As state agencies across the United States have begun to recognize the need for increased sharing of data, effectively designing an integration solution requires three steps: 1) analyzing data and determining integration opportunities; 2) establishing a data governance board and policies; and 3) implementing technologies that support efficient data sharing.
1. Analyze data and determine integration opportunities
Before states move toward integration, it is important to first define desired outcomes. These outcomes should inform the strategy and the determination of what data should be integrated. Accomplishing this will require analyzing existing systems and identifying data that would be of value in an integrated environment. It is important to recognize that not all data should be integrated.
Integrating data from a state education department with data from state-run hospitals, for example, probably wouldn't deliver sufficient return on investment (ROI) to justify the cost and effort required to bring the data together. In contrast, integrating select data from state-run hospitals with select data from in-home behavioral health providers could deliver significant value, as it would enable staff to provide better follow-up care.
In determining what data merits integration, it is important to consider not only potential ROI, but also public perceptions and state and federal regulations.
2. Establish a data governance board and appoint data stewards
The most effective way to establish and enforce data-sharing policies and regulations is through data governance and data stewardship.
Data governance involves setting standards, policies and procedures that apply to collecting, integrating and sharing data. Policies should stipulate what data can and cannot be integrated; who can access the integrated data; how the integrated data can be used; how permissions will be granted; how data will be stored; and how metadata (or information about data) will be created and managed. Data governance also sets standards for data quality.
3. Implement technologies in support of data-sharing initiatives
With data governance and data stewardship established, the next step in an interagency data-integration project involves selecting and implementing technologies to support the initiative. These will include a master data management solution as well as an integrated data platform with analytical and reporting capabilities.
Typically, the master data management solution will be an extract, transform and load (ETL) solution that will de-duplicate person data and establish a master record for each unique person. If different agencies collect data regarding the same person but have captured some details differently, the ETL solution will determine whether these differences suggest that the data refers to two different people or to one person to whom different designators, or values, have been applied. Along with this, a single value will be selected for each data field in the individual's master record. This will enforce reporting consistency going forward. De-duplication and data standardization are essential to the effective analysis and reporting of data.
To bring together all available information about constituents whose data appears in multiple systems, an integrated data platform is needed. This usually takes the form of a data warehouse.
Once the integrated data platform has been designed, built and tested (to ensure that the information it contains is usable and actionable), the next step is to create automated reports and provide tools for self-service reporting and analytics.
These capabilities will enable users to inform decision-makers through real-time access to information in the form of reports, dashboards and analytics, leading to data-driven policymaking and decision-making.
For more information about data sharing across state agencies, download my recent white paper: Data Sharing Across State Agencies: Improving constituent services, enhancing policymaking and reducing costs.
About the Author
Gabriella LivelyGabriella Lively is an IT professional with expertise in state government and the healthcare industry. She has experience in data warehousing, data engineering, data analytics, and business intelligence. Ms. Lively has helped a variety of clients utilize their existing data more efficiently to drive business planning and make more informed decisions through the integration of disparate systems and the implementation of analytic solutions to report, monitor, and predict outcomes.