EARLY CHILDHOOD DATA SYSTEMS IN MICHIGAN. Prepared for the Michigan Early Childhood Investment Corporation

Transcription

1 EARLY CHILDHOOD DATA SYSTEMS IN MICHIGAN Prepared for the Michigan Early Childhood Investment Corporation Sarah Friese Elizabeth Groginsky Carlise King March

2 TABLE OF CONTENTS Table of Contents... 2 Summary and Key Recommendations... 3 Project overview... 4 Methodology... 4 MICHIGAN EARLY CHILDHOOD DATA SETS AND SYSTEMS...4 GENERAL INFORMATION ABOUT COORDINATED, INTEGRATED DATA SYSTEM...5 PROFILES OF OTHER STATES DATA SYSTEMS...5 Integrated Data Systems in Early Childhood... 6 EARLY CHILDHOOD DATA COLLABORATIVE...6 THE RACE TO THE TOP-EARLY LEARNING CHALLENGE AND OTHER SUPPORTS FOR COORDINATED EARLY CHILDHOOD DATA...8 DATA GOVERNANCE IN EARLY CHILDHOOD...9 Early Childhood Data in Michigan OVERVIEW OF MICHIGAN S EARLY CHILDHOOD DATA SOURCES MICHIGAN S PROGRESS ON THE ECE 10 FUNDAMENTALS DATA GOVERNANCE IN MICHIGAN State Profiles ILLINOIS DATA GOVERNANCE PROFILE MISSISSIPPI STATE GOVERNANCE PROFILE PENNSYLVANIA STATE GOVERNANCE PROFILE Recommendations Appendix I: Interviewee List Appendix II: Data set profiles

3 SUMMARY AND KEY RECOMMENDATIONS Michigan launched the Great Start Initiative in 2003 to ensure that families would have the resources they need to help their children thrive from birth to age five. In 2005, the Early Childhood Investment Corporation (ECIC) was created to mobilize public and private resources to create a more comprehensive early childhood system. Michigan continued its commitment to improving the lives of young children through the creation of the Office of Great Start in To build on the important work being done at the Office of Great Start, the ECIC, and other organizations that address the needs of young children, Michigan needs a coordinated and integrated early childhood data system that provides easy access to the wealth of information collected by state departments and other non-profit organizations. A coordinated data system will provide on-the-ground service workers and their managers, department directors, and policy-makers with the information they need to improve service delivery, meet mandated reporting requirements, address research and evaluation questions, and identify areas of strength and weakness. With this information, Michigan will be better able to assess the developmental outcomes of young children to improve their readiness for school and prepare them for life. Michigan should focus on the following changes to move towards a more coordinated early childhood data system. 1. Determine the focus of a Michigan early childhood coordinated data system and the policy questions that would be answered with data from that system. 2. Engage data stakeholders in a process to review the state s early childhood policy priorities and create a plan for implementation of a data governance structure that aligns with existing P-20 data efforts. 3. Implement a data governance structure with a low to moderate level of centralization that provides, at a minimum, guidance and systemization to the policies and procedures that would facilitate data sharing across state departments and other external organizations. 4. Progress towards a more centralized, integrated data system with data still maintained by individual data owners but with a strong central group that provides data matching, cleaning, and management services. 5. Continue working towards adoption of a statewide Unique Identifier Code that would allow for easier data connections. 6. Expand the type and amount of data collected on children and families for a better understanding of their demographics and developmental outcomes. 7. Continue working towards full attainment of the other 10 Fundamentals. EARLY CHILDHOOD DATA SYSTEMS IN MICHIGAN Sarah Friese Elizabeth Groginsky Prepared for the Michigan Early Childhood Investment Corporation Carlise King March

4 PROJECT OVERVIEW In November 2011, the ECIC on behalf of the Great Start Early Learning Advisory Council contracted with Child Trends to complete a project describing Michigan s early childhood data systems, identifying areas of promise and of challenge, and proposing short, intermediate, and long-term steps toward building a coordinated system. This report will address four areas of interest as Michigan pursues a coordinated early childhood data system. Those four areas are: Contextual information about the history and importance of data coordination and integration in the field of early childhood; The state of early childhood policy and data in Michigan; The governance structures that other states with coordinated early childhood data systems have implemented; and Recommendations regarding the next steps for Michigan to move from its current decentralized data structure to a more coordinated one that better meets the early childhood policy goals of the state. METHODOLOGY Data was collected for this project primarily through interviews and a review of existing documentation about early childhood data systems. There were three types of interviewees: those with knowledge about an early childhood data set in Michigan, those with expert-level knowledge about integrated data systems in early childhood, and those with knowledge about an early childhood data system in another state. Michigan Early Childhood Data Sets and Systems Between July and October 2012, 21 semi-structured phone interviews were completed by a Child Trends research analyst with 23 key informants who had in-depth knowledge about an early childhood data system in Michigan (Appendix I: List of interviewees). Interviewees were most often program directors with broad knowledge about all aspects of their programs, including the storage and management of data. Occasionally, staff responsible for the technical aspects of data system management joined these calls. All of the interviews took approximately 30 minutes to an hour to complete and a few interviewees completed a second interview. Some interviews were completed individually and some were completed as a group. Interviewees were asked questions on topics such as, the types of data housed in their system, unique identifiers used, current data sharing agreements, system supporting documentation, and technical specifications. The results of these interviews are described in detail in Appendix II. Child Trends staff completed a review of the existing technical documentation for each of the early childhood data systems discussed during the interviews. These documents included data dictionaries, 4

