Modern-day organizations are embarking on digital transformation strategies to cope with the digital revolution. It has enhanced the importance of data and made it a crucial asset to ensure success. Enterprises collect a huge amount of internal and external data and data governance is essential to make it usable.
The following is a guide to understanding data governance, its importance, and its impact on the company’s objectives.
What is Data Governance?
Data governance refers to the process of managing the usability, reliability, accuracy, and security of data present in enterprise systems. It is a set of procedures and principles that ensure high-quality data throughout the complete data lifecycle.
Big data and digital transformation efforts have enhanced the importance of data governance programs. With the increase in data volumes from new data sources like Internet of Things technologies, enterprises need robust data management practices to increase business growth.
Data governance roles and responsibilities involve measures to ensure that data is accurate and reliable and determine steps to be taken to manage and maintain data integrity. Furthermore, data governance encourages accountability and responsibility for data inconsistencies.
Data governance involves a massive overflow of data that makes daily data processes challenging for your business. High volumes of unorganized data cause complex data governance challenges that threaten your company’s reputation.
Without quality data governance tools, skilled data leaders, and hubs of organized data, businesses may fail to maintain the integrity of their data.
Improving your company’s data governance is essential to guarantee that you provide customers with high-quality data and maintain a healthy flow of data. The Gartner data governance maturity model helps businesses assess their data governance initiatives and identify weaknesses throughout the data pipeline.
However, it is essential to go beyond manual models when managing a massive quantity of data and start implementing data governance tools. Gartner provides excellent resources for learning about data governance and specific tools to simplify governance in your business.
Additionally, tools like Acceldata’s data observability platform are essential for managing your data pipeline and are a perfect, all-in-one solution to your company’s current data governance challenges.
Acceldata acts as a comprehensive data governance platform to address weak spots in your data pipeline and foster a greater understanding of your data’s importance. Effective data governance is the most critical solution to enhance data quality, boost security, reduce or eliminate data silos, and ensure that data access is distributed appropriately.
Watch this video by IBM Technology for a visual walkthrough of data governance.
Types of Data Governance Programs
Data governance policies need to align with the organization’s business practices and culture to maximize benefits. Generally, there are four types of data governance programs:
- Adaptive, which consists of a set of data governance principles that can be used flexibly and included in business operations for appropriate data quality management.
- Formalized, which are training programs created as a part of a data literacy initiative to encourage appropriate data governance.
- Non-Invasive, which includes formalization of existing data governance roles and practices.
- Command-and-Control, this is a top-down approach that includes data governance rules and assigns associates to follow them.
While designing a data governance program, enterprises need to consider which operational model will be suitable for their business size and structure. Here are three basic data governance models.
- Centralized- In this data governance model, enterprises designate one person as the lead for data governance. They are responsible for making required decisions and determining the direction of the data governance program. It increases security and improves quality control.
- Decentralized - In this model, a committee is created to design and manage the data governance strategy for the organization. This model contributes to better data quality, increased efficiencies, and shared maintenance.
- Hybrid - In this model, each area of the enterprise owns its data and metadata, and they are free to create their policies, standards, and procedures that best suit the business needs.
Importance of Data Governance
The 2022 report by Gartner estimates that around 80% of organizations planning to scale their digital business will fail by the year 2025 due to the lack of a modern approach required for data governance. Enterprises need to effectively manage their data uses to support business growth.
Effective data governance improves data analytics, which in turn helps in improved decision-making. Moreover, it helps to eliminate data errors or inconsistencies that can cause integrity issues.
There are vast benefits of data governance that make it an essential tool for your organization. However, businesses new to the concept might question the importance of data governance toward an organization’s overall well-being and sustainability.
Data governance is key to an organization because it steers a healthy culture with minimal chances of inaccurate, slow data. Processing data without a data governance framework makes it cumbersome for data teams to leverage data integrity and reliability.
You might ask: what is the impact of data governance on a business? Data governance is essential to a business because it fast-tracks an organization’s trustworthiness and transparency while helping teams make agile, data-driven decisions to benefit the company.
