Data governance is a set of principles for managing data throughout its life cycle, from acquisition, usability, data security, and integrity to improving data quality. As businesses across all industries continue their digital transformation efforts, data has rapidly become their most important asset.
For strategic company decisions, senior managers want reliable and timely data. Marketing and sales professionals require reliable data to comprehend what customers desire. Personnel responsible for procurement and supply chain management want precise data to maintain inventories and reduce production costs. Compliance officers must demonstrate that data is managed following internal and external regulations. And so forth.
What does data governance mean?
As defined by the Data Governance Institute (DGI), data governance is "a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models that describe who can take what actions with what information, when, under what conditions, using what methods."
This set of principles comprises a collection of practices, responsibilities, policies, standards, and measurements that ensure the efficient and effective use of information, enabling a company to accomplish its goals.
These characteristics suggest that robust governance conforms to internal data standards and procedures to maintain the integrity of data usage. It specifies who can do, what actions, under what circumstances based on what data, and using what methods.
As new data privacy rules and regulations are implemented, companies must create, implement, and adhere to ethically competent data governance frameworks. A realistic structure for it includes operational roles and duties as well as tactical and strategic objectives.
Why is data governance important?
Data governance offers transparency and protection against ineffective data management and non-compliance.
Low data quality impacts every business element, from marketing insights to financial planning, and impedes the achievement of crucial KPIs. When data quality is inadequate, making informed decisions or taking reasonable risks is impossible.
What are the benefits of data governance?
Despite initial difficulties, data governance enables businesses to remain adaptable in oversaturated marketplaces while remaining compliant with ever-changing regulations.
1. Better and more prompt choices
Users within your business have access to the information required to reach and service customers, build and improve products and services, and capture chances for new revenue streams.
Strong data governance enables authorised users to access the same data, eliminating the risk of data silos inside an organisation. As a result, the IT, sales, and marketing teams collaborate, share information and perspectives, cross-pollinate knowledge, and conserve time and resources. In addition, they have access to the information required to reach and service customers, build and improve products and services, and capture chances for new growth revenues.
2. Improves cost control
Data enables more efficient resource management. Due to the ability to prevent data duplication caused by information silos, you do not have to overbuy and maintain costly hardware.
3. Enhanced compliance
The increasing complexity of the regulatory environment has increased the importance of companies establishing robust data governance policies. As a result, you prevent risks connected with noncompliance while anticipating future requirements proactively.
Implementing a data governance system facilitates your organisation's compliance with the most recent laws, such as the General Data Protection Regulation (GDPR) of the European Union, the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI-DSS), and others.
What are the 4 pillars of data governance?
The effective data governance structure is based on four pillars. Establishing a data governance structure around these pillars is crucial while creating a layout. Doing so satisfies the fundamental need for data governance to be effective for the company and the end users.
1. Development Of Standards
Standard creation is the first and fundamental pillar of data governance architecture. This standard must be curated in response to "why must the data be from your organisation?" In this pillar, the definition of the organisation's master data must also be developed. Additionally, taxonomies, enterprise data models, and other technical standards must be curated.
With the successful implementation of the first pillar, the organisation will also have its primary communication language. Standardisation will also contribute to the uniqueness and dependability of the data supplied.
2. Curation Of Policies And Processes
An efficient framework for data governance focuses on establishing the appropriate policies and procedures for the future. The organisation must establish data management, usage, and execution policies within this pillar. Additionally, it must select the appropriate processes. The rules for data-related business must be defined, and data changes must be controlled. With this, the data's control or audit must also be determined. This pillar also highlights the need for data accessibility and distribution via all measurable mechanisms.
3. Organisational System
Any business or organisation faces the most significant difficulty in establishing the organisational structure for its data governance. This pillar highlights the need for organisations to define the roles and duties associated with data accountability. These positions can be divided to varying degrees, including business and IT personnel.
The organisational system must also address management challenges to maintain consistency with the data governance plan. For example, it will be simpler for the organisation to determine who is performing which tasks through the pre-definition of roles and duties. This architecture must also include executive councils and independent day-to-day implementers.
4. Technological Usage
The use of technology is the last pillar of the data governance architecture. Technology has somewhere accelerated the usage of data governance and its instruments. While employing technology for the same, there are a few things to remember. First, the framework's technology foundation must be appropriate for the policies. Companies must choose the technology tools, such as spreadsheets, based on the policies. Utilising technology in a data governance framework can assist with erroneous standards enforcement and audits. Additionally, it can assist in streamlining prior solutions to prevent errors during the final execution of data governance strategies.
Components of Successful Data Governance
Certain components are required to create an effective data governance plan. This set of principles serve as the foundation for all data management programs. In addition, it is a necessary practice that serves as the foundation for all other aspects of data management expertise. It also includes components for each knowledge area that meet the company's data management needs. These ten components are as follows:
1. People - Every data governance program's core comprises specialists on the field, data stewards, and other key business and IT personnel. They develop and maintain workflows to meet the company's data governance needs.
2. Data Planning - Developing an enterprise data strategy is a crucial step in data management; nevertheless, many businesses are unaware of the need for a formal and recorded data plan.
3. Data Processes - For data management, data governance programs must build important data processes. Common data practices include issue tracking and resolution, quality surveillance, data exchange, lineage tracking, evaluation, and automated data quality testing.
4. Data Protection Regulations - A data policy is a high-level collection of statements that outline expectations and desired outcomes with the intent to influence or steer business-level data practices. These activities result in the establishment of data governance policies. The sharing of outbound data, regulatory compliance, and other policies are merely a few instances.
5. Data Rules & Data Standards - A data standard is a framework and method for ensuring compliance with a data policy. A data rule governs or restricts the action to ensure adherence to data standards, assuring compliance with the data policy.
6. Data Protection - Data security is concerned with protecting digital data, such as those in a database, from destructive factors and unwanted actions of authorised and unauthorised users, such as theft, hacking, or a data breach.
7. Technology - Numerous technologies will be required for the data governance program to be as automated as possible. Smaller projects frequently utilise the company's existing software platform. Larger data governance projects generally invest in software designed for data governance and enterprise-specific activities.
8. Key Performance Indicators - Establishing business metrics and KPIs for monitoring and analysing the data governance program's overall business impact is crucial to its success. Metrics and key performance indicators must be verifiable, clearly traceable through time, and monitored the same way each year.
9. Communications - Data governance communications encompass all written, spoken, and electronic interactions with organisational audiences interested in or required to know about the data governance team's work. Therefore, a communication plan should be included in a governance program from the start, including objectives, targets, and communication instruments.
10. Socialisation - The data governance socialisation plan is a strategy and process for integrating data governance into an organisation's rules, culture, structure, and operations. This new method of effectively controlling an organisation's data must be implemented throughout the organisation and become ingrained in the culture.
Data Governance with Trackingplan
Data governance is required in today's increasingly dynamic and competitive business world. Now that businesses can collect vast quantities of heterogeneous internal and external data, they require discipline to optimise their value, mitigate risks, and decrease costs.
Trackingplan is an always up-to-date single source of truth and data governance tool. It helps you eliminate outdated spreadsheets and ensures the data you add to your data warehouse is always clean and follows the expected specs. In addition, Trackinglan helps you discover, understand, and document your data and improve team communication.
Book a demo now and always be up to date with existing problems in your data governance, get notified when they are solved, and ensure everyone on your team is on the same page.