5 training manuals and videos, data element descriptions, data sharing agreements, and data architecture maps. Two interviews were also completed with technical staff who had high-level knowledge about data management in Michigan one with a staff member from DTMB who oversees the DHS data warehouse and the other with the head of the Office of Shared Solutions for contextual information about progress related to data coordination in Michigan. An interview with the Deputy Superintendent of the Office of Great Start was completed in January This interview provided contextual information about the state s early childhood policy priorities. General Information about Coordinated, Integrated Data System In September 2012, two interviews were completed with three experts in the field of coordinated data systems to provide contextual insight about the history and evolution of data system work in early childhood. In addition to interviews, a thorough review of the available literature about integrated, coordinated data systems in early childhood was completed. Profiles of Other States Data Systems Four interviews with directors of early childhood data systems in other states were completed to create profiles of how other states have approached the coordination their data systems. These semi-structured interviews were completed over the phone during the month of February The interviews lasted approximately an hour. Additional documentation detailing the profiled states data systems was reviewed. A. ACRONYM LIST Bureau of Children and Adult Licensing (BCAL) Center for Educational Performance and Information (CEPI) Child Care Development Fund (CCDF) Child Development and Care (CDC) Department of Human Services (DHS) Department of Community Health (DCH) Department of Technology, Management, and Budget (DTMB) Early Childhood Data Collaborative (ECDC) Early Care and Education (ECE) Early Childhood Investment Corporation (ECIC) Great Start Readiness Program (GSRP) Head Start Collaboration Office (HSCO) Kindergarten Entry Assessment (KEA) Master Person Index (MPI) Michigan Department of Education (MDE) Michigan Interagency Coordinating Council (MICC) Michigan Student Data System (MSDS) Office of Great Start (OGS) Office of Shared Solutions (OSS) Race to the Top Early Learning Challenge (RTT-ELC) Statewide Longitudinal Data System (SLDS) 5

6 INTEGRATED DATA SYSTEMS IN EARLY CHILDHOOD Differences in life trajectories can begin very early in children s lives. Narrowing the educational achievement gap between at-risk children and their low-risk peers requires policy-makers to have access to rich, longitudinal data that spans the domains of education, health, and economic experience. However, most states maintain data on young children in multiple, siloed systems. These systems are mostly uncoordinated and not accessible to the individuals who need to use the information housed in them to improve child outcomes. These uncoordinated data systems often lack important data points related to children s learning and development, program quality, and the early care and education workforce, limiting the potential to use the data to inform key decisions that affect the early experiences of young children. Growing recognition of the importance of coordinated data in early childhood is evidenced by the establishment of new groups like the Early Childhood Data Collaborative (ECDC), which focuses on providing information to advocate for greater sophistication in the use of data in the field of early childhood; the Race to the Top-Early Learning Challenge (RTT-ELC) program, which emphasized the importance of data in the provision of high quality care to young children, as well as other federal initiatives that provide support to states for greater data coordination; and the move towards focusing on data governance as the first step in establishing coordinated data systems. Early Childhood Data Collaborative The ECDC is playing an important role in addressing the lack of data coordination in the field of early childhood by providing information and assistance to states to develop more coordinated data systems. In 2010, the ECDC i released the 10 Fundamentals of a coordinated state-level early care and education data system to identify the most common early childhood data challenges. The 10 Fundamentals advocate for coordinated, longitudinal data-driven systems focused on providing information to guide states improved use of data (Textbox D). B. Glossary DATA SET A collected set of data elements collected for one program or purpose. For example, a data set could be the Michigan Student Data System, the DHS Licensing data set, or the greatstartconnect.org data set. DATA SYSTEM A data system is a collection of data sets housed and managed by a group. Examples of early childhood data systems are the multiple data sets managed by CEPI, the DHS data warehouse, or the Great Start to Quality QRIS and greatstartconnect.org managed by the ECIC. DEPARTMENT In this report, a department is a unit within the state government of Michigan such as the Department of Human Services or the Michigan Department of Education. ORGANIZATION An organization is a group outside of state government like the ECIC or Head Start agencies. Organizations might receive state funding but are not part of the permanent organizational structure. COORDINATED DATA SYSTEM A coordinated data system is one where multiple sub-data systems and sets are governed by a central body that provides guidance related to the policies and procedures for handling and sharing data. 6 INTEGRATED DATA SYSTEM An integrated data system builds on a coordinated one by also provided direct assistance in the management of individual data sets housed in different sub-data systems.

