Is it possible to create a lean governance program in a strict regulatory environment? Can you reduce its complexity without incurring undue risk? Where do you start? If you’re trying to build a foundation of trusted data within your organization, you’re probably asking these questions, too.
While data governance covers a lot of areas and serves a crucial role for any organization, it doesn’t have to be complex. In fact, it can fit naturally into your existing processes. This article will explain how to launch data governance activities that integrate well.
Learn all about data governance processes, tools, best practices, benefits of data governance for organizations and stories that data tells, and more below.
Data Governance vs. Data Management vs. Master Data Management
It’s easy to assume that terms describing information-related processes are interchangeable. Data governance, data management, and master data management get confused frequently.
- Data management refers to the full data lifecycle and all practices for handling data within the organization.
- Data governance is the core component of data management and only refers to standards ensuring the availability, usability, integrity, and security of the data.
- Master data management is also a slightly different discipline. It focuses on data stewardship and accuracy by reconciling fragmented views of key entities into a single view.
So, before you establish a data governance process, make sure you understand exactly what you’re dealing with. It’s not concerned with execution and operationalization, and it’s not about decision-making. Data governance is about the roles, responsibilities, and processes that ensure the ownership of and accountability for data assets.
Building a Data Governance Framework
All programs have a data governance framework that provides a holistic approach to discovering and storing data. Think of it as the hub for different knowledge areas. When establishing a data governance strategy, each of the following facets of data collection and use should be considered.
- Data architecture. Technology and infrastructure design for supporting your data strategy. It includes logical and physical data assets.
- Data modeling. Blueprint for designing new databases and reengineering legacy applications, also a visual representation of the entire information system and parts of it.
- Data storage. Practices for retaining digital information on computers and other devices, storage deployment, and management.
- Data warehousing. Policies, procedures, and standards for data resources and data access (data warehouse examples: enterprise data warehouse, operational data store, data mart).
- Data security. Practices for classifying data according to its sensitivity, safeguarding it, and preventing data loss.
- Data quality. Defining, monitoring, and maintaining the integrity and value of information.
- Metadata management. Collecting informational assets to convert them into enterprise assets deriving value from data about data.
- Business intelligence. Tools and processes to transform raw data into meaningful and actionable information.
10 Steps to Start Your Data Governance Program
With areas of governance defined, let’s move on to the steps for building data governance programs.
Important note: you shouldn’t be looking for a one-size-fits-all program because every organization is different. These are broad guidelines. If you’re not sure how these steps would fit into your data operations and overall business processes, you may want to discuss them with a data specialist.
1. Define the Objective
Understand why data is important to your company. Answers might range depending on who you ask and whether they’re looking to improve internal data analysis, optimize operations, or create a long-term competitive advantage. So, consult with different people across the organization to have various inputs.
It may also be the case that some people don’t recognize the value of data for their work. As you formulate your data governance vision, communicate how your data processes might change across teams. The better people understand the role of data, the better governance program you can put in place.
2. Determine the Optimal Level of Data Governance
As mentioned, governance programs may look different from one organization to the other. That also applies to the level of governance needed. Here are some of the factors to consider:
- The size of the organization
- The complexity of data
- Resources you can make available (time, budget, staff)
- Relevant regulatory requirements
- Industry-wide data standards
- The need for analytics consulting
If you don’t have any kind of governance program, don’t try to govern every aspect of your data right away. Focus on high-priority data subject areas and accomplish the main objective of data governance in small steps.
The level and intensity should scale with the company. The bigger the size, needs, risks, maturity, and capabilities, the more demand should be placed on data governance.
3. Assign Roles and Responsibilities
A top-down approach usually works best. Identify leaders throughout the organization that can see the program through. They will be the ones to introduce the new vision for how data will be used, explain what’s expected from everyone, and remove barriers to implementation.
In addition to those who will advocate for change, you need to set up and fill roles for day-to-day operations. These are data owners that have direct data quality activities, oversee data repositories, etc. Other participants include data stewards, who are the governing body for most data-related decisions. Finally, form a steering committee to set and champion the overall data governance strategy, as well as hold other members accountable to timelines and outcomes.
4. Create a Data Governance Committee Charter
Speaking of the committee, you need a formal data governance team charter, putting all the roles and responsibilities in writing. Other key components of a charter are listed below.
- Purpose – Why the group exists and what problems it will address
- Scope – What data governance is supposed to do and accomplish, boundaries for what it won’t cover
- Goals – SMART goals to address the problems identified in the purpose statement
As the scope of data-related processes grows, make changes as needed, incorporating feedback and suggestions.
5. Establish Data Workflows
Workflows allow you to standardize how work gets done and ensure consistent results, and data workflows move data across business systems from one step to the next. Essentially, it means establishing a data supply chain.
As you try to manage a big data workflow, be prepared to encounter a number of different data governance tools and a vast assortment of technology components. Try to avoid using too many and make sure the ones you have are synced and integrated.
6. Establish Data Controls
This is one of the most important steps—establishing appropriate controls for data quality and integrity. Bear in mind that different types of data go through different processes. So, people involved with documenting and implementing the appropriate controls should know which practices apply where.
