techies, most staff didn’t see much of a problem. It was simply business as usual.


The realization

Over time, the country became more interested in education data. Education stakeholders wanted more information for accountability purposes and to better understand what programs and instructional strategies worked. They wanted data to inform decisionmaking at all levels; and to improve administration, instruction, and student performance. The bottom line was, they wanted data from across the agency and they wanted them fast. This changing environment posed many problems for the agency. Requested analyses required linking across silos, or data integration in a central data store. Before this could happen, duplicate, inconsistent data had to be reconciled. However, once the integration work began, more inconsistencies were discovered than anyone had imagined. Data quality had to be a higher priority; security had to be improved; and the data elements collected had to serve business and stakeholder needs, not just meet federal requirements. Better methods of sharing data had to be devised if the agency was to meet the growing demand for a “P–20” system. And better, more consistent protocols were needed to make data sharing more efficient and prevent improper dissemination. The chief information officer decided something had to be done. Having seen a presentation at a national conference, he was convinced a process called “data governance” could help address the agency’s problems.



Conventions

Throughout this series, important terms and topics will be highlighted in sidebars. Notable subject matter will be easily identified by the following icons:

Definitions LDS lore
(fictional tales)
Key points
Bright ideas Tips District difference
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