Data governance basics highlight the core principles of quality governance, including accountability, accuracy, and relevance of data.
One must sequence-specific data governance roles alongside comprehensive data governance software to ameliorate the overall functionality of the organization. Among the best data governance examples are roles in monitoring and improving a company’s data governance, including a data manager, user, steward, and custodian.
Along with designating data governance roles, organizations must take precedence of quality data governance tools to guarantee accurate and efficient data processes. Harboring your data through a straightforward data governance framework is the simplest when implementing platforms and solutions like Acceldata.
Goals of Data Governance
Let’s look at the goals of data governance and how they’re manifested in an organization. Though the goals may vary among organizations depending on their business type and values, some common data governance goals can include:
- Enhancing data security by establishing roles and responsibilities related to data ownership.
- Meet regulatory guidelines and avoid legal issues related to data.
- Ensure the right business decision based on accurate and trustworthy data adequately aligned with enterprise value and purpose.
- Define data distribution policies, roles, and responsibilities of people involved.
- Optimize staff effectiveness by providing the right data assets.
- Optimize the planning process by offering clean and well-structured data.
- Eliminating re-work by offering trusted data assets that are capable of serving various purposes.
- Define data quality KPI to boost operational efficiency.
Primary Benefits of Data Governance
High-quality data is essential to a company’s sustainability. While minor errors in your data pipeline might not seem like a big deal, these errors can culminate and form significant problems in your organization by disrupting your workflow. Improving your data pipeline requires comprehensive data governance to organize data and maintain its integrity. Many benefits of data governance make it an essential factor in a company’s overall well-being.
Data governance frameworks enhance the data quality and value within the organization. It not only improves the data accuracy but also impacts data-based outcomes. Some of the benefits of data governance include:
Data governance ensures data accuracy, completeness, consistency, and integrity. Good and effective data allows enterprises to comprehend their customers and workflows better, which in turn optimizes the overall business performance. Data governance frameworks and tools can eliminate potential inaccuracies and allow close inspection of data errors.
Better Decision Making
One of the key benefits of data governance is improvement in decision making. It applies to the decision-making process as well as the decisions. Well-governed data is more comprehensive and discoverable, making it easier for data teams to find useful insights. It ensures that decisions are based on good data, allowing greater accuracy.
Drives Data Literacy
Data governance frameworks create pathways for different teams allowing them to come together and create an understanding of data across systems. This shared understanding helps to manage data standards across the organization.
When implemented appropriately, governance provides a comprehensive view of an organization’s data assets. Moreover, it offers greater accountability by determining the responsibilities of specific data.
Ensure Data Privacy and Compliance
Data governance policies are created in a way to meet the demands of government regulations associated with privacy and sensitive data. Moreover, it helps to meet the industry requirements like Payment Card Industry Data Security Standards (PCI-DSS).
Data governance tools help to avoid any sort of issues caused due to violations of legal rules and regulations. It sets guardrails that prevent the misuse of data and data breaches.
Promotes Data Analytics
Quality data is the foundation of advanced data science initiatives like complex machine learning projects, AI projects, and even business intelligence reporting. In addition to this, it enhances data analytics by ensuring that the data is good, accurate, and effective.
Challenges of Data Governance
Data volume and collection are surging more than ever. Therefore it is essential to use a strong data governance framework. Good data governance can help to balance the needs of data management and collection.
Most organizations need to create a practical data governance framework due to the challenges of data governance. Though the benefits of data governance are endless, the possible risks of different governance approaches cause hesitation for many companies.
Some common challenges associated with data governance include:
It is common for data to become segmented or siloed within an enterprise. It happens due to various reasons like new data sources, the rapid pace of data collection, growing data infrastructure, and the constant growth of new technologies. As enterprises collect more and more data, it often becomes siloed, making it difficult to analyze the data and drive meaningful information from it.
