Unlocking Success: The Vital Role of Data Governance in Business Growth

Donna BurbankIn today’s data-driven world, businesses can’t survive without efficient data management to maintain a competitive edge and make data-driven decisions. To guarantee data is maintained safely, properly, and in compliance with regulations as data volumes and complexity grow, businesses must develop robust data governance processes and standards.

Harvard Business Review Analytics Services conducted a survey and found that 67% of respondents said good data governance was critical to developing high-quality enterprise data. This is likely to stay the same since technological developments like Machine Learning and AI rely on data quality and as digital transformation projects gain momentum worldwide.

We intended to enhance data governance awareness to help data quality activists understand how data governance affects business settings, stakeholders, and company goals.

Data Governance Models

Depending on the specific demands of the organization and the specific data governance methods a company employs, individuals can choose from several different data governance models. Let’s check out some examples below.

• Individual Decentralized Execution

This setup is ideal for a sole proprietor who handles all data management and upkeep. Typically, only the person who develops and configures the data will use it in this model.

• Team Decentralized Execution

Owners of businesses in which personnel from different teams utilize and share master data may find this model particularly useful. This is especially helpful for companies with many offices or locations, as it guarantees that information is organized and shared between all employees.

• Centralized Governance

Master data in this model is managed by firm owners or executives and is generated in response to requests from operational units. Team leaders are responsible for centralizing data and disseminating it throughout the firm. This is useful for corporations that need to control the flow of information within the company.

• Decentralized Execution and Centralized Data Governance

Aspects of the aforementioned systems are incorporated into the final model. Each group generates its dataset to add to the overall body of knowledge controlled by an individual or group. This is perfect for larger companies and management teams as it facilitates data collection and sharing.

Data Governance Framework

Putting effort into data governance can yield continual customer insights for business. Businesses may build a solid data governance strategy by following the steps below.

1. Set Team Goals

Defining specific objectives and indicators is essential when designing a data governance framework. It’s a useful tool for locating relevant information and ensuring that everyone is moving in the same direction toward an attainable objective.

2. Setup Data Governance

After objectives have been established, a team of management, data stewardship, liaisons, and other stakeholders involved in data collecting and protection should be put together. The data governance team will make important managerial choices.

3. Data Governance Model

Build a data governance model to specify who can access and share what information. This restricts access to private information to only those who need it and prevents its disclosure without proper authorization.

Best Implementation Practices for Data Governance

The purpose of each organization is to perform at their best. Unfortunately, businesses find it challenging to engage and acquire insight into what’s best for them. Following these best practices for data governance will help organizations perform at its highest level:

Create Transparent Policies and Guidelines

Policies, processes, and guidelines should govern organizational data management. As a result, there will be uniformity and transparency, and workers will be in sync with the data governance goals.

Stakeholder Engagement and Data-Driven Culture

Create an understanding of the significance of the data governance endeavor by including important stakeholders. Encourage data-driven actions by raising awareness, providing training, and rewarding people who take such actions.

Utilize Strong Data Management Methods

Use technology and tools for data management that are appropriate for the data governance framework. Data quality tools, lineage tools, and similar applications fall under this category.

Evaluate Effectiveness

The efficacy of the data governance structure must be evaluated regularly, along with its ability to ensure compliance and its effect on business outcomes. This paves the way for businesses to hone their approaches and evolve.

Having said that, a competent data management service is the greatest approach to properly implementing all the aforementioned procedures.

Why is Data Governance Important?

Data governance is sought after by businesses because of the benefits it brings by connecting many elements, such as roles, procedures, communications, metrics, and technologies. Harvard Business Review predicts that “ data collected across an organization would become more valuable than people ever anticipated.”
Despite widespread recognition of the value of data governance, implementing truly efficient data governance has proven difficult due to cumbersome institutional barriers. According to Gartner, 80% of companies must adopt a cutting-edge approach to data governance, such as a service model, to scale their digital businesses. Now, let’s examine the advantages of data governance.

