Imagine a fortress where every brick represents a piece of your organization’s data. Now, picture some bricks riddled with cracks and inconsistencies. Despite its imposing exterior, this fortress is vulnerable to breaches – just like your organization, if it doesn’t have high quality data. Without impeccable data quality, even the most robust security frameworks can crumble, and leave organizations exposed to high-stakes risks.
High-quality data is not just a technical asset; it forms the bedrock of both security strategies and effective compliance. It can mean the difference between a secure and compliant organization and one that’s susceptible to breaches, fines, operational disruptions, and reputational damage.
Without high quality data, maintaining compliance is nearly impossible, and the financial consequences of falling short can be devastating.
The Critical Intersection of Data Quality and Compliance
Compliance in information security isn’t just about adhering to regulations; it’s about safeguarding an organization’s assets, reputation, and stakeholder trust. High-quality data ensures that the security measures an organization has in place are both effective and compliant with the standards set by various regulatory authorities, such as GDPR, HIPAA, and ISO/IEC 27001.
Poor data quality, on the other hand, can lead to compliance failures, exposing organizations to legal penalties and financial losses in addition to reputation damage.
According to IBM Security’s Cost of a Data Breach Report 2022, the average cost of a breach for organizations with high levels of compliance failures was $5.57 million. Even organizations with low levels of compliance failure faced an average cost of $3.31 million per breach.
The high costs of breaches can directly impact financial planning and shareholder value. Beyond this, poor quality data also carries significant operational risks.
When inaccurate, incomplete, or inconsistent data is used for compliance, the consequences ripple through the entire organization. This can lead to:
- Halted operations
- Increased customer churn
- Lengthy recovery times after incidents
Poor data quality can also lead to false assurances about security postures and compliance readiness and leave organizations unknowingly vulnerable.
Compliance might seem like just about ticking boxes for regulators; however, it is also intricately linked to operational efficiency, customer satisfaction, and business continuity. And high-quality data is at the heart of it all. It allows organizations to:
- Operate seamlessly
- Ensure their systems are secure
- Build trust with regulators and customers alike
- Mitigate risks
- Future-proof their operations
- Make more agile and informed decisions
The Role of Data Quality in Compliance
Data quality forms the foundation of compliance in information security. Without high quality data, businesses risk failing audits, face regulatory penalties, and experience operational breakdowns.
High-quality data supports compliance by enabling:
1. Regulatory Adherence
Regulatory frameworks impose stringent requirements on data handling, privacy, and security.
For example, under HIPAA, healthcare organizations must maintain patient data integrity to avoid penalties and ensure compliance with data privacy standards. GDPR mandates that organizations demonstrate accountability in how they process personal data. And the CCPA requires businesses to provide transparency and control over personal data for California residents.
High-quality data ensures that organizations can:
- Accurately report on their compliance efforts,
- Maintain thorough documentation, and
- Provide necessary evidence during audits.
2. Risk Management
Risk management hinges on the ability to foresee and mitigate potential threats. Inaccurate, incomplete, inconsistent, or duplicate data can skew risk assessments, which can render an organization blind to potential vulnerabilities.
These inaccuracies can lead to:
- Legal penalties
- Financial losses, and
- Reputational damage
For example, failing to maintain accurate logs of data access could lead to non-compliance with data protection regulations and cause organizations to suffer from hefty fines and loss of customer trust.
For those in financial services, misguided risk assessments due to inaccurate data can impact investment decisions and credit evaluations.
To ensure effective risk management, information security professionals should leverage data validation platforms and risk management solutions that ensure the accuracy, completeness, and timeliness of data used to assess risk.
Advanced data profiling and cleansing tools, such as DataMatch Enterprise, can help organizations mitigate risks by improving the quality of their risk assessment data.
3. Enhanced Decision-Making
High-quality data provides a comprehensive view of an organization’s operations and thus, enables informed decision-making related to compliance and security strategies. When data is accurate and consistent, decision-makers can:
- Identify trends,
- Uncover anomalies, and
- Allocate resources effectively to mitigate risks.
For instance, financial institutions can use predictive analytics to identify potential compliance gaps and make interventions before they escalate.
Similarly, healthcare providers can analyze patient data to ensure compliance with treatment protocols and regulatory standards.
This proactive approach not only strengthens compliance efforts but also enhances overall security posture.
