Chapter 3 Data Governance and Stewardship (321)

Chapter definition of DG

The enterprise authority that ensures control and accountability for enterprise data and establishes decision rights, data policies, and data standards Implements and monitors these through a formal stucture of assigned roles, responsibilities and accountabilities

Analysis

The process of translating data into meaningful information

Collection

The processes by which data elements are accumulated

Application

The purpose for the data collection

Information

Understanding relationships or context

Value

Utility or business purpose of specific informaiton

Consistency

Value of data should be reliable and the same across applications

Accurate Data represent what

What was intended or defined by the official source, objective or unbiased data, complance worh known standards

Information governance is a what?

asset

Difference between Data Governance and Information Governance

correlation between the two but also differences

Healthcare revolves around what?

data and information

Data governence

emerged in the 1990s to address data quality in data warehouses

Data stakeholders

executive and senior management, line of business units, service units, and information technology groups

Governance is an enterprise activity that

exercises authority across the entire org and includes an enforcement component

Data Governence

includes policies to ensurer data are consisently defined, structured, accurate, and current across the enterprise

The continuos monitoring of their proper implementation is put in place to

manage organization assets and enhance the propserity and viability of the organization

Data Life Cycle Cycle Management ****

manages data fron beginning to end

Data Goveernance

overarching authority ensuring cohesive operation and intergration of the data management domains

Data Governance

policy related to data content, structure, reliability, validity, interopearibility

policies,requires, standards, accoutabilities, controls and data definitions

rules

Metadata Management

structured information that describes, explains, locates, or helps retrieve, use or manage an information resource

Data stewards****

subject matter stewards, data definition stewards, production stewards, data usage stewards

To ensure relevance may use:

1. A pilot of the data collection instrument 2. A parallel test completing a new instrument and the current one simultaneously

10 characteristics of data quality

1. Accuracy 2. Accessibility 3. Comprehensive 4. Consistency 5.currency 6.definiton 7. Granularity 8.Precision 9.Relevance 10.Timeliness

Core concepts across most definitions of DG

1. Consists of interprise-level authority and accountability for effective data asset management 2. Establishes and monitors data policies, standards, practices, decision rights, and accountabilities for managing, using, improving, and protecting organizational information corporate data 3.Executes policies, standards, and practices through formal structures, roles, and responsibilties

Consistency should be facilitated through the use of

1. Data definitions 2. Extensive training 3. Standardized data collection 4.Inteegeated orninterfaces systems

Terminology anf Classfication Management

1. Ensuring appropriate adoption, maintenace, dissenmination, and accessibility of vocabularies, terminologies, classification systems, and code sets for semantic interoperability and data integrity 2.Developing algorithmic translations, concept representations, and mapping among clinical nomenclatures 3.Providing oversight for clinical and diagnostic coding to ensure compliance with established standards

Integrity May be threatened by

1. Hardware problems 2. Power outages 3. Disk crashes 4. Application software 5. Viruses

Data Quality Management

1. Identifying data quality requirements and establishing data quality metrics 2.Identifying and carrying out data quality projects 3.Profilling data and measuring conformance to established quality metrics and business rules 4.Identifying data quality problems and assessing their root cause 5.Managing data quality issues 6.Implementing data quality and improvement measures 7.providing training for ensuring data quality

Master Data Management

1. Identifying reference data sources (databases, files) 2. Maintaining authoritative value lists and metadata 3. Establishing and organization data sets 4. Defining and maintaining match rules 5. Reconciing system of record

3 Overarching segments in DG Institute Framework

1. Rules and rules of engagement 2.People and organizational bodies 3.Processes

Data Governance

1.Advocating for the data asset 2.Establishing data strategy 3.Establishing data policies 4.Approving data procedures and standards 5.Communicating, monitoring, and enforcing data policy and standards 6.Ensuring regulatory compliance 7.Resolving data issues 8.Approving data management projects 9.Coordinating data management organization

Information intelligence and Big Data

1.Assessing current intelligence, needs resources and use 2.Determining scope, requirements, and architecture for enterprise intelligence 3.Developing and implementing policies and procedures for enterprise information inteligence

Implement framework through a formal structure

1.Assigned roles 2. Responsibilities 3. Accountabilities

Data definition

1.Clear definitons assure that current and future data users know what they mean 2.Each data element should have clear meaning and acceptable values 3. Clear, consise data defintions facilitate accurate data collection 4. Acceptable values should be defined 5. Instrument of collections should include the data definitons and ensure that data integrity characteristics are managed

Rules and Rules of Engagement in DG Institute Framework

1.Creating a mission statement 2.Developing DG goals 3. Establishing DG metrics 4.Determining funding strategies 5.Developing data rules and definition 6.Assigning decision rights 7.Assigning accountabilities 8.Establishing controls