7 One of the first undertakings of the ECDC was to complete a 50 state survey to measure states progress toward implementing the 10 Fundamentals. The results of that survey revealed that, while every state collects at least some early care and education ECE data on individual children, data about program sites and/or members of the ECE workforce was not collected in a systematic way for all practitioners. Further, only one state could link data at the child, program site, and ECE workforce-levels. The survey also revealed significant deficiencies in the collection of data about the development of young children. D. THE 10 FUNDAMENTALS OF AN EARLY CARE AND EDUCATION (ECE) DATA SYSTEM 1. UNIQUE STATEWIDE CHILD IDENTIFIER. A single, non-duplicated number that remains with a child throughout participation in ECE programs and services. 2. CHILD-LEVEL DEMOGRAPHIC AND PROGRAM PARTICIPATION INFORMATION. Information such as age, ethnicity, socioeconomic status and program participation, including early intervention services for children with special needs. 3. CHILD-LEVEL DATA ON DEVELOPMENT. Developmental data collected from multiple sources (e.g., child observations, parent questionnaires) and the assessment of all domains of development. Data collection methods must be appropriate, valid and reliable, using scientifically sound instruments. 4. ABILITY TO LINK CHILD-LEVEL DATA WITH K 12 AND OTHER KEY DATA SYSTEMS. Linkages that allow policy-makers to track the progress of children over time, as well as better understand relationships among ECE programs and other programs that influence child development. 5. UNIQUE PROGRAM SITE IDENTIFIER WITH THE ABILITY TO LINK WITH CHILDREN AND THE ECE WORKFORCE. A single, nonduplicated number assigned to a school, center or home-based ECE provider. States also may assign unique classroom identifiers to identify individual classrooms within a site. 6. PROGRAM SITE DATA ON STRUCTURE, QUALITY AND WORK ENVIRONMENT. Structural data such as location; length and duration of the program(s) offered; and funding sources. Program quality data such as national accreditation information; child-adult classroom ratios; curriculum; and staff-child interaction measures. Work environment data such as the availability of professional development opportunities for staff; wages and benefits; and turnover. 7. UNIQUE ECE WORKFORCE IDENTIFIER WITH ABILITY TO LINK WITH PROGRAM SITES AND CHILDREN. A single, non-duplicated number assigned to individual members of the ECE workforce. 8. INDIVIDUAL ECE WORKFORCE DEMOGRAPHICS, INCLUDING EDUCATION AND PROFESSIONAL DEVELOPMENT INFORMATION. Demographics such as race/ethnicity, gender, age, educational attainment, experience in the field, retention and compensation. Data on program content and delivery, funding sources, financial aid, and monetary rewards for educational attainment. 9. STATE GOVERNANCE BODY TO MANAGE DATA COLLECTION AND USE. A body that establishes the vision, goals, and strategic plan for building, linking, and using data. They set policies to guide the collection of, access to, and use of the data. 10. TRANSPARENT PRIVACY PROTECTION AND SECURITY PRACTICES AND POLICIES. Transparent, publicly available policies and 7 statements that articulate how states ensure the security of the data and the privacy and confidentiality of personally identifiable information.

8 The ECDC 50-state survey found that no state had a data system that could help answer the following critical policy questions: 1. Are children, birth to age 5 on track to succeed when they enter school and beyond? 2. Which children have access to high-quality ECE programs? 3. Is the quality of programs improving? 4. What are the characteristic of effective programs? 5. How prepared is the ECE workforce to provide effective education and care for all children? 6. What policies and investments lead to a skilled and stable ECE workforce? The ECDC has emphasized that coordinated and integrated data systems that promote consistent business intelligence rules across departments for example, by establishing a consistent procedure for assigning a universal unique ID allow for the retrieval of information that can be used to answer the above questions. By taking steps to ensure that data are accessible and that stakeholders have the capacity to use the data to support decision making, states can more effectively improve the quality of ECE programs and the workforce, increase access to high-quality ECE programs, and ultimately improve child outcomes. Data, when used appropriately, can be a catalyst for system improvements and reforms. The Race to the Top-Early Learning Challenge and Other Supports for Coordinated Early Childhood Data The release of the ECDC s 10 Fundamentals also coincides with the emphasis placed on data systems in the Race to the Top-Early Learning Challenge program. In 2011, the U.S. Department of Education and U.S. Department of Health and Human Services released the RTT-ELC application ii which included an invitational priority focused on enhancing early learning data systems with an emphasis on linking to K-12 data (Textbox C). Of the 37 states that submitted applications, 30 selected the early learning data system priority. Of the 14 states awarded RTT-ELC funds, nine prioritized early childhood data system development (OH, MD, WI, NC, MA, NM, OR, WA, RI). The ECDC conducted an analysis of the states RTT-ELC applications that revealed the following trends in how states are focusing their data-related efforts: Making data accessible to improve and inform ECE practice and policy; Linking existing ECE data systems; Filling ECE data gaps, including workforce and child development data; Strengthening the connection between ECE data and data from other systems; and, Developing interagency data governance structures. States efforts to build and use longitudinal early childhood data systems have been supported by significant federal investments above and beyond the RTT-ELC. In 2009, competitive funding was made available to support the development of P-20 State Longitudinal Data Systems with a focus on establishing better linkages between early childhood, K-12, and workforce data. In that same year, funding 8

9 was made available to support the work of State Early Childhood Advisory Councils (SECACs) with a focus on the development of recommendations for a unified data collection system for public early childhood and development programs and services. An analysis of SECAC applications by the National Governor s Association, Center for Best Practices iii found that nearly every state dedicated funding to data system development with particular focus on analyzing gaps in current data systems and areas where existing data could be better linked across programs and agencies. Data Governance in Early Childhood Effectively coordinating data across many state departments and non-profit organizations requires a strong data governance structure. Governance structures are entities that establish authority and control over the management of data assets. These structures are responsible for setting statewide policies, processes, standards, definitions, and metrics in regards to the use of data. Data Management International (DAMA) has provided a functional framework (Figure 1) to help states move from decentralized data systems towards governance structures that focus on an enterprise perspective for managing data assets and information. The DAMA framework includes six domains of data governance: strategic business intent, goals and objectives, strategic intent of data management, organization, policies, and performance metrics. The data governance function is central to setting the priorities of these other domains of a state s coordinated data system. Figure 1. DAMA functional framework iv Strong data governance structures, based on best practices and processes, support states as they move from highly decentralized data systems towards an enterprise perspective in managing data assets and information. Effective data governance will ensure higher quality data, promote a higher level of 9

