Organizations need data governance for many reasons, not just to comply with a rising number of data privacy and protection rules, such as the GDPR of the European Union and the California Consumer Privacy Act (CCPA).
A lack of it can cause more pain than a fine. One of the most impactful areas of data governance is how it helps handle data discrepancies across various systems throughout the company.
Consider this relatively simple example: customer names may be listed differently in sales, logistics, and customer service systems. This might complicate data integration efforts and cause data integrity difficulties, affecting the accuracy of Business Intelligence (BI), corporate reporting, and analytics applications.
What’s worse, these data inaccuracies might go undetected, reducing the value of BI and analytics to consumers. This can easily jeopardize trust in data across the organization.
How do you establish data governance? What components make up this area of data management? Keep reading to get a primer on data governance.
Note: This is the fifth part of our series that dives into enterprise data architecture – you can find the previous parts here:
What is data governance?
Data governance is all about controlling data availability, accessibility, integrity, consumption, and security using internal data standards and regulations. The primary goals here are breaking down data silos and delivering data that is consistent and trustworthy.
This area becomes crucial as companies face novel data privacy regulations and start to rely more on data analytics to help optimize operations and drive business decisions.
A well-designed data governance program usually consists of a governance team, a steering committee that serves as the governing body, and a group of data stewards. They work together to create standards and policies, as well as implementation and enforcement strategies that data stewards typically use.
Why does data governance matter?
It can bring many advantages, including:
- Enhanced data quality
- Lower data management costs
- Increased access to critical data for analysts and business users
- Accurate analytics
- Higher regulatory compliance
All in all, this improves a company’s decision-making processes by providing executives with more information, which results in a competitive advantage in the sector.
Goals of data governance policies
Breaking down data silos in a company is a primary focus . When various business groups implement independent systems without centralized coordination or an enterprise data architecture, you’re going to deal with data silos sooner rather than later.
This is where data governance comes in. It seeks to unify the data in such systems through a collaborative approach that includes stakeholders from diverse business divisions.
Another purpose is to ensure that data is utilized correctly, both to avoid introducing data inaccuracies into systems and to prevent the misuse of sensitive data. Companies can achieve this by developing consistent data use regulations as well as mechanisms for monitoring usage and enforcing the policies on an ongoing basis. Data governance helps strike a balance between data-gathering activities and privacy regulations.
Key components of data governance
Data governance framework
A good framework consists of policies, processes, and technology tooling. It also specifies the program’s mission statement, goals, and how success will be assessed, as well as decision-making roles and responsibilities for the many functions that will be included in the program.
The governance structure is key here because it helps everyone involved understand how the program will operate.
On the technological front, teams use data governance solutions to automate sections of program management and help with workflow management, governance policy formulation, process documentation, data catalog building, and others. They can be used in tandem with tools for data quality, metadata management, and master data management (MDM).
Data governance policies and standards
A data governance policy explicitly defines how data processing and data management should be carried out to ensure that data is accurate, consistent, and accessible throughout an organization’s systems. The policy also specifies who is liable for data in specific situations.
Individual policies for data quality, access, security, privacy, and usage are generally included in such guidelines, as are varied roles and responsibilities for implementing such policies and monitoring compliance with them.
The policy serves as the foundation of a company’s program. The policy should describe the ideas, policies, and standards that senior business and IT executives have concluded are essential to guaranteeing security from both internal misuse and external threats.
The policy-making group – a committee or council – should ideally be composed mostly of corporate leaders and other data owners. This group creates a policy statement under the supervision of data governance managers.
This statement outlines the organization’s data governance structure as well as a set of governance guidelines and procedures for the executive team, business managers, data analysts, and operational staff to follow.
Data governance principles for policies
Policies often include:
- Data accuracy – Initiatives aim to enhance data quality, and clean, accurate data sets are the clearest indicator of good governance. To avoid data mistakes, inconsistencies, and other issues and detect and resolve them, the data governance policy should incorporate data quality and integrity processes.
- Data access – This policy ensures data access for business and analytics users. They can only access the data they require for work, not sensitive or proprietary data. Role-based access control may be part of the governance policy – as well as the repercussions of unauthorized data access.
- Data use – Data governance policies oversee ethical data usage. They’re meant to make sure a firm follows data privacy rules and doesn’t put customers at risk by misusing their personal data. The policy generally outlines penalties for violators, including data loss, disciplinary action, termination, and legal action.
- Data integration – This involves guidelines to standardize data definitions and remove data silos. The goal here is to make relevant data available to users throughout an organization and ensure that various departments aren’t using conflicting data sets.
- Data safety – Policies often cover data security and privacy, including end-user data security obligations. Internal data categorization rules are used to manage security, access, and usage in the policy.
Data stewardship and data owners
Data governance requires stewardship. This involves assigning duties and tasks to a data steward or team of data stewards to administer data governance principles.
Data stewards ensure that users:
- Are responsible for the data in their care
- Monitor the performance of data governance initiatives
- Identify key business data elements
- Create and maintain data quality rules
- Develop data policies and regulations
- Foster adoption
- Keep definitions and terms updated
Users will keep creating and using data that, if mismanaged, will be a burden. Compliance violations might result in massive fines. That’s why data management success starts with data stewardship.
Pillars of data governance
In a data governance initiative, a data steward is in charge of one section of the organization’s data. This person also assists in the implementation and enforcement of the policies.
Data stewards are frequently data-savvy business users who are subject matter experts in their fields. They work alongside data quality analysts, database administrators, and other experts in data management, and collaborate with business divisions to discover data needs and concerns.
