Web Content Viewer
Actions
Vision
Vision
Ohio Data Analytics is a collaborative initiative that provides agencies access to the expertise, technical support, and technology to eliminate silos and uncover data insights.

Ohio Data Analytics supports agencies in sharing data that has never been shared before and providing a support structure focused on bringing data analytics expertise to the state of Ohio.

Ohio Data Analytics offers agencies a support structure that allows agencies to take a proactive approach to understand their data and gain additional insights when shared across agencies. 

Guiding Principles of Ohio Data Analytics

Ohio Revised Code 125.32 was created to address silos that may exist inside the State, ultimately aimed at removing barriers and connecting state agencies, services, data, and most importantly, Ohio citizens. Taking direction from Ohio Revised Code 125.32, the Guiding Principles of Ohio Data Analytics focus on removing government silos, ensuring the right expertise is available for Ohio’s agencies, boards, and commissions, and keeping pace with analytical needs and emerging technologies.

Data is a Strategic Resource

Agencies treat data as a strategic resource and manage data to support the expanded use for analytical and research purposes. This principle aims to maximize analytical value and use of State data sets beyond traditional reporting and operational use cases.

Aligns with  ORC 125.32.A

Data is a Shared Asset

Data is a shared asset that has value to the State and is managed accordingly. Users have access to the data necessary to perform their duties; therefore, data is shared across State functions and agencies unless restricted by law. This principle emphasizes that data is a valuable State resource; it has real, measurable value. In simple terms, the purpose of data is to aid decision-making. Accurate, timely data is critical to accurate, timely decisions. Because data is the foundation of our decision-making, we must carefully manage it to ensure that we know where it is, can rely upon its accuracy, and can obtain it when and where we need it.

Aligns with ORC 125.32.B

Data is for Analytical & Research Purposes

Data sources within the Data Lake are used for analytical and research purposes only. This principle requires that where Data Lake is approved and implemented for analytical purposes, it is not then used for regulatory reporting purposes, compliance monitoring, or operational service delivery (e.g. calculation of benefit payments).

Aligns with ORC 125.32.A

The State will Preserve Confidentiality & Privacy

Policies and procedures used to manage the Data Lake must minimize any potential impact on privacy and confidentiality, and adhere to all governing laws and regulations. Open sharing of information and the release of information via relevant legislation must be balanced against the need to restrict the availability of classified and sensitive information. Existing laws and regulations require the safeguarding and privacy of data, while permitting free and open access.

Aligns with ORC 125.32.C.2 & ORC 125.32.D

Data is Governed by Accountable Entities

a) Data Owner Stewardship Accountability: 

Agencies maintain stewardship responsibly for business controls and data content related to data assets in the Data Lake and are fully accountable for granting security access to their data. This principle ensures that agencies recognize continued accountability for their data within the integrated Data Lake and establish adequate controls over the use of personal or other sensitive data in analytics projects.

Aligns with ORC 125.32.C.1

b) Data Administration Custodial Accountability: 

DAS maintains the custodial responsibilities and is accountable to administer all technical aspects of the Data Lake Platform. This principle ensures that DAS, as that data custodian, is accountable for the technical control of data including: administering security, platform scalability, configuration management, availability, consistency, audit trail, backup and restoration, technical standards, poles and business rule implementations.

Aligns with ORC 125.32.D

c) Data Integrators Accountability: 

One or more qualified ‘Analytics Firm(s)’ will be selected for each data and analytics proposal. This principle sets out the responsibilities of integrating authorities to manage a data & analytics project from start to finish in line with the pre-qualified agreements made under the State’s Supplement II and with the State’s Agencies, as part of approval processes.

Data is Reusable

Data sharing protocol should be designed to support reusability. This means sharing data from authoritative sources, integrated once with the highest possible level of granularity, and not in aggregate or modified forms as to support multiple use cases. This principle aims to maximize analytical value and use of State data sets, minimize the time to answer, which allows data analysts to work faster and minimize the integration re-work that is required when data is only sourced for a single purpose.

Aligns with ORC 125.32.D

Data is Interoperable

Data sharing protocol should be designed to support interoperability meaning data should be sourced and managed to support data integration within and across State agencies. This principle aims to support the disciplines of data science, inclusive of the discovery, research and machine learning analytics that can require various data sets and types be integrated for maximum value.

Aligns with ORC 125.32.D