10 coordination and collaboration across state agencies, reduce duplication and increase the efficiency and effectiveness of state agencies v. While the importance of data governance is well-established generally, it is an emerging issue in the field of early childhood. Only a few states have established governance structures that direct the use of their early childhood data. South Carolina has a robust and long-standing statewide data warehouse that incorporates not just early childhood data but information from other fields like health, human services, and employment. Pennsylvania s Early Learning Network is a robust data system responsible for data management and governance activities exclusively related to early childhood. With the assistance of the federal investments described above, several other states, like Illinois and Ohio, are beginning development of their own early childhood coordinated data systems by establishing governance structures as the first step of their long-term plans for more advanced data warehouses. States with well-established data governance structures have shown that they can more efficiently answer questions about their residents and the services they are receiving. One of the initial, primary goals of Pennsylvania s Early Learning Network was to provide their legislature with evidence as to the efficacy of its early childhood programs vi. Equipped with this rich information they were better able to improve their service delivery to ultimately better meet the children in their state s needs. As more states establish coordinated or integrated data systems, the significance of these systems becomes more pronounced: they are one of the best ways to efficiently and effectively manage information about young children. 10

11 EARLY CHILDHOOD DATA IN MICHIGAN Understanding the importance of data coordination in the field of early childhood generally, in this section, we will examine the state of early childhood in Michigan specifically. This section includes a description of the strengths and challenges of the early childhood data systems in Michigan, its progress towards meeting the ECDC s 10 Fundamentals, and options for data governance structures that the state might pursue. Overview of Michigan s Early Childhood Data Sources Information from the set of interviews that was conducted with individual data owners was used to create a data map showing each of the departments and organizations collecting early childhood data in Michigan, the individual data sets within each organization, data sharing that is already occurring, and data sharing relationships that organizations would like to initiate (Figure 2). Departments and organizations are frequently responsible for multiple data sets and data sharing between those sets is usually occurring with ease. The connections shown are those that are occurring between different departments and organizations, often requiring a formal data sharing agreement. As the data map illustrates, there is a complex network of early childhood data in Michigan that is being collected and housed E. MICHIGAN OFFICE OF GREAT START In April 2011, Michigan Governor Rick Snyder announced his intention to create the OGS to act as the central group driving the state s early childhood efforts. Executive order established the OGS in June of that year and Susan Broman was named its head in November. OGS collaborates with other departments within state government as well as non-profits groups like the ECIC to improve child developmental outcomes and prepare children for school. Creation of the Office of Great Start is part of a larger Great Start initiative to cultivate public-private partnerships to improve school readiness and other outcomes for young children. The OGS s four goals for positively impacting child development and well-being are for children to be: Born healthy; Healthy, thriving and developmentally on track from birth to third grade; Developmentally ready to succeed in school at the time of school entry; and Prepared to succeed in fourth grade and beyond by reading proficiently at the end of third grade. The Office of Great Start is responsible for programs such as: Child Development and Care: Formerly in the DHS, CDC oversees the CCDF for providing child care assistance to families. Great Start Readiness Program: GSRP is Michigan s publicallyfunded preschool program operated by its intermediate school districts. Head Start Collaboration Office: The HSCO provides coordination services between county-level Head Start agencies and other entities that provide services to at-risk young children. Early On: Michigan s Early on program assists parents with young children ages 0 to 3 who have developmental disabilities (Parts B and C of 619 of IDEA). OGS also participates in Michigan s 21st Century Community Learning Centers that provide academic assistance to children who attend lowperforming schools and the MICC which provides guidance to MDE about the development of a statewide, coordinated system for delivery of early intervention services to young children ages 0 to 3. in different departments and organizations. The interviews revealed several areas of strength in which data owners are doing an excellent job initiating collaboration to share and coordinate their data efforts. Common challenges also emerged from the interviews suggesting areas of focus where the potential for progress towards even greater collaboration is most promising. 11