4. Audit Preparedness
Organizations are routinely required to undergo audits to assess compliance with regulatory standards. And for that they require clear, accurate, and comprehensive data. Failing to maintain audit-ready data not only risks penalties but also impairs the organization’s ability to respond to compliance inquiries swiftly, which may result in legal actions.
The Wells Fargo’s 2016 scandal of fake accounts serves as a stark reminder of what fiasco lax oversight and the absence of robust checks and balances can cause. The bank was not only sued, but also had to pay $3 billion in settlement.
High-quality data ensures:
- All necessary information is readily available, thereby facilitating a smooth audit process.
- Organizations can present their compliance status clearly and transparently, which reduces the likelihood of negative findings.
Implementing data governance frameworks with strict controls can help organizations maintain audit-ready data. Additionally, using advanced audit management software to track data changes, ensure accurate logging, and maintain records that are essential for proving compliance can streamline audit processes.
Consistent data tracking not only allows for quick responses to auditor inquiries but also demonstrates a commitment to compliance.
5. Data Privacy and Security
Protecting sensitive data is non-negotiable for businesses. Poor data quality practices can open the door to breaches and non-compliance that put both companies and their customers at risk.
In 2022, Meta was fined €265 million ($275 million) by Ireland’s data privacy regulator for failing to meet data protection rules, which resulted in user data being exposed on a hacking forum.
Earlier, in 2019, Equifax agreed to pay $575 million up to $700 million as part of its global settlement with the FTC, the Consumer Financial Protection Bureau, and 50 US states and territories to the people affected by its 2017 data breach.
These examples demonstrate how poor data security and management can lead to significant compliance consequences.
To bolster data privacy and safeguard sensitive information, companies should adopt efficient data masking and encryption technologies. Data classification tools can also help categorize and protect private data and ensure compliance with international privacy standards.
6. Reporting Accuracy
Regulatory reporting is only as accurate as the data driving it. False or incomplete data can lead to significant legal repercussions and financial losses.
For instance, consider Wirecard, the German payments processor that collapsed in 2020 after admitting that €1.9 billion of cash reported in its accounts never existed. Inaccurate financial reporting due to fraudulent data caused the downfall of this once-prominent firm and caused Germany’s biggest post-ware fraud scandal. Accurate and reliable data reporting could have prevented such a catastrophic collapse by ensuring transparency and accountability.
Business intelligence tools that provide real-time reporting, backed by verified data, can help improve reporting accuracy. End-to-end data quality solutions, such as DataMatch Enterprise, can further enhance the accuracy of the data feeding these reports, ultimately helping to ensure compliance with regulatory standards.
7. Building and Maintaining Stakeholder Trust
Data breaches are becoming more frequent and severe. In this scenario, maintaining high data quality can help foster trust among stakeholders, including customers, regulators, and business partners. Organizations that prioritize data quality signal to stakeholders that they are committed to safeguarding sensitive information and adhering to regulatory standards. The opposite holds true as well.
For example, the 2017 data breach severely damaged Equifax’s reputation and also eroded public trust in its ability to protect sensitive data and work as a credit bureau.
Stakeholders trust can be invaluable in building strong relationships and strengthening a positive organizational reputation.
Key Components of Data Quality in Compliance
Ensuring data quality involves considering multiple dimensions that are crucial for maintaining compliance and the integrity of security measures within an organization and, ultimately, for safeguarding sensitive data and mitigating risks. These include:
1. Accuracy
Accurate data is fundamental for making informed decisions and ensuring that security measures align with regulatory requirements. Inaccurate data can lead to:
- Misguided security policies,
- Ineffective threat detection, and
- Erroneous compliance reporting.
Additionally, data inaccuracies can compromise automated compliance reporting systems. This may result in misleading compliance statuses that may mask underlying security vulnerabilities.
Example: Misreported user access levels can result in unauthorized data access or insufficient protection measures. For instance, if an employee’s access level is inaccurately recorded as ‘admin’ when it should be ‘read-only,’ it could expose sensitive information to unauthorized personnel and significantly increase the risk of a data breach.
Automated data verification tools like Data Ladder’s DataMatch Enterprise can play a vital role in ensuring accuracy by continuously cross-referencing datasets against verified sources and identifying discrepancies in real time. These tools use algorithms that compare new and existing data and flag any anomalies before they enter critical systems.