Many organizations face the challenge of limited budget, tools, or even skilled workers to maintain their data governance program. Enterprises often have various priorities in addition to the IT work so they need to create a separate team to handle data governance programs and policies. It is essential to create the right funnel for data governance efforts to be successful.
Lack of Appropriate Leadership
Another challenge of data governance faced by enterprises is the lack of appropriate leadership. Skilled and experienced leadership provides the right direction to the governance team. Moreover, governance leaders can create the right internal policies for everyone to follow. Not everyone is an expert in data making it tough for enterprises to find the right leaders.
Comprehensive data governance is one of the top ways to improve your company’s functionality and overall data performance. However, the impact of poor data governance can significantly harm your business, making it essential to implement tools and solutions to eliminate governance risks. Identifying your organization’s challenges regarding data governance is essential to mitigating data governance risks and controls.
Data Governance vs Data Management
Data management is the process of collecting, storing, processing, securing, and storing data of an organization efficiently and cost-effectively. The goal is to help organizations optimize data usage so that they can maximize business outcomes.
On the other hand, data governance is one of the components of data management. It is the practice of managing the process of data management. It helps to define the roles, processes, tools, metrics, and communications associated with data.
Data governance also includes other areas of the data management cycle like data storage, processing, and security. Data management and data governance teams need to work together to execute an adequate data governance strategy.
The Benefits of Data Management
Comprehensive data management is essential to your organization’s sustainability and communication between team members. There are myriad benefits of data management strategies that will significantly improve your data integrity when implemented correctly.
Among the benefits of data management to organizations is that comprehensive data management establishes a concrete framework to reduce data inaccuracies and improve data-driven decision-making. Organizations can improve every step of the data pipeline with an effective data management strategy to encourage transparency during the data journey.
However, you might still be asking: why is data management important? Moreover, why is data management important to an organization?
Data management helps organizations and team leaders improve the quality of their decision-making because it offers high-quality and prompt insights into data trends, indicators, and projections.
This information eliminates the challenges of data management for organizations with an unorganized framework and strategy to grapple data inaccuracies.
Furthermore, organizations must understand the important data quality dimensions needed to gizmo a system that achieves its intended purpose. The top examples of data management and data observability functionalities are its ability to ensure accuracy, completeness, consistency, freshness, validity, and uniqueness of data.
The emphasis of data management in education, enterprises, healthcare, or any other organization is undeniable, and comprehensive data management promotes consistency that prevents inaccuracies common in data pipelines.
The Benefits of Having a Data Strategy
Organizations should consider the perks of data strategy tools to ensure data quality management and boost data reliability. A blanket data strategy framework contains essential components of an organization’s data observability and governance.
Developing a data strategy requires organizations to nucleate solutions to envision and prevent data anomalies, increase data visibility, and produce accurate data consistently.
You might ask: what is data strategy? Data strategy covers various organizational techniques and is best understood through comprehensive data strategy examples. A data strategy PDF, such as information provided by Acceldata, outlines the yardstick procedures in your data strategy to enable accurate and helpful data in your organization.
It is a staple to have a data outfitting strategy within your data framework, and a provisioning strategy is crucial for maintaining data observability and safely collecting your organization’s data.
A data science strategy is also a requisite to improving your organization’s data governance framework.
A data science strategy certifies the sustainability of your data ecosystem to ensure that your organization makes effective, actionable decisions that benefit your data governance processes.
Your company’s data science strategy, provisioning strategy, and overall governance framework are crucial for encouraging data democratization in your organization and crafting a data pipeline with accurate, up-to-date data metrics.
Data governance describes the process of protecting a company’s data to guarantee consistency and accuracy. A comprehensive governance framework will outline your company’s data governance challenges and approaches. For instance, a framework and data observability tool can identify examples of bad data governance in your organization.
Finding solutions to improve governance frameworks is essential as you address the impact of poor data governance. KPMG is a standard tool to improve your company’s data management processes and governance framework.
Data observability platforms like Acceldata are crucial solutions to boost your company’s data validity and reliability.