12 Figure 2. The connections between Michigan s early childhood data sets and systems 12

13 These strengths and challenges are discussed below. Strengths The ability to collect longitudinal data and link data from data sets like the Michigan Student Data System to data collected through other systems. Individual Head Start agencies are just beginning to provide data to CEPI about the young children they serve. The ability to link Head Start data from children s early years with later achievement data is a prime opportunity to track how interventions can improve later outcomes. Sharing eligibility and licensing information to reduce duplication when providing services for families. There are a few instances where data is being shared across systems in regular, automatic exchanges. SNAP data from DCH is used by CEPI to determine eligibility for free and reduced lunch. Licensing data is transferred nightly between the between the greatstartconnect.org vii and BCAL systems. There are several benefits to these types of automatic transfers: they reduce the potential for human error to impede the connection of data and they simplify data confidentiality and security procedures because the exchange of data is conducted in a regimented way. The efforts within departments to build data warehouses to consolidate databases and facilitate increased data sharing. DTMB provides support to DHS and DCH by planning and managing their data warehouses. One of the benefits of storing data in warehouses is that it is governed by a shared set of rules. Additionally, there are often standardized procedures for establishing data sharing agreements, as is the case for the DHS data warehouse, which helps to facilitate collaboration. DTMB is currently is working on establishing the Master Person Index system for assigning a unique ID for every person on which they have data. DCH will pilot the MPI procedures which will then be adopted by DHS. Once all of the data sets in both the DHS and DCH data warehouses are using the MPI, the process for matching individual records across data sets will become easier. Challenges Use of multiple methods for assigning Unique IDs prevents linkages across programs. Most data sets in Michigan are using their own system for assigning and tracking ID numbers. Children that appear in multiple datasets often have several ID numbers without a systematic way of connecting them. The use of multiple IDs is one of the major impediments to merging data sets across departments and organizations. One of the exceptions to this is the data warehouses managed by DTMB; many of the datasets managed by DTMB share IDs and can be linked fairly easily. Lack of formal data sharing agreements across departments and the need for bidirectional data sharing. Only one group, the Department of Human Services, uses a formal data sharing process to facilitate the exchange of data within and outside of the Department. Other data sharing 13

14 agreements, like those between DHS Licensing and greatstartconnect.org, were created on an ad hoc basis and were not executed as part of a standardized procedure for either organization. Need for a planning body to provide direction and guidance and set policies that can address integration of data system across multiple agencies and departments. In Michigan there is no shared, central group that sets the policies and procedures related to data management. Governance procedures that apply to multiple data sets and systems are occurring to the greatest degree in the DTMB-managed data warehouses. DTMB is also home to the Office of Shared Solutions, which provides guidance on important governance related topics such as business intelligence rules and has played a role in facilitating data sharing in the past. The reach of DTMB and OSS is limited though and their policies and procedures do not cover the majority of the early childhood data systems in the state. One of the most important themes to emerge from the interviews was the shared sense of openness to greater data collaboration. Interviewees expressed genuine interest and enthusiasm for more data sharing and generally recognized that progress towards greater collaboration would be beneficial to both programs and the children they serve. Since Michigan has achieved national recognition for its forwardthinking approach to data integration it was not surprising that interviewees expressed a high level of positivity about increased data coordination viii. Michigan s early childhood data community seems primed to act on the successes it has already achieved in the area of data sharing by pushing for an even greater level of data coordination that would deepen the understanding of the impact of interventions on later child outcomes. Michigan s progress on the ECE 10 Fundamentals As described previously in this report, the 10 Fundamentals are an important framework for tracking states progress towards attaining their goals related to early childhood coordinated data systems. Table 1 shows Michigan s progress towards meeting the 10 Fundamentals as outlined by the ECDC. 14

15 Table 1. Michigan s progress towards meeting the ECDC s 10 Fundamentals ix 10 Fundamentals Not yet attained In progress Attained 1. Unique statewide child identifier X 2. Child demographic and program participation X data 3. Child-level development data X 4. Ability to link child-level data with K-12 and other key data systems 5. Unique program site identifier with the ability to link with children and the ECE workforce 6. Program site data on the structure, quality and work environment 7. Unique ECE workforce identifier with ability to link with program sites and children 8. Individual ECE workforce demographics, education, and professional development information 9. State governance body to manage data collection and use 10. Transparent privacy protection and security practices and policies X X X X X X X Michigan has attained one of the fundamentals, has made progress on eight others, and is in the beginning stages of work on the remaining one fundamental. For most of the In Progress fundamentals, there has been total attainment of some aspects of the fundamental but no attainment of other aspects. For example, for Fundamental #5, all licensed programs receive a licensing ID and this ID can be connected to an ECE workforce ID but only for those practitioners with professional development data in the greatstartconnect.org system. To fully attain this Fundamental, Michigan would need a process whereby all practitioners are assigned an ID in a systematic way and that ID would follow them if they became employed at a new ECE program. Below are examples of how Michigan is either attaining or falling short of the data goal outlined in each Fundamental. Fundamental #1: The Department of Technology, Management, and Budget is currently developing a unique identifier, the Master Person Index (MPI), that would be used in its DHS and DCH data warehouses. 15

16 There currently is not a definitive plan to proliferate the MPI to other departments within the state and organizations outside of state departments. Fundamental #2: Some demographic and participation data are being collected on children, with children participating in publically-funded education GSRPs having the most developmental and demographic data. There is currently very little demographic information being collected about the families of these children. Fundamental #3: Child development data is collected for children who receive early intervention services through Early On. Development is currently underway on a Kindergarten Entry Assessment that would provide consistent development data about children that will enter the public school system, prior to their first year of schooling. Fundamental #4: Early intervention child development data that is housed at CEPI can be connected to K- 12 education data housed in the Michigan Student Data System as the same ID is used in both data sets. If the kindergarten entry assessment data is housed at CEPI and the MSDS ID system is used, KEA data and K-12 data could be connected fairly easily. Fundamental #5: Licensed programs are identified by a licensing number and data about those programs is stored in the greatstartconnect.org system. This system is in the beginning phase of collecting professional development information in conjunction with the Great Start to Quality QRIS. There is currently no way to connect program and practitioner data from greatstartconnect.org with child-level data being housed in other data systems, like early intervention data housed at CEPI. Fundamental #6: Program data related to management is being collected through the Great Start to Quality QRIS. This is a relatively new system for Michigan that was started in the fall of The first round of ratings was released in December of One of the primary assessments of quality used to determine a program s rating is the Program Quality Assessment (PQA), a High Scope tool that is aligned with Michigan s Early Learning Standards. A verified Self-Assessment is the other main tool that is used to determine a program s rating. Fundamental #7: Workforce participants receive an identifier either through the CEPI Registry of Personnel if they are employed at a GSRP or through the greatstartconnect.org system is they are employed in a licensed child care center or licensed family child care. Practitioner s workforce data can be connected to programs but cannot be connected to child-level data unless both the practitioner and the child are participating in a GSRP with data housed in a CEPI data set. Fundamental #8: greatstartconnect.org is in the early stages of collecting education and professional development data about the ECE workforce. This data includes information about practitioner education level and the completion of professional development activities but only for the select group of practitioners whose programs choose to go for a rating beyond the one level in the Great Start to Quality QRIS. 16