2. Completeness
Completeness refers to the extent to which all required data is present. Incomplete data can:
- Hamper the ability to perform comprehensive security assessments and audits
- Obscure a full understanding of an organization’s security posture
- Skew trend analyses, which makes it difficult to identify long-term compliance issues and hinder strategic planning efforts.
- Lead to unresolved vulnerabilities
Example: Missing logs or incomplete incident reports can prevent thorough investigations. If a security breach occurs and relevant logs are absent, investigators may lack critical insights into the attack vector or the data compromised. This cannot only leave vulnerabilities unresolved but also increase the likelihood of future incidents.
Maintaining a complete dataset is crucial for effective incident response and compliance with regulatory authorities that require thorough documentation of data processing activities.
To ensure complete records, several tools now utilize advanced AI and machine learning algorithms to predict and fill in data gaps. These technologies can:
- Detect incomplete datasets,
- Suggest probable values based on historical patterns, and
- Flag fields that need further validation.
By using AI and ML to automate the process, companies can reduce human error and maintain compliance with minimal manual intervention.
3. Consistency
Consistency across data sources ensures reliability and standardization, which:
- Facilitates seamless integration and analysis,
- Enables accurate reporting, and
- Reduces compliance risks.
In large organizations, data often originates from multiple sources and systems, which increases the risk of inconsistencies.
By providing conflicting information across different parts of the organization, inconsistent data can:
- Cause discrepancies in compliance reporting
- Hinder the ability to detect and respond to security incidents effectively
- Impede cross-departmental collaboration
- Hinder data-driven decision-making
Example: Inconsistent naming conventions across various departments can cause confusion during audits. For instance, if one department records customer data under “Client Name” while another uses “Customer Name,” it may result in conflicting reports during compliance audits, which can undermine the organization’s credibility and increase the risk of penalties.
Using tools to standardize data formats and structures across systems and master data management solutions can help organizations ensure data consistency across the organization.
4. Timeliness
Timeliness is crucial for real-time threat detection and response. Data must be available and actionable when needed; otherwise, organizations risk missing critical windows for risk mitigation, which can lead to security breaches and, eventually, compliance violations.
In addition to increasing security risks, delays in data processing can also lead to significant financial losses and damage to an organization’s reputation.
Example: If an organization receives threat intelligence indicating an impending attack but the alert is not processed in time due to slow data processing, the window to implement countermeasures may close, leaving the organization exposed to significant risk.
Outdated data can have serious repercussions in the context of compliance as well.
For instance, delayed reporting of transactions in the financial sector can violate regulations set by bodies like FINRA or SEC, and cause businesses to suffer from heavy fines. This was evident in the case of Yahoo, which was fined $35 million in 2018 for failing to report data breaches in a timely manner.
In the healthcare sector, delays in updating patient records can also lead to non-compliance with treatment protocols and regulatory standards and may even potentially endanger patient safety.
Real-time data processing tools can help address this by ensuring that data is immediately available for compliance-related purposes. In addition, Event Stream Processing (ESP) technologies can alert compliance teams to potential issues as they arise, which allows organizations to act swiftly before violations occur.
These real-time systems enable firms to stay ahead of compliance requirements, ensuring that they are never caught off-guard by data delays that could compromise their operations or security protocols.
5. Validity
Validity pertains to data conforming to defined formats and standards. Valid data is usable for compliance purposes and supports accurate analysis. Invalid data, on the other hand, can disrupt automated compliance processes and lead to inaccurate reporting.
Example: If a regulatory report requires valid email formats for all users but the dataset contains numerous invalid entries, it could lead to incorrect reporting and potential fines during audits, as organizations must demonstrate due diligence in maintaining accurate contact information for all stakeholders.
Similarly, if financial records contain invalid numerical formats, such as improperly formatted transaction amounts, it can result in inaccurate financial statements and jeopardize compliance with financial reporting standards. As a result, the organization may be subjected to regulatory scrutiny and potential fines.
The Role of Data Quality in Security and Investigations
When a data breach occurs, the quality of your data can mean the difference between swift recovery and catastrophic failure. High-quality data is the backbone of effective security and investigation processes, whereas poor quality data complicates responses to security incidents and can also mask vulnerabilities that leave organizations exposed.
