Data Governance Step by Step: Month 2 Week 1

Month 2:

1. Identify key data assets and systems: Which data assets are most critical to your business? Which systems are used to collect, store, and manage these data assets?

2. Data Quality Assessment: Evaluate the quality of your data to identify issues like inaccuracies, duplication, and inconsistency. This will be impactful to get buy-in from the executive committee.

3. Identify Project Management Approach: Do you have a PMO (project management office)? Assess project management process used and specific project 'gates' that gates data activities. Identify gaps.

4. Categorize Business Needs and Key Challenges: Based on the surveys and assessments conducted during the previous month, build out use case priority and impact matrix.

5. Establish a data governance framework: This framework should define the roles and responsibilities of key stakeholders, as well as the policies and procedures that will govern the use and management of data.

6. Define Data Governance Roles & Responsibilities

7. Define the scope and goals of your data governance program: What do you want to achieve with data governance? Do you want to improve data quality, enhance compliance, or optimize data utilization? Once you know your goals, you can tailor your implementation plan accordingly. What problems are you trying to solve, and what benefits do you expect to gain? Set clear objectives by defining the goals and objectives of your data governance program. Note, this is at a draft stage at this point. You'll need to review with the governance steering committee to finalize.

8. Data Governance Charter: Develop a draft data governance charter, stakeholder map and policy outline. Review it by the leadership influencers.

9. Data Catalog: Begin developing a data dictionary/catalog.

10. Identify members for Executive Steering Committee

Week 1

​Identify key data assets and systems: Which data assets are most critical to your business? Which systems are used to collect, store, and manage these data assets? In the previous month, during the data influencer surveys and data architecture assessment, we'll have a good understanding of they key data assets and the key systems. This determination should be based on the business team assessment and preferably not the IT team's assessment. In terms of system, you'll identify core business systems, core operational systems, core corporate level systems (e.g. ERP) that the business would rank them higher than the IT infrastructure, IT middleware etc.

​I'll share examples in different industries to give you an idea of the level Key data assets and systems in a clinical research organization (CRO) include:

• Electronic clinical trial management systems (eCTMS): eCTMS are used to manage all aspects of a clinical trial, from site selection to data collection and analysis. They typically include features such as randomization, case report form (CRF) management, data cleaning and validation, and reporting.

• Clinical data repositories (CDRs): CDRs are used to store and manage clinical trial data. They typically provide features such as data versioning, audit trailing, and security.

• Clinical data warehouses (CDWs): CDWs are used to store and manage clinical trial data in a centralized location. They typically provide features such as data integration, data mining, and data visualization.

• Electronic medical records (EMRs): EMRs are used to store and manage patient medical records. They typically include features such as patient demographics, medical history, medications, laboratory results, and imaging reports.

• Laboratory information systems (LIS): LISs are used to manage laboratory data, such as specimen collection, testing, and results reporting.

• Imaging information systems (PACS): PACSs are used to manage medical images, such as X-rays, CT scans, and MRIs.

• Bioinformatics systems: Bioinformatics systems are used to analyze biological data, such as DNA sequences and gene expression data.

• Regulatory information management (RIM) systems: RIM systems are used to manage regulatory information, such as clinical trial protocols, investigator brochures, and informed consent forms.

In addition to these systems, CROs may also use a variety of other systems, such as project management systems, quality management systems, and financial management systems.

​Key data assets and systems in an Electronic Medical Record (EMR) healthcare IT organization include: Data assets:

• Patient data: This includes information such as patient demographics, medical history, medications, laboratory results, imaging reports, and vital signs.

• Provider data: This includes information such as provider demographics, specialty, and credentialing information.

• Billing and insurance data: This includes information such as patient insurance information, claims data, and billing codes.

• Clinical performance data: This includes information such as quality measures, patient outcomes, and resource utilization.

Systems:

• Electronic health record (EHR) system: An EHR system is a software system that healthcare providers use to store and manage patient medical records.

• Practice management system (PMS): A PMS is a software system that healthcare providers use to manage their practice operations, such as scheduling appointments, billing patients, and managing inventory.

• Telemedicine system: A telemedicine system is a software system that healthcare providers use to provide remote healthcare services to patients.

• Clinical decision support system (CDSS): A CDSS is a software system that provides healthcare providers with clinical decision support, such as drug interaction alerts and clinical guidelines.

• Reporting and analytics system: A reporting and analytics system is a software system that healthcare providers use to generate reports and perform analytics on their data.

In addition to these systems, EMR healthcare IT organizations may also use a variety of other systems, such as revenue cycle management systems, patient portals, and interoperability solutions.

​Key data assets and systems in a financial services organization include: Data assets:

• Customer data: This includes information such as customer names, addresses, contact information, employment history, financial history, and investment preferences.

• Account data: This includes information such as account numbers, account types, balances, and transaction history.

• Market data: This includes information such as stock prices, bond yields, and currency exchange rates.

• Risk data: This includes information such as credit scores, loan-to-value ratios, and collateral values.

Systems:

• Core banking system (CBS): A CBS is a software system that banks use to manage their day-to-day operations, such as account management, deposits, withdrawals, and loans.

• Customer relationship management (CRM) system: A CRM system is a software system that organizations use to manage their interactions with customers.

• Enterprise resource planning (ERP) system: An ERP system is a software system that organizations use to integrate and manage their core business processes, such as accounting, manufacturing, and sales.

• Risk management system: A risk management system is a software system that organizations use to identify, assess, and manage risks.

• Fraud detection system: A fraud detection system is a software system that organizations use to detect and prevent fraudulent transactions.

In addition to these systems, financial services organizations may also use a variety of other systems, such as trading platforms, portfolio management systems, and compliance systems.

​Data Quality Assessment: Evaluate the quality of your data to identify issues like inaccuracies, duplication, and inconsistency. This will be impactful to get buy-in from the executive committee. I managed a technical team and prioritized them to perform ad-hoc analysis of the data. I had previous experience using TAMR and provisioned it for data quality analysis. TAMR uses machine learning capabilities to perform massive data comparisons to identify data duplicates and other data quality issues. If I get time, I'll do a separate post on TAMR and how to leverage it (or similar tools) for these needs. Note, I also used Informatica tools (Informatica Data Quality) for data quality analysis. However this was not during Month 2 Week 1. In my previous company, I'd done similar analysis using Talend. The presentation materials I'd created had charts illustrating the extent (quantify) of quality issues and explained (qualified) how this will impact loss of revenue, loss of trust in data, etc.

Sash Barige

Apr/30/2022