One of the most powerful driving reasons behind governance initiatives is the improvement of data quality. The correctness, completeness, and consistency of data across systems are critical characteristics of effective governance programs.
Data cleansing corrects data mistakes and inconsistencies while also correlating and removing duplicate instances of the same data pieces to align how customers or items are presented across systems. Data tools enable this via data profiling, parsing, and matching operations.
Master data management (MDM)
Another data management discipline that is closely related to data governance procedures is MDM. MDM solutions provide a master collection of data about customers, products, and other business entities to ensure data consistency across systems inside a company.
This makes MDM inextricably linked to data governance. MDM efforts, like governance programs, may spark debate in companies due to differences in how departments and business units construct master data. However, the combination of the two powers a trend toward smaller-scale MDM programs motivated by data governance objectives.
Data governance is connected to information governance, which focuses on how information is used in a company as a whole. At a high level, it is a component of information governance, but they are often regarded as distinct disciplines with comparable goals.
5 best practices for data governance implementation
1. Assess the current state and define your goals
Before setting out to build an initiative and form a council, carry out a comprehensive assessment of the existing data landscape and practices. Understanding where your organization is in terms of data management is an essential first step.
Once you have a clear understanding of your current state, you’re ready to set the objectives for your data governance initiative. An important part of this is defining measurable goals – without them, you have no way of knowing whether your program and organization are headed in the right direction.
2. Establish a data governance council
The data governance council is a team made up of data practitioners who deal directly with the governance program’s data sources but don’t answer to a more formal compliance department. These can be database architects, software engineers, and business analysts.
The team should constantly analyze laws relating to the governance program, identify areas where the program’s policies can be improved or expanded, monitor the program for issues, and track its progress.
3. Data classification and data inventory
Another important point relates to classifying data based on sensitivity, criticality, and regulatory requirements.
To perform data inventory and mapping, you need to have capabilities that allow you to categorize, catalog, and find data. This is a great starting point for a data protection program, as long as privacy risks are understandable at the time of data collection and access.
You also need to create a legal foundation for data processing and cross-border transfers. How you handle data and where you transmit it may become more complicated with time. That’s why you need structure to regulate the data protection process, including creating leadership, establishing policies, and training staff.
4. Data quality management
Organizations can’t achieve data quality without proper data governance. All of the duties mentioned above are closely related to data quality improvement and monitoring. A good data governance platform brings together data makers and data users, allowing for communication and common knowledge of data quality.
While current data may require extensive reorganization to increase its quality, you can use this experience to fine-tune data governance rules and procedures for onboarding new data.
Data teams typically approach this from two perspectives:
- Determine what is crucial to the business – This might be a regulatory report, a cube, or a key performance indicator (KPI).
- Data worth – Estimate the shelf life of poor data quality or the risk associated with low quality. They then focus first on the regions with the highest risk.
Once you identify and prioritize your areas of emphasis, you’re ready to build a collaborative framework for managing and creating policies, business rules, and assets to offer the essential degree of data quality control.
Once you understand how data moves through the company and what the standards are, it’s much easier to ask the data quality team to convert these standards into data quality rules and execute them on the data in those systems.
5. Data governance tools and technologies
You can find tools from many vendors on the market. Oracle, SAP, and SAS Institute Inc. are among the main providers. Most governance technologies are sold as part of larger suites that often include metadata management and data lineage capabilities.
Data catalog software is included in many data governance and metadata management solutions. It’s also offered as a standalone solution from Alation, Alex Solutions, Atlan, Hitachi Vantara, IBM, OvalEdge, and a variety of additional vendors, as well as cloud platform industry leaders AWS, Google, and Microsoft.
Data governance challenges and mitigation strategies
The early steps in data governance initiatives are sometimes the most difficult, as various teams within the organization may have divergent perspectives on essential data items, such as customers or goods.
These disparities must be addressed as part of the process – for example, by establishing consistent data definitions and formats. A good dispute-resolution method comes in handy here.
Demonstrating the commercial worth of data governance
Getting a program approved, funded, and supported can be difficult without upfront verification of the predicted business advantages. Presenting a strong business case for establishing data governance is a challenge.
Moreover, ongoing commercial value demonstration requires you to establish and track quantitative metrics, especially around data quality improvements. This might include the number of data inaccuracies addressed on a quarterly basis or cost savings. Other popular data quality indicators include accuracy and error rates in data sets, as well as data completeness and consistency.
Assisting with self-service analytics
By placing data in the hands of more users in businesses, the transition to self-service Business Intelligence and analytics has produced new concerns.
Governance systems must guarantee that data is correct and available for self-service users, but also ensure that those users don’t abuse data or violate data privacy and security rules. Streaming data used for real-time analytics poses an even bigger challenge in this area.
Managing large datasets
Implementation of big data systems introduces new governance requirements and issues. Data governance programs have traditionally focused on structured data stored in relational databases, but they must now deal with the mix of structured, unstructured, and semistructured data found in big data environments and data platforms such as Hadoop and Spark systems, NoSQL databases, and cloud object stores.
Massive data sets are frequently kept in raw form in data lakes and then filtered as needed for analytical purposes, complicating it even further.
Most companies have informal governance for apps, business divisions, and functions. But as they grow and become too large for people to execute cross-functional duties, creating a data governance council and developing policies ensures that teams capture maximum value from data.
Data governance delivers better decision support from consistent, unified data across the organization, delineates clear standards for modifying processes and data, reduces data management expenses, and enhances data compliance.
Prioritizing data governance makes sense. It helps you unlock data’s full potential and competitive advantage in the data-driven landscape.
To see how to implement data lake governance at scale with an open-source solution, take a look at this practical guide: Data Lake Governance at Scale with lakeFS.
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