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Data governance: what it is and why it's important for your company
If your company doesn't know where its data comes from, who has access to it, and whether it's truly trustworthy, how can it make sound strategic decisions?
The lack of data governance not only affects the internal organization of information but can also generate serious financial and operational consequences. According to research published by CNN Brazil , approximately 25% of Brazilian companies suffered financial losses due to cyberattacks in 2022. Many of these vulnerabilities could have been avoided with well-defined data governance policies, ensuring access control, compliance, and protection against external threats.
Data governance emerges as the solution to transform this scenario . More than just organizing information, it is a set of processes, policies, and practices that ensure data is accurate, accessible, protected, and used strategically. With this well-structured model, companies can reduce risks, improve the quality of information, and ensure compliance with standards such as the LGPD (Brazilian General Data Protection Law) and other sector regulations.
But, ultimately, what is data governance and why is it essential for your company? How to structure it efficiently, and what challenges may arise along the way? In this guide, we will explore the fundamentals of data governance, its benefits, how to implement it, and which tools can facilitate this process.
If your company seeks greater control, security, and quality in information management, this article is for you. Happy reading!
What is data governance and why is it essential?
The volume of data generated by companies is growing exponentially, but this information is not always well-organized, protected, or efficiently accessible. Without a structured process to manage this data, companies face quality problems, regulatory risks, and operational difficulties.
Data governance acts as a tool that establishes control, security, and transparency in the use of information, ensuring that data is a strategic and reliable asset . Learn more below.
Definition, concept and fundamental elements
Data governance can be defined as a set of practices, policies, and technologies that ensure an organization's data is accurate, protected, and used correctly . Its goal is to guarantee that information is always available for strategic decision-making and regulatory compliance.
According to Gartner , 80% of companies that do not adopt a data governance strategy by 2025 will face significant financial and operational risks . This demonstrates that data governance is not just a competitive advantage, but a necessity for business sustainability.
To ensure that data is well managed, an efficient governance framework needs to consider three essential elements/pillars :
- Data quality : the information must be accurate, up-to-date and consistent, avoiding duplicates or corrupted data;
- Security and compliance : strict control over who can access the data and under what conditions, ensuring compliance with standards such as the LGPD (Brazilian General Data Protection Law) and ISO 27001;
- Data lifecycle management : a clear definition of how data is collected, stored, shared, and disposed of, reducing the risk of exposure or loss of sensitive information.
Well-structured governance ensures that data is a strategic and reliable asset, preventing operational problems and regulatory risks .
Essential principles: transparency, accountability, and continuous auditing
For data governance to be effective, it is important to follow some fundamental principles that ensure control, traceability, and reliability of information.
- Transparency in data access : defining who can access what information and under what conditions ensures security and compliance;
- Data responsibility and ownership : each dataset needs to have a responsible manager, ensuring that it is always up-to-date and reliable;
- Continuous monitoring and auditing : data governance is not a static process. Regular audits, policy adjustments, and access analysis ensure that data remains secure and aligned with business needs.
Therefore, data governance is not just about organizing information, but about ensuring that it is reliable, accessible, and protected against risks . Companies that structure this practice not only avoid regulatory and security problems, but also transform their data into strategic assets for smarter and more efficient decision-making.
But why is this structure so essential for companies? That's what we'll see in the next topic.
Why do companies need data governance?
In today's landscape, data is one of a company's most valuable assets. However, without a solid governance model, this information can become inconsistent, vulnerable, and even a risk to the business.
Therefore, an effective data governance strategy ensures compliance, quality, and security, creating a more reliable environment for operation and decision-making . Below, we explore the main reasons why companies should invest in this practice.
Guarantee of compliance with regulations
The increasing amount of data collected by companies brings significant challenges regarding privacy and information security . To avoid penalties and protect users' rights, several countries have established strict laws for data management, such as Brazil's LGPD (General Data Protection Law), Europe's GDPR ( General Data Protection Regulation ), and the United States' CCPA ( California Consumer Privacy Act ).
