What's really needed for data-driven governmentWhile poor data quality, the challenges of pulling and integrating data from legacy IT systems and the difficulty of persuading stakeholders to embrace open data can all be stumbling blocks to a data-driven government, there are proven ways local governments can improve data governance.The Local Government Association and Nesta, both U.K.-based, studied the ways local governments were using data, describing challenges and projecting success factors. The entities worked together during 2016 to review and analyze data practices of local councils to see how local authorities could better use their data.In their case studies, they found several common issues facing data projects in local governments and described ways to combat these issues. Some of these common issues [are documented as goals in this StratML rendition]Government Computer NewsGCN_adf688ca-c951-11e6-b00c-459cb8ca98f4Kathleen HickeyReporterLocal Government AssociationNesta_adf689f6-c951-11e6-b00c-459cb8ca98f4To help local governments improve data governance._adf68aa0-c951-11e6-b00c-459cb8ca98f4Data Standard, Quality & IntegrationImplement data standards and improve data quality for integration._adf68bc2-c951-11e6-b00c-459cb8ca98f41Lack of data standards (or poor data quality) hindering data integration -- In every case study, some or all of the data was unusable. In some instances this was because of data entry errors, mainly because the staff members inputting the data were not the primary data users and did not understand the importance of accurate data. In other cases, data was collected in ways that made it hard to integrate with other information. Data was not routinely checked for quality."The case studies confirmed that poor data quality is likely to be a feature of most, if not all, local government data projects," the researchers said.Furthermore, data in legacy systems of one agency did not always follow the same data standards as at another agency, making integration difficult. In most cases, researchers said, the problem was due to "how values were represented, data categories and fields, or how the data is organized." In some cases it was because data standards had not been adopted systematically.Solution: Researchers recommended finding where problems with data quality originate and putting the onus on the data owners to improve the data quality. Dashboards also can help expose where poor data quality is linked to data input methods. As data quality and access increases, the number of people needed for data extraction and reporting is reduced.Problem IdentificationFind where problems with data quality originate._adf68c58-c951-11e6-b00c-459cb8ca98f41.1ResponsibilitiesPut the onus on data owners to improve the data quality._adf68cee-c951-11e6-b00c-459cb8ca98f41.2Data OwnersDashboardsUse dashboards to expose where poor data quality is linked to data input methods._adf68d8e-c951-11e6-b00c-459cb8ca98f41.3Data Extraction & ReportingIncrease data quality and access in order to reduce the number of people needed for data extraction and reporting._adf68e42-c951-11e6-b00c-459cb8ca98f41.4Information Governance & Data SharingImprove information governance and data sharing._adf68ed8-c951-11e6-b00c-459cb8ca98f42Poor information governance and data sharing -- Legal and cultural hurdles regarding information governance are unavoidable, researchers said. These issues should not be seen as an insurmountable barrier, though. In every project studied, team members found ways to legally share information in accordance with current legislation.Solution: Researchers recommended embedding decisions about sharing data into new systems. Additional best practices include developing information governance protocols for responsible data sharing based on specific-use cases and creating a team manager to drive data integration/use projects. These data-sharing decisions should be based on a balanced risk assessment that “weighs privacy concerns against the risk to the organization or individual of not sharing,” they said. Data should not be collected where there is no apparent, immediate use for it. Instead, all data projects should start with a problem that can be solved by data, then determine which data and analysis might help solve it. The system should record who is using the data to better track if the data is being used inappropriately. There should also be a data inventory that is published as metadata.New SystemsEmbed decisions about sharing data into new systems._adf68f82-c951-11e6-b00c-459cb8ca98f42.1Information Governance ProtocolsDevelop