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Introduction
If your business does not know where your data come from, who has access to it and if it is really reliable, how can you make safe strategic decisions?
The lack of data governance does not only affect the internal organization of information, but can also generate serious financial and operational consequences. According to a survey released by CNN Brazil , about 25% of Brazilian companies suffered financial losses due to cyber attacks 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 that data is accurate, accessible, protected and used strategically. With this well -structured model, companies can reduce risks, improve information quality and ensure compliance with standards such as LGPD, and other industry regulations.
But after all, what is data governance and why is it essential for your business? How to structure it efficiently and what challenges can arise along the way? In this guide, we will explore the foundations 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 management quality, this article is for you. Good reading!
What is data governance and why is it essential?
The volume of data generated by companies grows 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 enters as this tool that establishes control, security and transparency in the use of information, ensuring that data is a strategic and reliable asset . Understand more below.
Definition, concept and fundamental elements
Data governance can be defined as a set of practices, policies and technologies that ensure that an organization's data is accurate, protected and used correctly . Its goal is to ensure 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 this year, 2025, will face significant financial and operational risks . This demonstrates that data governance is not just a competitive differential, but a need for business sustainability.
To ensure that data is well managed, an efficient governance structure needs to consider three essential elements/pillars :
- Data Quality : Information should be accurate, updated and consistent, avoiding duplicities or corrupted data;
- Security and Compliance : Strict control over who can access data and under what conditions, ensuring compliance with standards such as LGPD and ISO 27001;
- Data Life Cycle Management : Clear definition of how data is collected, stored, shared and discarded, reducing exposure risks or loss of sensitive information.
A well -structured governance ensures that data is a strategic and reliable asset, avoiding operational problems and regulatory risks .
Essential principles: transparency, responsibility and continuous audit
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 : Define who can access which information and under what conditions guarantees security and compliance;
- Data Responsibility and Property : Each data set needs to have a responsible manager, ensuring that he is always up to date and reliable;
- Continuous Monitoring and Audit : Data governance is not a static process. Regular audits, policy adjustments and access analysis ensure that data remains safe and aligned with business needs.
In this way, data governance is not just about organizing information, but of ensuring that it is reliable, accessible and risk protected . Companies that structure this practice not only avoid regulatory and safety problems, but also turn their data into strategic assets for smarter and more efficient decision making.
But why is this structure so essential to companies? This is what we will see in the next topic.
Why do companies need to count on data governance?
In the current scenario, data is one of the most valuable assets of companies. However, without a solid governance model, this information can become inconsistent, vulnerable and even a risk to the business.
Thus, an effective data governance strategy ensures compliance, quality and security, creating a more reliable environment for operation and decision making . Next, we explore the main reasons why companies should invest in this practice.
Guarantee of compliance with regulations
The growing 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 the Brazilian LGPD (General Data Protection Law), the General Data Protection Regular ) and California Consumer Privacy Act ).
Effective data governance helps companies keep up with these regulations , ensuring that:
- Data is collected and stored in an ethical and secure manner;
- Access to sensitive information is restricted and monitored;
- Companies have well -defined transparency and consent policies for data use.
According to IBM report , the average cost of a data violation by 2023 was US $ 4.45 million per incident . Companies that do not adopt data governance measures are serious risks of fines, legal proceedings and loss of credibility in the market.
Improvement in data quality and accuracy
Inconsistent, duplicate or outdated data can lead to serious errors in financial reports, operational failures and information based on incorrect information. Without governance, companies face challenges such as:
- Lack of clear patterns and rules for data entry and maintenance;
- Difficulty tracking the origin and reliability of information;
- Distructured data that compromises strategic analysis and insights.
With well -implemented governance, companies ensure that data are treated as an asset of value , establishing processes for:
- Elimination of redundancies and inconsistencies, ensuring clean and reliable data;
- Standardization of formats and terminologies, facilitating analysis and integration between different sectors;
- Continuous monitoring of data quality, avoiding outdated or incorrect information.
According to Deloitte , companies that invest in data governance reduce by 40% the costs of rework and inconsistent information correction , as well as increasing the efficiency in analysis and decision making.
Risk mitigation and increased safety
Information security is one of the main challenges of companies today. Cyber attacks, data leakage and improper access can compromise sensitive information and directly impact an organization's credibility.
Thus, without data governance, companies are more exposed to :
- Unauthorized access and sensitive information leaks;
- Important data loss due to lack of structured backups.
- Cyber attacks by exploring breaches in systems and processes.
A governance strategy strengthens data security by implementing practices such as:
- Cryptography and control of rigorous access, preventing improper leaks and access;
- backups and rapid data recovery, ensuring business continuity;
- Active monitoring and constant audit, identifying and correcting vulnerabilities before they become a problem.
According to a study by Cybersecurity Ventures , cyber crime already costs more than $ 10.5 trillion companies a 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 the operation more efficient. Companies that adopt this practice can make faster, strategic and safe decisions, ensuring competitive advantage in the market.
But how to put all this into practice? Check it out below.
How to implement data governance in your company?