17 Fundamental #9: There is currently no central governing body overseeing data collection and use in the state of Michigan. Establishing a governance structure is one of the first and most important steps towards creating a more coordinated early childhood system. It provides the framework under which policies and procedures related to data quality and data sharing can be established. Data integration cannot take place until there exists a way for various entities to participate in a coordinated data system that ensures that the data they collect for programmatic purposes is being collectively governed by shared rules. Fundamental #10: There are currently universally agreed upon privacy and security practices being implemented in Michigan though, according to the ECDC x, those practices are not communicated to the public in a regular way. Data governance in Michigan Data governance is the set of business processes, policies, and data management practices that provide structure to a single data set or compilation of multiple, related sets. Governance is an integral piece to any complex data system because it provides a way for data to be used in a systematic way that ensures data quality and confidentiality. Since governance is the skeleton that holds up an entire system, it is important that it be in place prior to the development of a specific system s data architecture. Before an integrated data system can be implemented, a governance structure must be in place to oversee the activities of the people responsible for providing the oversight, management, and policies related to the system. In Michigan, there is currently little to no data governance structure in the area of early childhood. Different organizations are maintaining their own data sets and any data sharing that occurs is at the behest of individuals within the organizations. Data is siloed and there is no formal system that facilitates data sharing between groups. There are some data governance activities occurring on a smaller scale for the data that are managed by DTMB for the DHS and DCH data warehouses. Data from the individual sets within these two warehouses can be connected much more easily than it can with data outside of the warehouses. The warehouses typically use the same set of unique identifiers which facilitates these connections. Outside of the data warehouses, connections between data sets and systems are limited and have occurred when individuals within the organizations have identified a need for data sharing, created a data sharing agreement, and put in place the technical architecture to allow for data sharing on a regular basis. Sharing across organizations is difficult and is inhibited by differences in rules and policies that govern departments. Often, it is unclear whose rules should be followed. Other states have approached the issue of data governance in a variety of ways, coming to different conclusions about the best way to promote data sharing. In the next section, State Profiles, we describe in detail the early childhood governance structures that some other states have implemented. The 17

18 experiences of these states offer a guide to the many different ways to approach governance. These different solutions tend to vary along a spectrum of low centralization (coordination and integration) to high centralization. Table 2 describes the benefits and drawbacks to data system integration at high, medium, and low levels of coordination and integration. Table 2. Benefits and drawbacks related to the degree of centralization in early childhood data systems Degree of Example state Benefits Drawbacks centralization High Pennsylvania Most efficient way High upfront costs to coordinate and share data Low level of input from individual Lower maintenance costs once departments and organizations established Medium Mississippi Individual data set control mostly remains with the group that is Data coordination might be limited by the use of different UICs collecting and using the data Moderately higher maintenance costs Lower start up costs once system is established Low Illinois Low initial costs Fewest Individual data set control remains opportunities for data coordination completely with the group collecting and using the data Least likely to share UICs across organizations Basic governance structure to provide standardized policies and procedures. High maintenance costs States like Pennsylvania with data systems with a high degree of centralization benefit from data operations that are streamlined and consistent across organizations. Data from multiple organizations are collected at the organizational level but then sent to a central group that is responsible for managing the 18

19 data and creating new data sets that span departments. One of the main benefits to a system with high centralization is that different departmental policies do not need to be contended with when data needs to be shared. One of the drawbacks of such a system is that initial start-up costs are high. It takes a substantial investment of time and manpower to move from a siloed system to one with a high degree of centralization. Individual departments with current control over their own data are often reluctant to relinquish control over the information they need to properly provide services to their clients. This is understandable given that the people directly using the data have important insights into how it should be managed and used and what data sharing opportunities are most beneficial. With Michigan s current system of siloed data sets, sharing is done on an ad hoc basis. Currently, the two options with the most potential for success are those with low to medium levels of centralization and integration. An early childhood data system with low centralization, like what Illinois is in the process of implementing, is the obvious next step in improving on the current system. The benefits of a system with a low degree of centralization are that it establishes a basic framework for governance, involves a relatively low initial investment, and allows for day-to-day control of individual data sets to remain at the departmental level. Figure 3 illustrates one way that a system with low centralization could be structured in the state of Michigan. In this system, the policies and procedures related to early childhood data are guided by an executive-level oversight and advisory group made up of representatives, such as directors and commissioners, from the state departments (like: OGS, MDE, CEPI, DHS, and DCH) and independent organizations (like: ECIC, Head Start agencies) managing early childhood data sets and systems. This group also includes staff from the DTMB and Governor s Office. The advisory group sets the priorities and direction of the early childhood system while remaining one step removed from the actual work of the data system governance services group. Under the advisory group s guidance, the data system governance services group oversees the day-to-day operations of the coordinated data system. This group of individuals develops the forms and processes that departments use to request data-sharing agreements. They also develop standards related to data privacy and confidentiality as well as establishing procedures to ensure data quality across different departments. Figure 3 describes a coordinated data system rather than an integrated one in that the data governance services group does not actually work with the data from each of the early childhood organizations it serves. It is a governance structure alone and not a data warehousing service. This model is similar to what Illinois is implementing as the first step towards a system that is a data warehousing service. 19