information governance protocols for responsible data sharing based on specific-use cases._adf69022-c951-11e6-b00c-459cb8ca98f42.2Data Integration/Use ManagersCreate a team manager to drive data integration/use projects._adf690cc-c951-11e6-b00c-459cb8ca98f42.3Data Integration/Use ManagersRisk AssessmentBase data-sharing decisions on a balanced risk assessment that weighs privacy concerns against the risk to the organization or individual of not sharing._adf69176-c951-11e6-b00c-459cb8ca98f42.4CollectionRefrain from collecting data where there is no apparent, immediate use for it._adf69220-c951-11e6-b00c-459cb8ca98f42.5ProjectsStart data projects with a problem that can be solved by data._adf692ca-c951-11e6-b00c-459cb8ca98f42.6SolutionsDetermine which data and analysis might help solve problems._adf693b0-c951-11e6-b00c-459cb8ca98f42.6.1UsageRecord who is using the data to better track if the data is being used inappropriately._adf69586-c951-11e6-b00c-459cb8ca98f42.6.2Data InventoryProvide a data inventory published as metadata._adf69644-c951-11e6-b00c-459cb8ca98f42.6.3Legacy IT SystemsImprove interaction with legacy IT systems._adf69702-c951-11e6-b00c-459cb8ca98f43Difficulty working with legacy IT systems -- Pulling data from legacy IT systems is difficult, particularly when there is poor quality architecture and systems. Data from legacy systems may be in the same format as that in other systems, creating additional integration issues. Solution: Researchers recommended software to "broker between multiple legacy systems" that can integrate data from several sources into a single data repository that can reduce duplicative efforts, facilitate processes and increase data accuracy. Organizations should also devote resources to work with legacy systems and create a data dashboard to more easily understand issues, improve prioritization, decrease decision-making bottlenecks and expose data quality issues.Data Brokerage SoftwareUse software to broker data among legacy systems to integrate data into a single data repository to reduce duplicative efforts, facilitate processes and increase data accuracy._adf697b6-c951-11e6-b00c-459cb8ca98f43.1Data DashboardWork with legacy systems and create a data dashboard to more easily understand issues, improve prioritization, decrease decision-making bottlenecks and expose data quality issues._adf6986a-c951-11e6-b00c-459cb8ca98f43.2ResistanceBreak down resistance to open data._adf69928-c951-11e6-b00c-459cb8ca98f44Resistance to open data -- Oftentimes teams were nervous about potential negative reactions from revealing the poor quality of their data. Additionally, once data was integrated and open, it was sometimes difficult to get some employees to use the data -- usually for cultural reasons.Solution: Researchers recommended actively engaging potential data users, creating partnerships with interested communities to better solve challenges faced by the local public sector. Publishing full, raw datasets, creating an open-data portal with multiple datasets with a user-friendly interface are also recommended. The data should be machine readable in a standard open format and use an application programming interface wherever possible. Data should be routinely updated, preferably automatically. It should also be analyzed to determine if it is consistent across the enterprise and achieving its purpose of solving the issue for which it was intended. Historic data that is no longer used should be retired.Engagement & PartnershipsEngage potential data users, creating partnerships with interested communities to better solve challenges faced by the local public sector._adf699dc-c951-11e6-b00c-459cb8ca98f44.1PublicationPublish full, raw datasets, creating an open-data portal with multiple datasets with a user-friendly interface are also recommended._adf69a90-c951-11e6-b00c-459cb8ca98f44.2Machine-ReadabilityPublish data in an open standard, machine-readable format._adf69b6c-c951-11e6-b00c-459cb8ca98f44.2.1APIsProvide an application programming interface._adf69c34-c951-11e6-b00c-459cb8ca98f44.2.2UpdatingRoutinely update data, preferably automatically._adf69cf2-c951-11e6-b00c-459cb8ca98f44.2.3Analysis & IntentionsAnalyze data to determine if it is consistent across the enterprise and achieving its purpose of solving the issue for which it was intended._adf69dba-c951-11e6-b00c-459cb8ca98f44.2.4ArchivalRetire historic data that is no longer used._adf69e82-c951-11e6-b00c-459cb8ca98f44.2.52016-12-222016-12-23https://gcn.com/articles/2016/12/22/data-governance-challenges-solutions.aspx?s=gcntech_231216OwenAmburOwen.Ambur@verizon.net