Effective data governance helps companies stay compliant with these regulations , ensuring that:
- The data should be collected and stored in an ethical and secure manner;
- Access to sensitive information should be restricted and monitored;
- Companies should have well-defined transparency and consent policies for the use of data.
According to an IBM report , the average cost of a data breach in 2023 was $4.45 million per incident . Companies that fail to adopt data governance measures run serious risks of fines, lawsuits, and loss of market credibility.
Improved data quality and accuracy
Inconsistent, duplicated, or outdated data can lead to serious errors in financial reporting, operational failures, and strategies based on incorrect information. Without governance, companies face challenges such as:
- Lack of clear standards and rules for data entry and maintenance;
- Difficulty in tracing the origin and reliability of the information;
- Unstructured data that compromises strategic analysis and insights.
With well-implemented governance, companies ensure that data is treated as a valuable asset by establishing processes to:
- Eliminating redundancies and inconsistencies, ensuring clean and reliable data;
- Standardization of formats and terminology, facilitating analysis and integration between different sectors;
- Continuous monitoring of data quality, preventing outdated or incorrect information.
According to Deloitte , companies that invest in data governance reduce costs associated with rework and correcting inconsistent information by 40% , in addition to increasing efficiency in analysis and decision-making.
Risk mitigation and increased security
Information security is one of the main challenges facing companies today. Cyberattacks, data leaks, and unauthorized access can compromise sensitive information and directly impact an organization's credibility.
Thus, without data governance, companies are more exposed to :
- Unauthorized access and leaks of sensitive information;
- Loss of important data due to lack of structured backups.
- Cyberattacks exploiting vulnerabilities in systems and processes.
A governance strategy strengthens data security by implementing practices such as:
- Strict encryption and access control, preventing leaks and unauthorized access;
- backups and fast data recovery, ensuring business continuity;
- Active monitoring and constant auditing, identifying and correcting vulnerabilities before they become a problem.
According to a study by Cybersecurity Ventures , cybercrime already costs companies globally more than US$10.5 trillion per year , making investment in data protection and governance strategies essential.
As we have seen, data governance not only protects the company from financial and regulatory risks, but also improves the quality and reliability of information , making operations more efficient. Companies that adopt this practice are able to make faster, more strategic and secure decisions, guaranteeing a competitive advantage in the market.
But how do you put all this into practice? Check it out below.
Knowing that data governance is important is already a big step, but practical implementation is what really makes the difference . And for this structure to work efficiently, it's necessary to establish clear rules, use the right tools, and ensure team involvement.
Below, we address the three fundamental points for a successful implementation : policies and best practices, technology, and internal responsibilities. Check it out!
Establishing policies and best practices
Data governance begins with defining clear rules and guidelines for the use, access, and protection of information within the company. Without well-structured policies, data can become disorganized, insecure, and unreliable for decision-making.
The main best practices for efficient governance include:
- Data standardization : creating unified standards and formats to ensure consistency and avoid redundancies;
- Defining access levels : not all employees need access to all data. Establishing permissions and restrictions reduces risks.
- Registration and traceability : implement audits and access
logs - Incident response plan : establish protocols for dealing with data breaches, cyberattacks, or operational failures.
According to McKinsey , companies that adopt robust data governance policies reduce the risks of non-compliance and data leaks by up to 30% , ensuring greater security and reliability.
Tools and technologies for data governance
Technology plays a fundamental role in data governance, enabling the automation of processes, ensuring security, and monitoring the use compliance solutions .
The main types of these include:
- Master ) platforms : unify and organize data from different sources, ensuring consistency and accuracy. Examples: SAP Master Data Governance , Informatica MDM , IBM InfoSphere ;
- Security and compliance : monitor access, apply encryption, and ensure compliance with regulations. Examples: Microsoft Purview , OneTrust , Varonis ;
- Data quality and cataloging tools : identify errors, duplicates, and inconsistencies, as well as automatically classify data. Examples: Talend Data Fabric , Collibra Data Governance , Alteryx ;
- Automation and monitoring platforms : facilitate the application of defined policies by continuously managing permissions and audits. Examples: Azure Data Governance , AWS Lake Formation .