Knowing that data governance is important is already a big step, but practical implementation is what really makes a difference . And for this structure to work efficiently, it is necessary to establish clear rules, use the right tools and ensure team involvement.
The following are the three fundamental points for successful implementation : policies and good practices, technology and internal responsibilities. Check it out!
Establishing policies and good practices
Data governance begins with the definition of 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 unusual for decision making.
The main good practices for efficient governance are:
- Data Standardization : Create unified standards and formats to ensure consistency and prevent redundancy;
- Definition of Access Levels : Not all employees need to have access to all data. Establishing permissions and restrictions reduces risks;
- Registration and traceability : Implement audits and access
logs - Incident Response Plan : Establish protocols to deal with leaks, cyber attacks or operational failures.
According to McKinsey , companies that adopt robust data governance policies reduce the risk of non -compliance and leakage of information by up to 30% , ensuring more security and reliability.
Tools and Technologies for Data Governance
Technology plays a key role in data governance, allowing automating processes, ensuring security and monitoring the use of information in real time. There are several tools that can be used to improve governance, from cataloging and data classification platforms to security and compliance .
The main types of them include:
- Data Management Platforms (MDM - Master Data Management ) : Unify and organize data from different sources, ensuring consistency and accuracy. Examples: SAP MASTER DATA GOVERNANCE , INFORMATIC MDM , IBM INFOSPHERE ;
- Compliance Solutions : Monitor hits, apply encryption and ensure compliance with regulations. Examples: Microsoft Purview , Onetrust , Varonis ;
- Data Quality and Cataloging Tools : They identify errors, duplications and inconsistencies, as well as classify data automatically. Examples: Talend Data Fabric , Collibra Data Governance , Alteryx ;
- Automation and Monitoring Platforms : Facilitate the application of defined policies, managing permissions and audits continuously. Examples: Azure Data Governance , AWS Lake Formation .
Defining responsibilities and team involvement
Data governance cannot be the responsibility of a single sector. To work properly, all employees must be aligned with the company's policies and good practices.
Check out the team's essential roles in data governance:
- Data Owner (Date Owner) : Responsible for data management and integrity within a specific department;
- Data Steward : ensures that policies and guidelines are followed by acting on standardization and quality of information;
- Security and Compliance : Take care of the protection of information and compliance with regulations such as LGPD and GDPR;
- FINAL USERS : They should be trained to use and interpret data correctly, avoiding errors and failures in information management.
In addition to the definition of roles, continuous training and data culture are also key elements . Companies investing in the training of their teams can reduce operational failures and improve the adoption of safe practices in everyday life.
In short, the implementation of data governance does not happen overnight , but with well -structured policies, appropriate technologies and a prepared team, your business can turn data into a strategic and safe asset!
Now that we explore the pillars of governance, let's look at hypothetical practical examples in different sectors to see how companies apply this strategy in everyday life and what challenges they face.
Examples of data governance in different sectors
Data governance is not a unique approach - each sector has specific challenges and needs personalized strategies to ensure the integrity, safety and compliance of information. While the financial sector prioritizes protection against fraud and regulatory compliance, the health area faces challenges with interoperability and medical data privacy. In the public sector, transparency and traceability of information are essential to avoid fraud and ensure administration efficiency.
Next, we explore hypothetical situations that illustrate how data governance can solve real problems in different industries.
Financial Sector: Data Protection and Regulatory Compliance
Imagine that a large Brazilian digital bank, with millions of customers, realizes an increase in fraudulent transactions reports. Internally, he finds that there is no unified control over financial data access , and different systems store duplicate and inconsistent information. In addition, there is no clear audit on who accesses critical customer information.
The problems we can identify would be:
- Lack of governance in access, resulting in possible fraud and misuse of data;
- Inconsistent financial information, impairing the accuracy of risk and compliance ;
- Risk of millionaire fines for not being 100% in accordance with regulations such as LGPD.
How can data governance resolve these issues?
- Definition of access rules and audit trails : Each employee has access only to what they really need, with total screening of activities;
- Standardization and Integration of Databases : Avoids duplicate records and inconsistencies in financial transactions;
- Automation in Anomalies Detection : Active monitoring to identify suspicious movements and act before a fraud happens.
Health Sector: Data Safety and Interoperability
A network of hospitals has invested in digitizing electronic medical records, but in practice doctors report difficulty accessing data from patients from different units . In addition, a safety failure exposes sensitive patients from patients, resulting in an information leakage incident.
The problems we can identify would be:
- Lack of interoperability between hospital systems, making it difficult to diagnose and treatments;
- Low traceability of access, where any employee can view critical information without proper restriction;
- Risk of violation of LGPD, resulting in legal penalties and damage to the reputation of the hospital.
How can data governance resolve these issues?
- Implementation of interoperability and standardization of medical data : all systems speak the same “language”, allowing efficient integration between hospitals;
- Definition of Function Based Accesses : Only authorized professionals can view certain data;
- Continuous encryption and audit : total traceability to detect any improper access and ensure regulatory compliance.
Public Sector: Transparency and Information Control
Imagine that a certain city hall of a Brazilian city receives complaints that tax data from taxpayers were improperly changed, resulting in exemptions from irregular taxes. The problem is that there is no reliable tracking of those who access and edit this information , making it difficult to identify those responsible.
