Enterprise Data & Analytics

State data are items of information that are collected, maintained, and utilized by state departments and agencies for the purpose of carrying out State of Iowa business. State data are essential data required to conduct operations, and would include any data elements that are created, received, maintained, or transmitted.

State data assets are data that have been transformed into information that contains operational characteristics or ambient conditions for the purpose of communicating results, monitoring issues or problems, controlling processes and managing performance, improving operational effectiveness and efficiency, and/or facilitating actionable insights. Data assets would include datasets, as well as charts, maps, measures, stories and dashboard derived from such datasets.

State agencies should publish data to:

• Improve the public’s understanding of the cost and purpose of government services.
• Improves governmental accountability and public participation.
• Leverages data held by different agencies by connecting datasets and finding new insights.
• Eliminates redundancies by allowing the access of data in one place.
• Improves decision making by better informing people with data.
• Creates more efficient and proactive process for open records requests.
• Encourages innovative ideas (e.g., web applications) that enhance the lives of our citizens.
• Increases economic activity by generating new and rich content through new applications and services.
• Comply with the requirements of the Taxpayer Transparency Act and Accountable Government Act.

State data is considered high impact where it:

• Tracks strategic initiatives.
• Tells the agency's story.
• Is frequently requested Is in strong demand.
• Is of timely interest.
• Costs high dollars to collect.
• Provides an economic opportunitiy.
• Facilitates reporting.
• Encourages cross-agency collaboration.

Data is difficult to publish where:

• Data is unstructured contained only in documents or on paper.
• Data contains a lot of missing or incomplete values.
• Data is not precisely defined and can be easily misinterpreted.
• Data is inconsistent and recorded values can mean different things.
• Data contains contradicting values leaving uncertainty to which information is correct.
• Measured data lacks standard reference method or measurement protocol.
• Data contains confidential or sensitive information.
• Agencies lack procedures for exporting data, and assuring its quality.