Data Security Management

1.Data security planning and organization 2.Development, implementation, and enforcement of data security policies and procedures 3.Risk management 4.Business continuity 5.Audit trails

People and Organizational Bodies in DG Institute Framework

1.Data stakeholders 2.Data governance council 3.Data stewards 4.Data steward program committee 5.Data governane office

Reasons for DG in Healthcare

1.Ensure data intergrity to support and improve all org clinical and business units 2.Coordinate and improve access to and protect sensitive data 3.Facilitate data intergration and reduce data silos 4.Estabish and maintain a consistent set of business and clinical defintions, clinical, terminology, and metadata 5.Facilitate seamless exchange of patient data for patient care and health info exchange 6.Adhere to compliance and reporting regulations

Data Governance Processes in DG Institute Framework

1.Establishing ules and processes for governing data 2.Establishing rules and process for coordinating the DG program

Metadata Management

1.Manage data dictionaries 2.Establish enterprise metadata strategy 3.Develop policies and procedures or metadata identification, management, and use 4.Establish standards for metadata schemas 5.Monitor policy implementation 6.Establish and implement metadata metrics

Data Architecture Management establishes:

1.Standards, policies, procedures for data collection, storage, and intergration 2. Standards fo IS design 3. Identification and documention of requirements 4.Development and maintencance of data models

Making the Business Case for DG

1.The desired outcome or benefit of implementing or changing a process or activity anabling stakeholder interations 2.DG impact goes beyond data quality 3.Linking DG to improved business processes is the business cae

Data Life Cycle Management establishes:

1.What data are collected 2.Standards for data capture 3.Standards for data storage and rentention 4.Processes for data access and distribution 5.Standards for data archival and disposal

Governance uses a framework

1.policies 2.standards 3.rules 4.decision rights

Quality

A degree of excellene of a thing and/or a requires character or property that belongs to a thing's essential nature

Precision

Acceptable values or value ranges for each data item must be defined

Comprehensiveness

All required data items are included, entire scope of data should be collected and intentional limitations should be documented

Ensuring accuracy involves what?

Appropriate education and training and timely and appropriate communication of data definitions to those who collect data

Granularity

Atrributes and value of data should be defined at the correct level of detail

Currency

Changes should be documented so current and future users know the meaning of the data

Integrity

Data are true to the aource and have not been alteres or deatroyed in an unauthorized manner or by unauthorized users

Currency

Data defenitions chabce or are modified over time

Accessibility

Data items should be easily obtainvale and legal to collect

Currency

Data shoud be up-to-date

Relevance

Data that are meaningful to the performance of the process or application for which they are collected

Precision

Data values should be just large enough to support the application or process

Timeliness

Determined by how the data are being used and their context

Data Governance Institute Framework

Developed by Data Governace Institute and consists os three overarching segments

Content and Record Management

Developing and adopting taxonomic systems

Content and Record Management

Developing and maintaining an information architecture and metadata schema that identify links and relationships among documents and defines the content within a document

Content and Record Management

Develping and implementing policies and procedures for the organization and catergorizing unstructures data (content) in electronic, paper, image, and audio files for its delivery, use, rse, and preservation

Information Governance

Emerged in 2004 as a framework for information privacy and security

Goal of Information Management Plan

Enable enterprise data managment

Data Quality Management

Ensure data are meeting quality characteristics

Governance

Establishment of policies, and continuous monitoring of their proper implementation, by the members of the governing body of an organization.

Data Governance

Focuses on the input: data

Information Governance

Focuses on the output: informtion

Organizational Strategic Plan objective:

IM infrastructure that advances quality patient care, effecient operations,competitive advantage and decision making, and ensures compliance with data regulations and manadates

Information Governance

Includes policies for records and information management for confidentaility, regulartoy compliance, retention, disposal, and ethical use

Data Governence

Includes structured and instructured data

Data are identically maintained during any operation

Integrity

Security principle that protects information from being modified or corrupted

Integrity

Data Architecture Management

Intergrated specification artifacts

Wisdom

Knwoledge insight and action. Understanding principles

Knowledge

Learning, interpretation,pattern, recognition. Informetion meaning

Duty

Legal obligation for specific information

Granularity

Level of detail=

Information Intelligence and Big Data

Management of applications and technologies for gathering, storing, analyzing and providing data for decisions

Master Data Management

Management of key business entity data

Content and Record Management

Management of unstructured data

DG Plan Relationship to Organizational Planning

Organization Strategic Plan > Information Management Plan >Data Governance Program Plan

Information Governance

Policy related to information, use, protection, compliance

Warehousing

Processes and systems used to archive data

Data Security management

Protection measures and safeguards for data

Data

Raw Facts

The way that stakholders (policy makers, data woners , data stewards, and so on) interact with each other

Rules and Rules of Engagement

Asset

Specific container of information

Qualities of Data refers to what

The caharacteristics or attributes that make up data or factual information