Here are some ways high data quality supports effective security measures, compliance, and investigations:
Enhanced Threat Detection
High-quality data enables the deployment of advanced analytics and machine learning models to identify and respond to threats proactively. Accurate and timely data feeds into Security Information and Event Management (SIEM) systems and enhance their ability to detect anomalies and potential breaches.
For example, a financial institution can utilize high-quality transaction data to identify suspicious activities indicative of fraud. Accurate data ensures that legitimate transactions are not flagged erroneously, reducing false positives and improving the efficiency of fraud detection systems.
Failure to detect threats in a timely manner cannot only compromise an organization’s data but can also get them in trouble with regulatory authorities.
In May 2014, Premera Blue Cross suffered a massive data breach that compromised ePHI of 10.4 million individuals. The breach exposed sensitive information like patients’ addresses, bank account information, and Social Security numbers.
What made it even worse was that it remained undetected for about 9 months. This failure to protect data and detect the breach eventually led to Premera Blue Cross receiving the second largest ever HIPAA penalty of $6.85 million.
Better Incident Response
When a security incident occurs, the speed and effectiveness of the response can significantly mitigate damage. High-quality data, which involves having access to accurate logs, incident reports, and threat intelligence that provides a comprehensive view of the situation, enables organizations to act swiftly and decisively.
Inadequate or delayed data, on the other hand, can lead to missed or delayed incident responses. If an organization lacks accurate or up-to-date data, identifying the scope and impact of a breach within the timeframe becomes nearly impossible.
In the instance of a ransomware attack, for example, organizations with well-maintained incident response protocols and accurate data can quickly assess the extent of the breach, identify affected systems, and initiate recovery measures. Conversely, poor data quality can lead to delays and confusion, which will only exacerbate the incident’s impact.
Accurate Audit Trails
Accurate trials are essential for both internal security investigations and regulatory audits. For example, data lineage – the tracking of data’s origin and flow across systems – plays a critical role in demonstrating compliance with regulations like PCI DSS (Payment Card Industry Data Security Standard) and SOX. Poor data quality can obscure the audit trail, making it difficult for organizations to verify the integrity of their data, as well as the effectiveness of their security measures.
Streamlined Investigations
Successful investigations rely on having complete and valid data. Investigators require access to all relevant information to piece together a coherent narrative of an incident. Incomplete or inaccurate data can obscure critical details and lead to misinterpretations.
For example, in a case of data theft, having a complete audit trail of user access and activity logs is crucial for understanding how the breach occurred. It can help quickly trace the source and extent of the breach, implement corrective measures, and provide detailed reports to regulatory bodies. If logs are missing or inconsistent, investigators may struggle to establish timelines and accountability, which will ultimately complicate the resolution of the incident.
Effective Regulatory Compliance & Reporting
Regulatory frameworks often mandate thorough documentation and reporting of data breaches and security incidents. High-quality data ensures that these reports are accurate, complete, and adhere to the specific requirements of various regulations, thereby minimizing the risk of non-compliance.
For instance, a healthcare provider subject to HIPAA must maintain accurate patient records. High-quality data ensures that all patient information is correctly stored, accessed, and reported and thus, helps healthcare organizations avoid potential fines and legal issues.
A Culture of Data Responsibility
Prioritizing data quality builds and nurtures a culture of accountability within organizations. When employees understand the importance of maintaining high data standards, they are more likely to adhere to best practices in data handling and security protocols. This culture can enhance the organization’s overall security posture and reduce risks associated with poor data management.
Companies that invest in data quality training for their employees often see improved data stewardship, a greater emphasis on compliance and fewer incidents resulting from human error or negligence.
Best Practices to Ensure Data Quality and Compliance – A Quick Guide for Information Security Professionals
In addition to meeting regulatory requirements, maintaining high data quality is crucial for building a resilient security infrastructure that can withstand emerging threats. For information security professionals, ensuring data quality is a continuous process that must be integrated into both day-to-day operations and long-term strategic initiatives.
Here are the best practices to align your data quality efforts with the dual goals of security and compliance:
1. Establish Robust Data Governance
A strong data governance framework is essential for maintaining consistent data quality across the organization. Clearly defined roles, accountability, and comprehensive policies ensure that data management processes uphold the highest standards.
Actions:
- Define data ownership and accountability – Assign specific individuals or teams the responsibility for maintaining data quality.