20 Figure 3. Example data governance structure, coordinated data system 20

21 Figure 4 builds on the model described in Figure 3 by detailing the provision of additional data services like data set creation and management. In this expanded model, departments with early childhood data send their data to the data governance services group, who then cleans and merges it with other data sets to form a new data set. This data is then sent back to the individual departments for program improvement and research purposes. This expanded model does not create a centralized data warehouse akin to what is in place in a state like Pennsylvania with a highly centralized, integrated data system. Instead, ownership of the data remains with the departments that collect and use it. The data governance services group acts as a facilitator to apply standard processes and rules to data that is shared across groups and to ease the burden of matching and cleaning data that would otherwise be the responsibility of the individual data owners. Both governance models propose a collaborative approach to the decision-making. In these collaborative models, representatives from state departments like the Office of Great Start and the Department of Human Services are a part of an advisory group, along with representatives from groups from external groups like the ECIC, that determines the overall focus of the data system and aligns the efforts of the Governance Services group with the state s policy priorities. In both of the data governance examples described above, the data governance services group is housed within DTMB. DTMB is one of the best candidates for being the home of data governance because of their experience with data governance procedures and data management. Although their governance responsibilities have not expanded beyond the data warehouses they manage, they have the most experience in establishing data quality and security procedures of any group within the state. They also benefit from being the home of the Office of Shared Solutions which is a group within the state with a history of investment in facilitating data sharing and improving on business intelligence processes related to data governance. 21

22 Figure 4. Example data governance structure, coordinated and integrated system 22

23 STATE PROFILES In this section, we profile other states (Illinois, Mississippi, and Pennsylvania) that have gone through the process of developing an early childhood data governance structure as a way to highlight the various possible paths that Michigan might take to establishing its own system. Different states target different goals with their early childhood data systems. Some of the most common goals are to: Strengthen early childhood systems: the interventions provided and the outcomes of children; Answer key policy and research questions; Improve data coordination, sharing and use; Link data across systems; Reduce redundancy and improve the quality of data; and, Identify data gaps. These profiles include information about the departments participating in the governance structure and the hierarchy of decision-making. Illinois Data Governance Profile Illinois s framework for developing a unified early childhood data system involved the development of intergovernmental agreements with seven state agencies to implement a federated data model assessing state investments and outcomes from birth to career. These agencies include: Illinois Community College Board (ICCB) Illinois Board of Higher Education (IBHE) Illinois School Board of Education (ISBE) Illinois Student Assistance Commission (ISAC) Illinois Department of Employment Security (IDES) Illinois Department of Human Services (IDHS) Department of Commerce and Economic Security (DCES) The agreements consist of standard data access, use, and security terms to guide data collection, sharing, and analysis. The governing board is comprised of high level decision-makers from each agency. A five committee structure is utilized to tackle on-going technical, legal, and policy related issues (Figure 5). The board and committees receive input from the P-20 and Early Learning councils which serve as advisory bodies for their respective programs data systems. The Governor s Office of Early Childhood Development (OECD) represents the early childhood field as part of this structure as well as guides the concurrent development of a unified early childhood data system. 23

24 Figure 5. Illinois s early childhood data governance structure xi The state s early childhood data governance structure consists of three interagency teams developed to organize their cross-agency work. Similar to the state s overall data governance structure, leadership teams with high-level decision makers from Illinois Board of Education, Department of Human Services, Department of Child and Family Services, and OECD will be responsible for guiding larger policy issues which encompass early childhood data systems with the support of working committees (Figure 6). Figure 6. Illinois s early childhood data governance system hierarchy Leadership team (High-level decicionmaking) Interagency team (On-going decisionmaking) Interagency project teams (Working committees) 24