The problems we can identify would be:
- Lack of control over access and modifications to public databases;
- High risk of corruption and fraud, impairing municipal collection;
- Difficulty meeting the requirements of the Access to Information Law (LAI) .
How can data governance resolve these issues?
- Implementation of Audit Trails and Unchanging Access Logs : Records each change made in the systems, preventing fraud;
- Definition of Segmented Access Levels : Only authorized servers can make modifications to tax data;
- Publication of automatic transparency reports : facilitates the supervision by control bodies and society.
Regardless of the segment, one thing is certain: well -governed data reduces costs, increases the efficiency and strengthens the confidence of all , clients, patients and citizens.
However, implementing a solid model of governance is not a simple process . In the next topic, we will know the main challenges faced by companies and how to overcome them to ensure that data governance works in practice. Keep following!
Main challenges and how to overcome them
Implementing efficient data governance is not only a matter of technology, but also processes, organizational culture and adaptation to an increasingly complex data environment. Companies that do not structure this journey well face difficulties with disorganized data, disconnected systems and resistance to the adoption of new practices.
Check out the main challenges that arise in data governance and the best strategies to overcome them!
Dealing with destructed data
Companies deal with an increasing volume of unstructured data, such as emails chat messages , scanned images, videos and documents. Unlike traditional databases, this information does not follow a standardized format , making the analysis, organization and governance much more complex.
Without proper control, disruptioned data can generate inconsistencies, make access to information difficult and compromise the security of the organization.
To overcome this challenge it is important to adopt:
- Automatic Data Classification and Indexation Machine Learning tools can help organize destructed data, identifying patterns and converting information into more structured formats;
- Definition of archiving and retention rules : Create a clear policy for storing, accessing and discarding discarded data safely;
- Data Lake and Data Catalog platforms : Allow companies to structure, organize and recover information quickly even if they are in different formats.
- Mapping of business processes that originate data: Understand business requirements and their final results as well as their sources and what formats are available and especially the correlation between them. This is important to, regardless of whether you know the technical question, can understand what information you want to extract, what questions to be asked to obtain the directed analytical result. Remember, if the origins of the data are bad/inconsistent, the result of the analyzes will also be.
Integrating data from different sources
Business Intelligence tools to internal databases and legacy systems. When these environments do not communicate efficiently, they occur duplicities, inconsistencies and difficulties in obtaining reliable insights .
Thus, the lack of integration directly impacts decision making, as scattered and not synchronized data generates operational errors and lack of visibility about the business.
To overcome this challenge it is important to perform:
- Implementation of a hub Data or Middleware : Facilitates communication between different systems, centralizing information and eliminating redundancy;
- Use of APIs for data synchronization : When well structured, they ensure continuous and secure connectivity between platforms;
- ADOPTION OF ETL Tools ( Extract, Transform, Load ) : In Portuguese, “Extraction, Transformation and Load”, involves technologies such as Talend , Apache Nifi and PowerCenter Informatics that transform and integrate data from different sources in an automated manner.
How Skyone simplifies data governance
At Skyone , we know that data governance cannot be an obstacle to your business growth. While many organizations face technical challenges, lack of integration between systems and the weight of regulatory compliance, we assume all this complexity so that your team can focus on what really matters: innovation and results.
Our unique and managed platform simplifies the process , ensuring that data is always organized, accessible and protected without your business having to deal with the technical part. Check out why choose Skyone :
- Governance without complication : We unify management, safety and compliance in a single solution, eliminating the need for multiple disconnected tools;
- GUARANTEE SECURITY AND CONFORMITY : We implement strict data protection protocols, ensuring that your company is always aligned with LGPD and other essential regulations;
- Fluid Integration between Systems : We connect scattered data from different platforms, ensuring reliable and up -to -date information to support strategic decisions;
- Continuous monitoring and specialized support : Our team follows the entire journey of your data, ensuring high availability, traceability and threat protection.
Our biggest mission is to eliminate the complexity of data management , and offer a safe, scalable and efficient solution to our customers.
Conclusion
Data is one of the most valuable assets for any company, but without efficient governance, they 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 competitiveness in the market.
Companies that structure this practice can reduce operational failures, ensure transparency, increase safety and turn dispersed data into insights . However, implementing and maintaining effective governance requires technology, well -defined processes and involvement of the entire team.
It is also important to remember that data governance is not a project with just the beginning, middle and end. It is a continuous process that evolves as new technologies emerge, regulations change and business needs become.
The good news? The more structured, the more natural and fluid it becomes in the daily lives of companies. And this is what separates businesses that only store data from those who really use it strategically.
Now how about keeping up with more insights , trends and best practices on data governance and innovation? blog now and read our other content !
Theron Morato
expert on data and chef in his spare time, Theron Morato brings a unique look at the universe of data, combining technology and gastronomy in irresistible metaphors.
Author of Skyone's “Data Bites” column, it turns complex concepts into tasty insights, helping companies to extract the best from their data.
Connect with Theron on LinkedIn: https://www.linkedin.com/in/theronmorato/