1. Ethical Data Infrastructure

Due to data ethics, a corporation implementing a flexible data governance program with reliable service has proven its ability to manage its data. The benefits will become clear when a business understands where its data goes, how it will be used, and who will access it. Laws protecting consumers’ privacy and mandating company compliance with audits have been adopted in multiple regions since 2018, beginning with the European General Data Protection Regulation (GDPR). Data governance must adapt to meet the needs of an ever-growing clientele base as the scope of applicable laws grows to encompass 65% of the global population. Therefore, businesses consider data governance an essential initiative for cutting costs and ensuring data compliance. However, only well-established, regularly executed data governance processes produce tangible proof of compliance.

2. Business Decision-Making Assistance

The importance of good data governance may also be seen in how it influences the quality of business decisions. Better decision-making is possible with better data. The Pareto principle states that only 20% of company activities produce 80% of earnings. However, firms need trustworthy business analysis predicated on high-quality data to recognize which activities warrant attention. Solid data governance gives faith that business choices will ultimately be beneficial and align with objectives.

3. Facilitating Better Interdepartmental Communication

Before the implementation of data governance, communication issues inside the company arose because different departments used different terminology and frameworks when discussing data. Donna Burbank, managing director of Global Data Strategy, provided a cartoon depicting a team that has completed the technical construction of an application but is stuck trying to figure out who the target audience is.

Misunderstandings like this persist in increasingly sophisticated technological systems. For instance, teams may design jobs that involve different data transformations when processing large datasets.

Data governance as a service helps departments within an organization come to a consensus on how to define and use data. The organizational foundation can be built on shared experience and cross-function accountability. Literacy in reading, writing, and talking about numbers or data as it pertains to the business world.

Business executives, employees, and automated systems receive more help and are given more autonomy when companies have robust data governance services. As a result, data-driven decision-making is helped by consistent and standardized data policies and practices.

Data Governance Challenges

While establishing data governance has numerous advantages, there are also certain difficulties that businesses may encounter. Among these difficulties are:

Gaining Stakeholder Support

Stakeholder buy-in is crucial for the success of any data governance effort. Unfortunately, some individuals may be averse to change, which can be an issue when implementing data governance. Educating stakeholders on the value of data governance is one approach to addressing this issue. Stakeholders are more inclined to support data governance if they see how it will improve the situation.

Specifying Methods and Processes

Processes and procedures are just as vital as roles and responsibilities. Having clearly defined policies and procedures is essential for successful data governance. How do we go about establishing data governance? This varies widely from company to company. However, some examples of routine procedures are:

• Gathering Information
• Controlling the quality of data
• Information safety
• Secrecy of Information

Creating these routines and methods can be difficult for obvious reasons. However, it is essential to accomplish things properly.

Data Quality

Another area for improvement with data governance is ensuring the data is high quality. Although providing high-quality data is crucial, it can be challenging. There are several potential influences on data quality, including:

• Inaccurate Data: Bad choices can result from erroneous data.
• Conflicting Evidence: Inconsistent data might be challenging to apply.
• Missing Information: It can be challenging to picture events if certain data needs to be included.
• Outdated Information: Making prompt judgments might be challenging if necessary data is outdated.

Data Governance Goals

The purpose of data governance and how it is implemented within a company will be discussed. While every company has its own unique set of aims for data governance, some of the most prevalent ones include:

• Improving data safety by assigning tasks for handling data.
• Follow the rules, and stay out of any data-related legal trouble.
• The policies, roles, and duties of those participating in the data distribution process must be laid down.
• Provide the relevant data assets to maximize employee efficiency.
• Provide information that is both accurate and easy to use to improve planning.
• Providing reliable data assets that can be put to multiple uses reduces the need for doing labor twice.
• Maximize productivity by defining data quality key performance indicators.

How Can Brickclay Help Data Governance?

Data governed, maintained, and secured by a designated group to guarantee that all employees use only reliable information can boost productivity, teamwork, and client confidence in business. Brickclay’s extensive data governance and data engineering services are a significant factor in the company’s success.

With Brickclay’s, businesses may put legally sound processes, rules, procedures, and controls in place to protect data assets to the highest possible standards of data integration maze, classification, availability, and security. To drive corporate growth via well-informed decision-making and improved operational efficiency, firms may commit data governance needs to Brickclay and leverage the power of well-organized and protected data. Brickclay is the go-to ally for companies looking to conquer the tangled web of data governance and realize their full expansion potential.

Contact us to learn how Brickclay supports corporate growth through data governance.