25 Process for developing governance structure Illinois s governance structure was modeled after the existing data oversight and sharing structure in use for its higher education entities. The committee structure, data sharing agreements, and processes used by the public and private colleges provided the basis for mapping out Illinois s birth to career and early childhood data governance structure. The work was supported by State Advisory Council and Longitudinal Data system federal grant funds to plan and develop Illinois s early childhood and P-20 data systems. From the start, the priority of the Illinois Early Learning Council (ELC) was to achieve data integration across departments serving young children and to answer key policy questions identified by the field. Illinois s recent award of the Phase 2 Early Learning Challenge Grant (ELC) xii funds will be instrumental in the implementation of proposed data architecture plans to facilitate data linkages and sharing. The ELC funds will also support staffing for three Data and Outcomes Managers in the Offices of Early Childhood Development, State Board of Education, and the Department of Human Services to address data sharing and integration needs for each department. Having a dedicated staff person to address these issues is an important part of being able to ensure cross-department coordination work. Lessons Learned and Next Steps Illinois s early childhood data governance structure is still in the process of being finalized; a process that is being guided by the Governor s Office of Early Childhood Development (OECD). The success of its higher education system s data sharing model has provided an encouraging framework to build from as the state moves forward. Illinois has also benefited from continued support from key stakeholders; however, the state still faces concerns related to long-term funding for improved data systems and coordination. These concerns are two-fold: first, how to integrate current data system, and second, how to address antiquated legacy data systems that may not be able to meet the technological requirements for increased data coordination. There are already efforts to modernize current data systems, however, this will be a process that spans several years. Another of their goals is to develop processes to incorporate new state data systems (ex. health care exchanges) into the unified data system as they develop. Overall, the OECD desires to create a constant feedback loop to key decision-makers and develop dashboards and reporting systems that display data in a meaningful way to inform state public policy and planning efforts related to young children. Mississippi state governance profile LifeTracks, Mississippi s Statewide Longitudinal Data Center (SLDS),includes data from birth through the workforce and is defined by six key building blocks: (1) scope, (2) data stakeholders, (3) application, (4) operational capacity, (5) leadership and accountability, and (6) sustainability (Figure 7). 25

26 Figure 7. Mississippi s State Longitudinal Data System xiii 1) Scope defines the purpose of the SLDS and provides a framework for supporting its use. In Mississippi the scope is to improve education and employment outcomes for Mississippians to improve the quality of life across the state. 2) Data stakeholders are defined as an individual or organization that could affect or be affected by information generated from the SLDS and that align with the scope of the SLDS. In Mississippi, data contributors include but are not limited to: Mississippi Department of Education Mississippi Community College Board Mississippi Community Colleges (14 separate institutions) Mississippi Institutions of Higher Learning Mississippi Department of Employment Security Delta Workforce Investment Area Mississippi Partnership Southcentral Mississippi Works Twin Districts Workforce Area Mississippi Development Authority Mississippi Department of Human Services Mississippi Head Start Association State Early Childhood Advisory Council 26

27 Mississippi Department of Rehabilitation Services Mississippi Department of Corrections Mississippi Department of Health Mississippi Division of Medicaid Chambers of Commerce Mississippi National Guard Veterans Affairs Board 3) The primary application of the Mississippi SLDS is to generate timely, accurate, and policy-compliant information from data collected and stored by different local and state educational and workforce entities for the purpose of conducting activities within the scope. 4) Operational capacity refers to the ability to fulfill the scope of the SLDS while ensuring individual privacy and confidentiality of data. This capacity is established through data and technical expertise, legal and compliance expertise, formal agreements, policies and procedures for the data life cycle, research expertise, and the creation of a Center of Excellence to carry out all activities that define the operational capacity of the SLDS. 5) In Mississippi leadership and accountability for the system rest with the SLDS Governing Board (Figure 8). The SLDS Governing Board does not assume ownership of any data provided by data stakeholders. Data ownership remains with the data contributor. The SLDS Governing Board is the steward of the SLDS itself and is responsible for establishing and maintaining the necessary partnerships and managing communications with all parties about the importance of supporting and benefitting from information available through the SLDS. 6) Sustainability is established through the legal authority outlined in Executive Order 1015 signed in Sustainability also includes activities to maintain ongoing operational capacity over time, continuous system innovation, ongoing training, and availability of resources. Data Governance Board The SLDS central data governance board is composed of a representative from each agency or entity providing data to the system. Their responsibilities include: Maintaining active communication with all data stakeholders; Assuming responsibility for developing formal processes for reviewing and approving research requests and non-standard or ad hoc reporting requests; Determining conflict resolution procedures and practices regarding any aspects, from data transfers to dissemination of information, of the SLDS; 27

28 Providing leadership to keep data contributors engaged with the system; Overseeing the data clearinghouse and the system host; and Assuming responsibility to contract with a third parties to manage and maintain the system and to ensure that governing policies and procedures are enforced. Pennsylvania state governance profile Pennsylvania s Enterprise to Link Information for Children Across Networks (PELICAN) spans Pre- Kindergarten, Early Intervention, Child Care Subsidy, and Quality Improvement data systems. PELICAN was developed as a data partnership between the Pennsylvania Department of Public Welfare (DPW) and Office of Child Development and Early Learning (OCDEL). The governance of this system requires oversight and management of the PELICAN and Home and Community Services Information System (HCSIS) data systems to ensure on-going quality and improvement. The Early Learning Network is responsible for collecting information about children, teachers and programs to answer key policy questions about Pennsylvania s early childhood initiatives and services to support program evaluation and improvement. However, the entry, collection, analysis, and development of these data systems documenting information about children, families, and programs is governed through a system of steering committees, project teams, change control boards, and program specific sub-committees that create a unified statewide system. Figure 9. Pennsylvania s Early Learning Network xiv The PELICAN governance structure consists of consists of 16 regularly scheduled meetings to provide executive decision making; strategic planning, technical expertise; and stakeholder engagement. Below is a summary of the four meeting types and objectives: Steering Committees: Provide executive decision making, cross agency/department coordination, and strategic planning related to the PELICAN and HCSIS systems. Decisions are informed by the Project and Change Control Board Team meetings 28