- Set clear metrics – Establish measurable data quality metrics and benchmarks to track progress.
- Enforce relevant policies – Develop and implement policies that govern data management and usage across the organization.
2. Utilize Advanced Data Quality Tools
Advanced tools for data cleansing, validation, and enrichment are key to enhancing data quality and streamlining data management processes. They help ensure the accuracy, completeness, and consistency of your data. Automating these processes also minimizes the risk of human error.
Actions:
- Deploy quality tools – Invest in data cleansing, validation, and enrichment tools tailored to your organization’s needs.
- Automate processes – Implement automation for data quality processes to reduce manual errors and improve efficiency.
3. Cultivate a Data-Driven Culture
Building a culture that prioritizes data quality across all levels of the organization ensures everyone plays a role in maintaining high standards. By ingraining data quality in the organizational culture, you create a proactive approach where compliance and security become shared responsibilities.
Actions:
- Provide training – Offer ongoing training and resources on the importance of data quality and its impact on security and compliance.
- Promote data stewardship – Encourage employees to take ownership of data quality and promote accountability among them.
- Integrate objectives – Embed data quality objectives into daily business processes to reinforce their importance.
4. Conduct Regular Audits and Assessments
Regular data audits and assessments are crucial for identifying and rectifying quality issues before they escalate into compliance risks. Continuous monitoring helps ensure that your data meets both internal standards and evolving external regulatory requirements.
Actions:
- Conduct periodic audits – Schedule regular data quality audits to assess compliance and identify areas for improvement.
- Implement monitoring systems – Establish continuous data quality monitoring systems to catch issues early and maintain high standards.
5. Integrate Data Quality with Security Strategies
Aligning data quality initiatives with security strategies ensures that both areas work together to protect the organization. For instance, incorporating data quality metrics into security dashboards can provide real-time insights into compliance status and potential risks, allowing for quicker, more informed responses to emerging risks and ensuring that data remains reliable in security operations.
Actions:
- Align initiatives – Integrate data quality initiatives with existing security policies and strategies for holistic protection.
- Incorporate metrics – Utilize data quality metrics within security monitoring systems to track compliance and data integrity effectively.
Challenges in Maintaining Data Quality for Compliance and Security
So far, we have established that high data quality is vital for effective compliance and security. However, achieving this is often easier said than done. Infosec professionals face numerous challenges that can undermine the quality of data, jeopardize compliance with regulatory standards, and increase the risk of security incidents.
Below are some of the most significant challenges they face:
1. Data Silos and Fragmentation
One of the biggest challenges organizations face is data existing in silos across different systems, departments, or geographical locations. This fragmentation creates inconsistent and incomplete data, making it difficult to maintain a unified view that supports security measures and compliance reporting.
For example, a multinational corporation might have customer data stored in different regional databases with varying data formats. If this fragmented data is not properly integrated and standardized, the organization could face non-compliance with regulatory authorities.
2. High Data Volume and Variety
The volume of data generated today is enormous, and its variety—structured, semi-structured, and unstructured data—adds complexity to data quality management. This can overwhelm traditional data quality management systems and make it challenging to maintain accurate, complete, and valid data for security measures and regulatory audits.
For example, a financial institution might collect data from customer transactions, web interactions, and social media, all in different formats. Ensuring that this diverse data meets quality standards across all sources is difficult and resource-intensive, but essential for accurate fraud detection and compliance reporting.
3. Evolving Regulatory Landscape
Regulations like GDPR, HIPAA, and the California Consumer Privacy Act are constantly evolving. This means organizations must continuously update their data management practices to comply with new standards. Failing to do so can result in non-compliance, fines, and reputational damage.
For instance, if a healthcare organization fails (or neglects) to update its data retention and privacy policies in line with the latest HIPAA regulations, it could risk facing severe fines. Maintaining high data quality in this context means ensuring all patient data is accurate, securely stored, and accessible in compliance with regulatory timelines.
4. Human Error
Human error remains a significant challenge in maintaining data quality, especially in organizations where data entry, management, and analysis are still heavily reliant on manual processes. Errors in data entry can introduce inaccuracies, inconsistencies, or missing information, which can have far-reaching implications for both compliance and security.
Consider a data migration project, for example. A small error in entering employee access permissions might allow unauthorized access to sensitive files, which is not only a violation of internal security protocols but also of external regulations like SOC 2.