Defining responsibilities and team involvement
Data governance cannot be the responsibility of a single department. For it to function correctly, all employees must be aligned with the company's policies and best practices.
Check out the essential roles of the team in data governance:
- Data Owner : responsible for the management and integrity of data within a specific department;
- Data Steward : ensures that policies and guidelines are followed, working to standardize and improve the quality of information;
- Security and compliance : responsible for protecting information and ensuring compliance with regulations such as the LGPD and GDPR;
- End users : must be trained to use and interpret the data correctly, avoiding errors and failures in information management.
Beyond defining roles, continuous training and a data culture are also key elements . Companies that invest in training their teams are able to reduce operational errors and improve the adoption of secure practices in their daily work.
In short, implementing data governance doesn't happen overnight , but with well-structured policies, appropriate technologies, and a prepared team, your company can transform data into a strategic and secure asset!
Now that we've explored the pillars of governance, let's look at hypothetical practical examples in different sectors to see how companies apply this strategy in their day-to-day operations and what challenges they face.
Data governance is not a one-size-fits-all approach— each sector has specific challenges and needs customized strategies to ensure the integrity, security, and compliance of information. While the financial sector prioritizes fraud protection and regulatory compliance, the healthcare sector faces challenges with interoperability and privacy of medical data. In the public sector, transparency and traceability of information are essential to prevent fraud and ensure efficient administration.
Below, we explore hypothetical situations that illustrate how data governance can solve real-world problems in different industries.
Financial Sector: Data Protection and Regulatory Compliance
Imagine that a large Brazilian digital bank, with millions of customers, notices an increase in reports of fraudulent transactions. Internally, it discovers that there is no unified control over access to financial data , and different systems store duplicate and inconsistent information. Furthermore, there is no clear audit of who accesses critical customer information.
The problems we can identify would be:
- Lack of governance in access control, resulting in potential fraud and misuse of data;
- Inconsistent financial information, compromising the accuracy of risk and compliance ;
- Risk of multimillion-dollar fines for not being 100% compliant with regulations such as the LGPD (Brazilian General Data Protection Law).
How can data governance address these issues?
- Defining access rules and audit trails : each employee only has access to what they truly need, with complete activity tracking;
- Standardization and integration of databases : prevents duplicate records and inconsistencies in financial transactions;
- Automation in anomaly detection : active monitoring to identify suspicious activity and act before fraud occurs.
Healthcare sector: data security and interoperability
A hospital network invested in digitizing electronic medical records, but in practice, doctors report difficulties accessing patient data from different units . Furthermore, a security flaw exposed sensitive patient data, resulting in a data breach incident.
The problems we can identify would be:
- Lack of interoperability between hospital systems, hindering diagnoses and treatments;
- Poor traceability of access, where any employee can view critical information without adequate restriction;
- Risk of violating the LGPD (Brazilian General Data Protection Law), resulting in legal penalties and damage to the hospital's reputation.
How can data governance address these issues?
- Implementation of interoperability and standardization of medical data : all systems speak the same "language," allowing for efficient integration between hospitals;
- Definition of role-based access : only authorized professionals can view certain data;
- Encryption and continuous auditing : full traceability to detect any unauthorized access and ensure regulatory compliance.
Public Sector: Transparency and Control of Information
Imagine that a certain city hall in a Brazilian city receives complaints that taxpayers' tax data has been improperly altered, resulting in irregular tax exemptions. The problem is that there is no reliable tracking of who accesses and edits this information , making it difficult to identify those responsible.
The problems we can identify would be:
- Lack of control over access to and modifications of public databases;
- High risk of corruption and fraud, harming municipal revenue;
- Difficulty in meeting the requirements of the Access to Information Law (LAI) .
How can data governance address these issues?
- Implementation of immutable audit trails and access logs : records every change made to the systems, preventing fraud;
- Definition of segmented access levels : only authorized servers can make modifications to tax data;
- Publication of automatic transparency reports : facilitates oversight by regulatory bodies and society.
Regardless of the sector, one thing is certain: well-governed data reduces costs, increases efficiency, and strengthens the trust of everyone – clients, patients, and citizens.