Assign data controllers who will have full oversight over data storage, access, and updates. They will also handle personally identifiable information and enforce rules for the deletion of data.
7. Determine Authoritative Sources
As the number of your data sources grows, it will get increasingly more crucial to prioritize the ones you can trust.
A trustworthy data source is any source that offers reliable, independent, and, most importantly, updated data. For the highest data accuracy, it needs to be updated in real-time. Verifiable information will ensure regulatory compliance and correct data-based managerial rulings.
Before you can agree on authorized sources of data organization-wide, classify data in data models. These, in turn, will serve as differentiators for creating a catalog of data domains and sources.
8. Identify Potential Risks
Learn about relevant security laws and compliance requirements. Keep in mind that your organization is likely to fall under several regulations, so you can’t do away with complying with one. Examples of such regulations are listed below.
- General Data Protection Regulation (GDPR)
- Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule
- Gramm-Leach-Bliley (GLB) Act
- Sarbanes-Oxley Act (SOX)
- Federal Information Security Management Act (FISMA)
These and other regulations will inform your data security measures.
You should also look into excess access and storage locations. Ensure users have the appropriate amount of access to do their job (no more), and all sensitive data storage locations are protected from common threats and vulnerabilities.
9. Automate Processes
By now, the data governance committee has established a data management glossary and key metrics, and the roles and responsibilities are defined. All that is left before roll-out is automating data governance principles and their associated components. This way, users will get a full 360-degree view of the data landscape in as little time as possible and avoid manually cross-referencing every data asset in every department.
Another benefit of self-service data governance tools is the ease of use. The key to creating a good data governance program is helping business users handle data without IT’s involvement.
10. Maintain
It might take some time before data governance is fully internalized and ingrained. In the meantime, you should be prepared to do some extra work upholding the goals of the program. Leaders should be overseeing and closely managing data—specifically, the transition from non-governed data assets to governed data assets.
Get ready for data-related questions as you roll out:
- Should a given data entity be governed?
- What does DBT stand for? What type of DBT do we need?
- How do we measure program success?
- What is analytics consulting, and do we need it?
Finally, no matter how successful your program turns out to be, don’t treat it as self-sustaining. Assess performance on a scheduled basis and adapt your program based on the changing needs and requirements. But it will get easier after you go through that initial cultural shift within the organization.
Data Governance Tools
It’s rare to have tools specifically developed for data governance. Cross-functional data governance solutions are more popular and offer better value.
Data Integration
This tool acts as an ETL (Extraction, Transformation, and Loading) solution for the entire data ingestion lifecycle—starting from initial capture and necessary conversions to allocation.
A data integration tool is meant to collect structured and unstructured data from various sources and in different formats. Then it standardizes input based on the rules set out by the governance program and transforms available data assets, bringing them to high data quality. Finally, it moves them to their target destination.
Data Processing
Data processing tools help single out specific content types. As a result, users can get access to easily digestible information and extract valuable content. This supports one of the key principles of data governance—fostering an organized system to ensure clean, consistent data and building effective procedures for internal data analysis.
Best Practices for Data Governance
Even with a strong governance foundation, the work should be ongoing. It’s unreasonable for organizations to seek a quick fix for their governance woes. Brace yourself for a long journey of constantly assessing your program, understanding new challenges, benchmarking, and identifying areas of improvement.
These practices lie at the center of all successful data governance and stewardship programs:
- Ensure integrity in all data-related dealings. All program participants should discuss the drivers, constraints, options, and impacts of data in a truthful way.
- Data-related processes should be accompanied by documentation and meet other auditing requirements.
- Participants and auditors should have clear access to data and controls; all processes require transparency.
- Determine accountability for cross-functional data-related processes, preferably define a specific position for it.
- Define responsibility for individual contributors and groups of data stewards.
- Introduce checks and balances for business and technology teams, with standards for all stages of data collecting and handling.
- Standardize enterprise data, focusing on a data management dictionary first.
- Support proactive and reactive changes, depending on the changing landscapes (regulatory, technological, etc.).
In Conclusion: Do You Need a Data Governance Program?
Data tells a story, and most trying to decipher that story may already have certain data governance practices in place. But where they fall short is formally structuring all the processes and responsibilities. An exhaustive strategy shouldn’t leave anything that affects the integrity and security of data behind—people, applications, business units, or functions. That can only happen with systematic, formal control.
With a data governance team, you will standardize the answers to some important questions about data:
- What data do you have?
- Where did it come from?
- Can you trust it?
- Who is responsible for handling it?
- How is it used?
In addition to always having answers to high-level questions, an effective data governance strategy brings other benefits:
- Improved data quality and controls for poor data quality (data quality measurements)
- A 360-degree view of all business entities
- Consistent compliance with government regulations and industry requirements
- Better risk management
- Effective data management
Start rolling out your new governance program slowly and build on the momentum. These ten steps should put you in a good position for a foundational program, which will grow and amplify the impact of data governance.
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