Making Government Data Compliant, Secure, and Transparent
What Is Data Governance
Data governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an organization. It involves establishing policies, standards, and processes for collecting, storing, using, and sharing data and ensures that the data is consistent, accurate, and protected. Data governance helps organizations ensure regulatory compliance, improve data quality, and reduce data-related risks.
Why Does Data Governance Matter To Government
Data governance matters for the government because it helps ensure that public data is used in an ethical, responsible, and secure manner. Some specific reasons why data governance is important for government include:
Compliance: Governments must comply with various regulations and laws regarding data privacy and security, such as the General Data Protection Regulation (GDPR) in Europe, and data governance helps ensure that these regulations are met.
Transparency: Good data governance can increase transparency and accountability, as it ensures that the public has access to accurate and reliable data.
Improved decision making: By having high-quality data that is well-managed and governed, government agencies can make more informed decisions that better serve the needs of the public.
Protection of sensitive information: Government agencies handle sensitive information, such as personal data and classified information, and data governance helps to protect this information from unauthorized access, misuse, and breaches.
Improved public trust: By demonstrating a commitment to good data governance practices, government agencies can improve the public’s trust in their ability to manage and protect important data.
Use Case – How Capio Group helped Government implement Data Governance
Capio Group’s client – the California High-Speed Rail Authority (HSR) was seeking assistance with data management support due to an absence of proper policy and process with regards to data governance, data security, data stewardship, data classification and data privacy among others. There was also a gap with regards to administering databases and meeting the organization’s data-related objectives.
After onboarding, Capio Group began to execute the Capio Group Data Governance Process. Capio Group’s team first approached the issue by defining HSR’s business needs and data domains that data governance can address. By assessing the current state of HSR’s data management process and practices through discovery sessions, the Capio Group team worked on defining a data governance framework, governing body, data catalogs and classifications. Moving into the next phase, Capio Group started identifying members that are going to be part of the data governing body with specific roles and responsibilities. As the governing body was being finalized, Capio Group implemented tools and processes to measure and monitor progress on HSR’s data goals. This enabled the organization to identify and apply solutions to improve data quality, compliance, security, and reporting.
Capio Group Data Governance Activities:
- Develop Database Management Standards
- Develop Data Governance Standards
- Develop Data Classification Standards
- Develop Metadata Management standards
- Develop Master Data Management Standards
- Support daily production requirements
- Upgrade and Migration of Databases
- Develop standards for Metadata naming conventions
- Compile Acronyms associated with Metadata Management
- Metadata Naming Standards
- Implementation of new data software
- Amazon Web Services (AWS)
- Salesforce, Maximo, and Relativity
- Microsoft Azure for Power BI
- MS SQL Server with Always ON for Cluster Failover
- MS SharePoint
- iMS SQL Server Management Studio
- Microsoft Visio
- Erwin Data Modeler & Intelligence
- SolarWinds Data Performance Monitoring System
Customer Successes Through Data Governance
Capio Group resolved key issues HSR was facing prior to implementation of Data Governance:
- Lack of database monitoring product → Capio Group configured and implemented SolarWinds database monitoring tool
- Lack of data modeling and data governance solutions to assist in the implementation of various data policies → Capio Group successfully completed configuration and installation of Erwin Data Intelligence Suite
- Successful implementation of new databases meeting HSR business and technical goals
- Successful upgrade and migration of critical databases from older SQL Server version to the latest SQL Server version.
- Successful upgrade and migration of databases from other database types (Oracle, Postgres) to SQL Server
- Successful implementation and migration of other database and applications from third party vendors to HSR’s private cloud
- Development and implementation of: Data Governance Best Practices, Data Governance Maturity Model, Metadata Management Best Practices, Metadata Naming Standards, Database Physical Model Naming Standards, Data Acronyms, Master Data Management Best Practices, and Data Classification Standards
“During my time with the California High Speed Rail Authority, we worked with Capio Group to establish new, thoroughly documented standards for best practices, and to implement a range of new tools for performance analysis, monitoring and data modeling.”
Senior Database Administrator | HSR
How to Ensure Data Governance Implementation Success – Use these Strategies
For organization starting the data governance process, it is important to take into consideration the following best practices for data governance:
- Acknowledge the differences – Stakeholders must recognize the differences between making a business case for data governance and making one for a traditional technology project. Many of these differences revolve around the non-conventional topics of business process, change management, business benefit, and holistic impact. Understanding the differences will help shape the organization’s approach, and better position it from becoming another static.
- Define the discipline, program, and purpose – Take the time to characterize what data governance is and what it means to the organization. A simple definition of the topic and its purpose will go a long way toward getting everyone on the same page. This is especially important given that data governance has different meanings to different people. Fine-tuning the characterization of this term and its intent will help align the masses and advance the dialogue with business leadership. It is also a healthy exercise to clarify and document the purpose of the data governance initiative.
- Expectations – Identify short- and long-term expectations for the data governance program at both a business and technical level. People involved in day-to-day information management activities have very different expectations than business leadership. Examine realistic expectations that will satisfy both decision-makers and stakeholders.
- Top-down or bottom-up – Establish whether the proposed program will be a top-down or bottom-up initiative. Top-down is a corporate-endorsed structure that can effectively scale across multiple projects and the enterprise, one project at a time. Bottom-up programs are typically project-based, and struggle to get the funding and visibility they need to grow beyond a single initiative.
- Value proposition – Determine whether the value proposition for the program will be based on a single project, the organization as a whole, or somewhere in between. While a specific project is important, a business case for data governance is more compelling when it addresses the impact across an entire data domain and enterprise.
- Interview the business – Determine whether the creation of the business case should involve one-on-one interviews with various lines of business. This is where much of the business value can be found, yet organizations have a tendency to ignore this path.
- People to influence – Draft a list of individuals and groups (Stakeholder Register) that will need to be convinced in order to secure funding and support for the data governance program. Know who to persuade and why. The roster of people should include business leadership and associates. Never assume that people, even those involved in daily data activities, understand the benefits.
- Third party – Decide whether the organization will use a third party to build the business case. A third party can add value in number of ways:
- Provide industry experience and expertise
- Gain visibility into the broader organization
- Overcome the common stigma of being too close to the situation
- Deliver the message to business leadership from an objective, non-partisan source
- Success criteria – Establish the initial success criteria for the program. The organization should know whether or not the program has been successful – you can’t manage what you can’t track. Establish KPI to measure the outcome and provide insights that help the organization to make better decisions.
- High-level project plan – Establish an initial project plan with timelines to help identify and track activities and milestones.