However, implementing a solid governance model is not a simple process . In the next section, we will explore the main challenges faced by companies and how to overcome them to ensure that data governance works in practice. Keep reading!
Implementing efficient data governance is not just a matter of technology, but also of processes, organizational culture, and adapting to an increasingly complex data environment. Companies that don't structure this journey well face difficulties with disorganized data, disconnected systems, and resistance to adopting new practices.
Check out the main challenges that arise in data governance and the best strategies to overcome them!
Dealing with unstructured data
Companies are dealing with an increasing volume of unstructured data, such as emails chat messages , images, videos, and scanned documents. Unlike traditional databases, this information does not follow a standardized format , making analysis, organization, and governance much more complex.
Without proper control, unstructured data can lead to inconsistencies, hinder access to information, and compromise organizational security.
To overcome this challenge, it is important to adopt:
- Automatic data classification and indexing Machine learning tools can help organize unstructured data by identifying patterns and converting information into more structured formats;
- Defining archiving and retention rules : creating a clear policy for securely storing, accessing, and disposing of unstructured data;
- The use of data lake and data catalog allows companies to structure, organize, and retrieve information quickly, even if it is in different formats.
- Mapping the business processes that generate the data: Understanding the business requirements and their end results, as well as their sources and available formats, and especially the correlation between them. This is important so that, regardless of technical knowledge, one can understand what information to extract and what questions to ask to obtain the targeted analytical result. Remember, if the data sources are poor/inconsistent, the results of the analyses will also be poor/inconsistent.
Integrating data from different sources
Business Intelligence tools to internal databases and legacy systems. When these environments fail to communicate efficiently, duplication, inconsistency, and difficulties in obtaining reliable insights .
Therefore, a lack of integration directly impacts decision-making, as scattered and unsynchronized data leads to operational errors and a lack of visibility into the business.
To overcome this challenge, it is important to:
- Implementing a data hub or middleware : facilitates communication between different systems by centralizing information and eliminating redundancies;
- Using APIs for data synchronization : when well-structured, they ensure continuous and secure connectivity between platforms;
- Adoption of ETL ( Extract, Transform, Load ) : This involves technologies such as Talend , Apache NiFi , and Informatica PowerCenter that transform and integrate data from different sources in an automated way.
How Skyone simplifies data governance
At Skyone , we know that data governance your company's growth we take on all that complexity so your team can focus on what really matters: innovation and results.
Our unique, managed platform simplifies the process , ensuring that data is always organized, accessible, and protected, without your company having to deal with the technical aspects. See why you should choose Skyone :
- Governance without complications : we unify management, security, and compliance into a single solution, eliminating the need for multiple disconnected tools;
- Security and compliance guaranteed : we implement rigorous data protection protocols, ensuring that your company is always aligned with the LGPD (Brazilian General Data Protection Law) and other essential regulations;
- Seamless system integration : we connect dispersed data from different platforms, ensuring reliable and up-to-date information to support strategic decisions;
- Continuous monitoring and expert support : our team accompanies your data throughout its entire journey, ensuring high availability, traceability, and protection against threats.
Our main mission is to eliminate the complexity of data management and offer a secure, scalable, and efficient solution for our clients.
Data is one of the most valuable assets for any company, but without efficient governance, it can become a source of risk, inefficiency, and inaccurate decisions. As we have seen throughout this article, data governance goes far beyond compliance and security : it is the foundation for organized processes, reliable information, and greater market competitiveness.
Companies that structure this practice are able to reduce operational errors, ensure transparency, increase security, and transform scattered data into insights . However, implementing and maintaining effective governance requires technology, well-defined processes, and the involvement of the entire team.
It's also important to remember that data governance is not a project with just a beginning, middle, and end. It 's an ongoing process that evolves as new technologies emerge, regulations change, and business needs transform.
The good news? The more structured it is, the more natural and fluid it becomes in the day-to-day operations of companies. And that's what separates businesses that merely store data from those that truly use it strategically.
Now, how about continuing to follow more insights , trends, and best practices on data governance and innovation? Explore our blog and read our other content !
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