5. Legacy Systems and Compatibility Issues
Many organizations still rely on legacy systems that are not built to handle modern data management requirements, including maintaining data quality. These outdated systems often lack the capabilities to automate data validation, cleansing, and monitoring, making it harder to meet today’s rigorous compliance and security standards.
For example, financial services company that’s till using legacy software for account management might struggle to maintain consistent data quality across newer systems. This increases the risk of both errors and regulatory non-compliance.
6. Limited Resources and Expertise
Organizations often face resource constraints to manage and monitor data quality. This is particularly challenging for small- to medium-sized enterprises that do not have the resources to invest in advanced tools or hire specialized staff for data quality management.
For instance, a small business that lacks dedicated data quality teams may find it difficult to perform regular data audits or implement advanced data quality solutions, which them vulnerable to compliance risks and potential security breaches.
7. Real-Time Data Processing Demands
In industries like financial services or e-commerce, the demand for real-time data processing can make it difficult to maintain data quality. Ensuring that data is accurate, complete, and timely while processing it in real-time requires advanced tools and infrastructure, which many organizations struggle to implement.
For example, an online retailer that processes thousands of transactions per minute may find it difficult to ensure that every transaction is accurately recorded in real-time. Errors in this process could lead to incomplete financial records, increasing the risk of security vulnerabilities and non-compliance with financial reporting standards.
The Future of Data Quality in Compliance
As data continues to grow in volume and complexity, the importance of data quality in compliance will only increase. Consequently, the technologies aimed at enhancing and maintaining data quality will also continue to evolve.
These advancements will help information security professionals better position themselves to navigate the ever-evolving regulatory landscape and protect their assets effectively.
AI for Enhanced Data Quality
Artificial intelligence can be significantly useful in automating data quality processes and enabling real-time data validation and anomaly detection. AI-driven tools can readily adapt to changing data patterns, which ensures that data quality remains high even as data sources and types evolve.
Organizations leveraging AI for data quality management can achieve higher accuracy and efficiency and position themselves to better handle the complexities of modern compliance requirements.
For instance, financial institutions using AI can swiftly identify discrepancies in transaction data and, thus, allows for timely corrective actions and minimizing compliance risks.
Key Benefits of AI in Data Quality
- Automated Data Validation: Reduces manual effort and error rates.
- Real-Time Anomaly Detection: Quickly identifies and addresses issues before they escalate.
- Adaptive Learning: Adjusts to new data patterns, ensuring ongoing quality.
Blockchain for Data Integrity
Blockchain technology helps enhance data integrity and trust by offering immutable and transparent data records. Integrating blockchain into data management practices can provide an additional layer of security and transparency.
By using this advanced technology, organizations can ensure that their data remains tamper-proof and verifiable, which are crucial aspects of compliance.
For example, in supply chain management, blockchain can track the provenance of goods. This will enable organizations to demonstrate compliance with regulatory standards through verifiable records of each transaction.
Key Benefits of Blockchain in Data Quality
- Tamper-Proof Records: Ensures data integrity over time.
- Transparency: Provides a clear audit trail, facilitating compliance audits.
- Decentralization: Reduces the risk of data manipulation by distributing records across a network.
Improve Data Quality, Ensure Compliance, and Guarantee Efficient Investigations with Data Ladder
In the information security sector, data quality is more than just a technical concern. It forms the foundation upon which compliance is built, data protection measures are erected, and successful investigations rely.
Simply put, prioritizing data quality is essential for navigating the complexities of today’s regulatory environment and safeguarding organizational assets.
Investing in data quality is investing in building the resilience and integrity of your organization. As the digital landscape continues to evolve, those who recognize and act on the importance of data quality will lead the way in maintaining compliance, securing their assets, and building trust with stakeholders.
However, maintaining data quality consistently is often easier said than done. This is where Data Ladder steps in.
Data Ladder offers a suite of powerful data quality management solutions to help information security professionals ensure their data remains accurate, complete, and compliant. With our advanced tools for data cleansing, validation, and monitoring, organization can proactively address data quality challenges, streamline their operations, and enhance their security posture.
The cost of neglecting data quality is far greater than the investment required to maintain it. So, act now, and download a free trial of Data Ladder’s data management software or schedule a demo with one of our experts.
